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SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners | http://arxiv.org/abs/2408.16768v1 | http://arxiv.org/abs/2408.16768v1 | http://arxiv.org/pdf/2408.16768v1 | 2024-08-29 | 2024-08-29 | [
"Ziyu Guo",
"Renrui Zhang",
"Xiangyang Zhu",
"Chengzhuo Tong",
"Peng Gao",
"Chunyuan Li",
"Pheng-Ann Heng"
] | [
"",
"",
"",
"",
"",
"",
""
] | We introduce SAM2Point, a preliminary exploration adapting Segment Anything
Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. SAM2Point
interprets any 3D data as a series of multi-directional videos, and leverages
SAM 2 for 3D-space segmentation, without further training or 2D-3D projection.
Our framework supports various prompt types, including 3D points, boxes, and
masks, and can generalize across diverse scenarios, such as 3D objects, indoor
scenes, outdoor environments, and raw sparse LiDAR. Demonstrations on multiple
3D datasets, e.g., Objaverse, S3DIS, ScanNet, Semantic3D, and KITTI, highlight
the robust generalization capabilities of SAM2Point. To our best knowledge, we
present the most faithful implementation of SAM in 3D, which may serve as a
starting point for future research in promptable 3D segmentation. Online Demo:
https://huggingface.co/spaces/ZiyuG/SAM2Point . Code:
https://github.com/ZiyuGuo99/SAM2Point . | Work in progress. Online Demo:
https://huggingface.co/spaces/ZiyuG/SAM2Point . Code:
https://github.com/ZiyuGuo99/SAM2Point | cs.CV | [
"cs.CV",
"cs.AI",
"cs.CL"
] |
||
ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion
Model | http://arxiv.org/abs/2408.16767v1 | http://arxiv.org/abs/2408.16767v1 | http://arxiv.org/pdf/2408.16767v1 | 2024-08-29 | 2024-08-29 | [
"Fangfu Liu",
"Wenqiang Sun",
"Hanyang Wang",
"Yikai Wang",
"Haowen Sun",
"Junliang Ye",
"Jun Zhang",
"Yueqi Duan"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Advancements in 3D scene reconstruction have transformed 2D images from the
real world into 3D models, producing realistic 3D results from hundreds of
input photos. Despite great success in dense-view reconstruction scenarios,
rendering a detailed scene from insufficient captured views is still an
ill-posed optimization problem, often resulting in artifacts and distortions in
unseen areas. In this paper, we propose ReconX, a novel 3D scene reconstruction
paradigm that reframes the ambiguous reconstruction challenge as a temporal
generation task. The key insight is to unleash the strong generative prior of
large pre-trained video diffusion models for sparse-view reconstruction.
However, 3D view consistency struggles to be accurately preserved in directly
generated video frames from pre-trained models. To address this, given limited
input views, the proposed ReconX first constructs a global point cloud and
encodes it into a contextual space as the 3D structure condition. Guided by the
condition, the video diffusion model then synthesizes video frames that are
both detail-preserved and exhibit a high degree of 3D consistency, ensuring the
coherence of the scene from various perspectives. Finally, we recover the 3D
scene from the generated video through a confidence-aware 3D Gaussian Splatting
optimization scheme. Extensive experiments on various real-world datasets show
the superiority of our ReconX over state-of-the-art methods in terms of quality
and generalizability. | Project page: https://liuff19.github.io/ReconX | cs.CV | [
"cs.CV",
"cs.AI",
"cs.GR"
] |
||
SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners | http://arxiv.org/abs/2408.16768v1 | http://arxiv.org/abs/2408.16768v1 | http://arxiv.org/pdf/2408.16768v1 | 2024-08-29 | 2024-08-29 | [
"Ziyu Guo",
"Renrui Zhang",
"Xiangyang Zhu",
"Chengzhuo Tong",
"Peng Gao",
"Chunyuan Li",
"Pheng-Ann Heng"
] | [
"",
"",
"",
"",
"",
"",
""
] | We introduce SAM2Point, a preliminary exploration adapting Segment Anything
Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. SAM2Point
interprets any 3D data as a series of multi-directional videos, and leverages
SAM 2 for 3D-space segmentation, without further training or 2D-3D projection.
Our framework supports various prompt types, including 3D points, boxes, and
masks, and can generalize across diverse scenarios, such as 3D objects, indoor
scenes, outdoor environments, and raw sparse LiDAR. Demonstrations on multiple
3D datasets, e.g., Objaverse, S3DIS, ScanNet, Semantic3D, and KITTI, highlight
the robust generalization capabilities of SAM2Point. To our best knowledge, we
present the most faithful implementation of SAM in 3D, which may serve as a
starting point for future research in promptable 3D segmentation. Online Demo:
https://huggingface.co/spaces/ZiyuG/SAM2Point . Code:
https://github.com/ZiyuGuo99/SAM2Point . | Work in progress. Online Demo:
https://huggingface.co/spaces/ZiyuG/SAM2Point . Code:
https://github.com/ZiyuGuo99/SAM2Point | cs.CV | [
"cs.CV",
"cs.AI",
"cs.CL"
] |
||
ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion
Model | http://arxiv.org/abs/2408.16767v1 | http://arxiv.org/abs/2408.16767v1 | http://arxiv.org/pdf/2408.16767v1 | 2024-08-29 | 2024-08-29 | [
"Fangfu Liu",
"Wenqiang Sun",
"Hanyang Wang",
"Yikai Wang",
"Haowen Sun",
"Junliang Ye",
"Jun Zhang",
"Yueqi Duan"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Advancements in 3D scene reconstruction have transformed 2D images from the
real world into 3D models, producing realistic 3D results from hundreds of
input photos. Despite great success in dense-view reconstruction scenarios,
rendering a detailed scene from insufficient captured views is still an
ill-posed optimization problem, often resulting in artifacts and distortions in
unseen areas. In this paper, we propose ReconX, a novel 3D scene reconstruction
paradigm that reframes the ambiguous reconstruction challenge as a temporal
generation task. The key insight is to unleash the strong generative prior of
large pre-trained video diffusion models for sparse-view reconstruction.
However, 3D view consistency struggles to be accurately preserved in directly
generated video frames from pre-trained models. To address this, given limited
input views, the proposed ReconX first constructs a global point cloud and
encodes it into a contextual space as the 3D structure condition. Guided by the
condition, the video diffusion model then synthesizes video frames that are
both detail-preserved and exhibit a high degree of 3D consistency, ensuring the
coherence of the scene from various perspectives. Finally, we recover the 3D
scene from the generated video through a confidence-aware 3D Gaussian Splatting
optimization scheme. Extensive experiments on various real-world datasets show
the superiority of our ReconX over state-of-the-art methods in terms of quality
and generalizability. | Project page: https://liuff19.github.io/ReconX | cs.CV | [
"cs.CV",
"cs.AI",
"cs.GR"
] |
||
A Score-Based Density Formula, with Applications in Diffusion Generative
Models | http://arxiv.org/abs/2408.16765v1 | http://arxiv.org/abs/2408.16765v1 | http://arxiv.org/pdf/2408.16765v1 | 2024-08-29 | 2024-08-29 | [
"Gen Li",
"Yuling Yan"
] | [
"",
""
] | Score-based generative models (SGMs) have revolutionized the field of
generative modeling, achieving unprecedented success in generating realistic
and diverse content. Despite empirical advances, the theoretical basis for why
optimizing the evidence lower bound (ELBO) on the log-likelihood is effective
for training diffusion generative models, such as DDPMs, remains largely
unexplored. In this paper, we address this question by establishing a density
formula for a continuous-time diffusion process, which can be viewed as the
continuous-time limit of the forward process in an SGM. This formula reveals
the connection between the target density and the score function associated
with each step of the forward process. Building on this, we demonstrate that
the minimizer of the optimization objective for training DDPMs nearly coincides
with that of the true objective, providing a theoretical foundation for
optimizing DDPMs using the ELBO. Furthermore, we offer new insights into the
role of score-matching regularization in training GANs, the use of ELBO in
diffusion classifiers, and the recently proposed diffusion loss. | cs.LG | [
"cs.LG",
"cs.AI",
"math.PR",
"math.ST",
"stat.ML",
"stat.TH"
] |
|||
Dissecting Out-of-Distribution Detection and Open-Set Recognition: A
Critical Analysis of Methods and Benchmarks | http://arxiv.org/abs/2408.16757v1 | http://arxiv.org/abs/2408.16757v1 | http://arxiv.org/pdf/2408.16757v1 | 2024-08-29 | 2024-08-29 | [
"Hongjun Wang",
"Sagar Vaze",
"Kai Han"
] | [
"",
"",
""
] | Detecting test-time distribution shift has emerged as a key capability for
safely deployed machine learning models, with the question being tackled under
various guises in recent years. In this paper, we aim to provide a consolidated
view of the two largest sub-fields within the community: out-of-distribution
(OOD) detection and open-set recognition (OSR). In particular, we aim to
provide rigorous empirical analysis of different methods across settings and
provide actionable takeaways for practitioners and researchers. Concretely, we
make the following contributions: (i) We perform rigorous cross-evaluation
between state-of-the-art methods in the OOD detection and OSR settings and
identify a strong correlation between the performances of methods for them;
(ii) We propose a new, large-scale benchmark setting which we suggest better
disentangles the problem tackled by OOD detection and OSR, re-evaluating
state-of-the-art OOD detection and OSR methods in this setting; (iii) We
surprisingly find that the best performing method on standard benchmarks
(Outlier Exposure) struggles when tested at scale, while scoring rules which
are sensitive to the deep feature magnitude consistently show promise; and (iv)
We conduct empirical analysis to explain these phenomena and highlight
directions for future research. Code:
\url{https://github.com/Visual-AI/Dissect-OOD-OSR} | Accepted to IJCV, preprint version | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Assessing Large Language Models for Online Extremism Research:
Identification, Explanation, and New Knowledge | http://arxiv.org/abs/2408.16749v1 | http://arxiv.org/abs/2408.16749v1 | http://arxiv.org/pdf/2408.16749v1 | 2024-08-29 | 2024-08-29 | [
"Beidi Dong",
"Jin R. Lee",
"Ziwei Zhu",
"Balassubramanian Srinivasan"
] | [
"",
"",
"",
""
] | The United States has experienced a significant increase in violent
extremism, prompting the need for automated tools to detect and limit the
spread of extremist ideology online. This study evaluates the performance of
Bidirectional Encoder Representations from Transformers (BERT) and Generative
Pre-Trained Transformers (GPT) in detecting and classifying online domestic
extremist posts. We collected social media posts containing "far-right" and
"far-left" ideological keywords and manually labeled them as extremist or
non-extremist. Extremist posts were further classified into one or more of five
contributing elements of extremism based on a working definitional framework.
The BERT model's performance was evaluated based on training data size and
knowledge transfer between categories. We also compared the performance of GPT
3.5 and GPT 4 models using different prompts: na\"ive, layperson-definition,
role-playing, and professional-definition. Results showed that the best
performing GPT models outperformed the best performing BERT models, with more
detailed prompts generally yielding better results. However, overly complex
prompts may impair performance. Different versions of GPT have unique
sensitives to what they consider extremist. GPT 3.5 performed better at
classifying far-left extremist posts, while GPT 4 performed better at
classifying far-right extremist posts. Large language models, represented by
GPT models, hold significant potential for online extremism classification
tasks, surpassing traditional BERT models in a zero-shot setting. Future
research should explore human-computer interactions in optimizing GPT models
for extremist detection and classification tasks to develop more efficient
(e.g., quicker, less effort) and effective (e.g., fewer errors or mistakes)
methods for identifying extremist content. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal
Sampling | http://arxiv.org/abs/2408.16737v1 | http://arxiv.org/abs/2408.16737v1 | http://arxiv.org/pdf/2408.16737v1 | 2024-08-29 | 2024-08-29 | [
"Hritik Bansal",
"Arian Hosseini",
"Rishabh Agarwal",
"Vinh Q. Tran",
"Mehran Kazemi"
] | [
"",
"",
"",
"",
""
] | Training on high-quality synthetic data from strong language models (LMs) is
a common strategy to improve the reasoning performance of LMs. In this work, we
revisit whether this strategy is compute-optimal under a fixed inference budget
(e.g., FLOPs). To do so, we investigate the trade-offs between generating
synthetic data using a stronger but more expensive (SE) model versus a weaker
but cheaper (WC) model. We evaluate the generated data across three key
metrics: coverage, diversity, and false positive rate, and show that the data
from WC models may have higher coverage and diversity, but also exhibit higher
false positive rates. We then finetune LMs on data from SE and WC models in
different settings: knowledge distillation, self-improvement, and a novel
weak-to-strong improvement setup where a weaker LM teaches reasoning to a
stronger LM. Our findings reveal that models finetuned on WC-generated data
consistently outperform those trained on SE-generated data across multiple
benchmarks and multiple choices of WC and SE models. These results challenge
the prevailing practice of relying on SE models for synthetic data generation,
suggesting that WC may be the compute-optimal approach for training advanced LM
reasoners. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | http://arxiv.org/abs/2408.16725v1 | http://arxiv.org/abs/2408.16725v1 | http://arxiv.org/pdf/2408.16725v1 | 2024-08-29 | 2024-08-29 | [
"Zhifei Xie",
"Changqiao Wu"
] | [
"",
""
] | Recent advances in language models have achieved significant progress.
GPT-4o, as a new milestone, has enabled real-time conversations with humans,
demonstrating near-human natural fluency. Such human-computer interaction
necessitates models with the capability to perform reasoning directly with the
audio modality and generate output in streaming. However, this remains beyond
the reach of current academic models, as they typically depend on extra TTS
systems for speech synthesis, resulting in undesirable latency. This paper
introduces the Mini-Omni, an audio-based end-to-end conversational model,
capable of real-time speech interaction. To achieve this capability, we propose
a text-instructed speech generation method, along with batch-parallel
strategies during inference to further boost the performance. Our method also
helps to retain the original model's language capabilities with minimal
degradation, enabling other works to establish real-time interaction
capabilities. We call this training method "Any Model Can Talk". We also
introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential
for future research. | 10 pages | cs.AI | [
"cs.AI",
"cs.CL",
"cs.HC",
"cs.LG",
"cs.SD",
"eess.AS"
] |
||
A GREAT Architecture for Edge-Based Graph Problems Like TSP | http://arxiv.org/abs/2408.16717v1 | http://arxiv.org/abs/2408.16717v1 | http://arxiv.org/pdf/2408.16717v1 | 2024-08-29 | 2024-08-29 | [
"Attila Lischka",
"Jiaming Wu",
"Morteza Haghir Chehreghani",
"Balázs Kulcsár"
] | [
"",
"",
"",
""
] | In the last years, many neural network-based approaches have been proposed to
tackle combinatorial optimization problems such as routing problems. Many of
these approaches are based on graph neural networks (GNNs) or related
transformers, operating on the Euclidean coordinates representing the routing
problems. However, GNNs are inherently not well suited to operate on dense
graphs, such as in routing problems. Furthermore, models operating on Euclidean
coordinates cannot be applied to non-Euclidean versions of routing problems
that are often found in real-world settings. To overcome these limitations, we
propose a novel GNN-related edge-based neural model called Graph Edge Attention
Network (GREAT). We evaluate the performance of GREAT in the
edge-classification task to predict optimal edges in the Traveling Salesman
Problem (TSP). We can use such a trained GREAT model to produce sparse TSP
graph instances, keeping only the edges GREAT finds promising. Compared to
other, non-learning-based methods to sparsify TSP graphs, GREAT can produce
very sparse graphs while keeping most of the optimal edges. Furthermore, we
build a reinforcement learning-based GREAT framework which we apply to
Euclidean and non-Euclidean asymmetric TSP. This framework achieves
state-of-the-art results. | 15 pages, 7 figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction
Retriever | http://arxiv.org/abs/2408.16672v1 | http://arxiv.org/abs/2408.16672v1 | http://arxiv.org/pdf/2408.16672v1 | 2024-08-29 | 2024-08-29 | [
"Rohan Jha",
"Bo Wang",
"Michael Günther",
"Saba Sturua",
"Mohammad Kalim Akram",
"Han Xiao"
] | [
"",
"",
"",
"",
"",
""
] | Multi-vector dense models, such as ColBERT, have proven highly effective in
information retrieval. ColBERT's late interaction scoring approximates the
joint query-document attention seen in cross-encoders while maintaining
inference efficiency closer to traditional dense retrieval models, thanks to
its bi-encoder architecture and recent optimizations in indexing and search. In
this paper, we introduce several improvements to the ColBERT model architecture
and training pipeline, leveraging techniques successful in the more established
single-vector embedding model paradigm, particularly those suited for
heterogeneous multilingual data. Our new model, Jina-ColBERT-v2, demonstrates
strong performance across a range of English and multilingual retrieval tasks,
while also cutting storage requirements by up to 50% compared to previous
models. | cs.IR | [
"cs.IR",
"cs.AI",
"cs.CL",
"68T50",
"I.2.7"
] |
|||
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less
Overfitting and Better Diversity | http://arxiv.org/abs/2408.16673v1 | http://arxiv.org/abs/2408.16673v1 | http://arxiv.org/pdf/2408.16673v1 | 2024-08-29 | 2024-08-29 | [
"Ziniu Li",
"Congliang Chen",
"Tian Xu",
"Zeyu Qin",
"Jiancong Xiao",
"Ruoyu Sun",
"Zhi-Quan Luo"
] | [
"",
"",
"",
"",
"",
"",
""
] | Large language models rely on Supervised Fine-Tuning (SFT) to specialize in
downstream tasks. Cross Entropy (CE) loss is the de facto choice in SFT, but it
often leads to overfitting and limited output diversity due to its aggressive
updates to the data distribution. This paper aim to address these issues by
introducing the maximum entropy principle, which favors models with flatter
distributions that still effectively capture the data. Specifically, we develop
a new distribution matching method called GEM, which solves reverse
Kullback-Leibler divergence minimization with an entropy regularizer.
For the SFT of Llama-3-8B models, GEM outperforms CE in several aspects.
First, when applied to the UltraFeedback dataset to develop general
instruction-following abilities, GEM exhibits reduced overfitting, evidenced by
lower perplexity and better performance on the IFEval benchmark. Furthermore,
GEM enhances output diversity, leading to performance gains of up to 7 points
on math reasoning and code generation tasks using best-of-n sampling, even
without domain-specific data. Second, when fine-tuning with domain-specific
datasets for math reasoning and code generation, GEM also shows less
overfitting and improvements of up to 10 points compared with CE. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners | http://arxiv.org/abs/2408.16768v1 | http://arxiv.org/abs/2408.16768v1 | http://arxiv.org/pdf/2408.16768v1 | 2024-08-29 | 2024-08-29 | [
"Ziyu Guo",
"Renrui Zhang",
"Xiangyang Zhu",
"Chengzhuo Tong",
"Peng Gao",
"Chunyuan Li",
"Pheng-Ann Heng"
] | [
"",
"",
"",
"",
"",
"",
""
] | We introduce SAM2Point, a preliminary exploration adapting Segment Anything
Model 2 (SAM 2) for zero-shot and promptable 3D segmentation. SAM2Point
interprets any 3D data as a series of multi-directional videos, and leverages
SAM 2 for 3D-space segmentation, without further training or 2D-3D projection.
Our framework supports various prompt types, including 3D points, boxes, and
masks, and can generalize across diverse scenarios, such as 3D objects, indoor
scenes, outdoor environments, and raw sparse LiDAR. Demonstrations on multiple
3D datasets, e.g., Objaverse, S3DIS, ScanNet, Semantic3D, and KITTI, highlight
the robust generalization capabilities of SAM2Point. To our best knowledge, we
present the most faithful implementation of SAM in 3D, which may serve as a
starting point for future research in promptable 3D segmentation. Online Demo:
https://huggingface.co/spaces/ZiyuG/SAM2Point . Code:
https://github.com/ZiyuGuo99/SAM2Point . | Work in progress. Online Demo:
https://huggingface.co/spaces/ZiyuG/SAM2Point . Code:
https://github.com/ZiyuGuo99/SAM2Point | cs.CV | [
"cs.CV",
"cs.AI",
"cs.CL"
] |
||
ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion
Model | http://arxiv.org/abs/2408.16767v1 | http://arxiv.org/abs/2408.16767v1 | http://arxiv.org/pdf/2408.16767v1 | 2024-08-29 | 2024-08-29 | [
"Fangfu Liu",
"Wenqiang Sun",
"Hanyang Wang",
"Yikai Wang",
"Haowen Sun",
"Junliang Ye",
"Jun Zhang",
"Yueqi Duan"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Advancements in 3D scene reconstruction have transformed 2D images from the
real world into 3D models, producing realistic 3D results from hundreds of
input photos. Despite great success in dense-view reconstruction scenarios,
rendering a detailed scene from insufficient captured views is still an
ill-posed optimization problem, often resulting in artifacts and distortions in
unseen areas. In this paper, we propose ReconX, a novel 3D scene reconstruction
paradigm that reframes the ambiguous reconstruction challenge as a temporal
generation task. The key insight is to unleash the strong generative prior of
large pre-trained video diffusion models for sparse-view reconstruction.
However, 3D view consistency struggles to be accurately preserved in directly
generated video frames from pre-trained models. To address this, given limited
input views, the proposed ReconX first constructs a global point cloud and
encodes it into a contextual space as the 3D structure condition. Guided by the
condition, the video diffusion model then synthesizes video frames that are
both detail-preserved and exhibit a high degree of 3D consistency, ensuring the
coherence of the scene from various perspectives. Finally, we recover the 3D
scene from the generated video through a confidence-aware 3D Gaussian Splatting
optimization scheme. Extensive experiments on various real-world datasets show
the superiority of our ReconX over state-of-the-art methods in terms of quality
and generalizability. | Project page: https://liuff19.github.io/ReconX | cs.CV | [
"cs.CV",
"cs.AI",
"cs.GR"
] |
||
A Score-Based Density Formula, with Applications in Diffusion Generative
Models | http://arxiv.org/abs/2408.16765v1 | http://arxiv.org/abs/2408.16765v1 | http://arxiv.org/pdf/2408.16765v1 | 2024-08-29 | 2024-08-29 | [
"Gen Li",
"Yuling Yan"
] | [
"",
""
] | Score-based generative models (SGMs) have revolutionized the field of
generative modeling, achieving unprecedented success in generating realistic
and diverse content. Despite empirical advances, the theoretical basis for why
optimizing the evidence lower bound (ELBO) on the log-likelihood is effective
for training diffusion generative models, such as DDPMs, remains largely
unexplored. In this paper, we address this question by establishing a density
formula for a continuous-time diffusion process, which can be viewed as the
continuous-time limit of the forward process in an SGM. This formula reveals
the connection between the target density and the score function associated
with each step of the forward process. Building on this, we demonstrate that
the minimizer of the optimization objective for training DDPMs nearly coincides
with that of the true objective, providing a theoretical foundation for
optimizing DDPMs using the ELBO. Furthermore, we offer new insights into the
role of score-matching regularization in training GANs, the use of ELBO in
diffusion classifiers, and the recently proposed diffusion loss. | cs.LG | [
"cs.LG",
"cs.AI",
"math.PR",
"math.ST",
"stat.ML",
"stat.TH"
] |
|||
Dissecting Out-of-Distribution Detection and Open-Set Recognition: A
Critical Analysis of Methods and Benchmarks | http://arxiv.org/abs/2408.16757v1 | http://arxiv.org/abs/2408.16757v1 | http://arxiv.org/pdf/2408.16757v1 | 2024-08-29 | 2024-08-29 | [
"Hongjun Wang",
"Sagar Vaze",
"Kai Han"
] | [
"",
"",
""
] | Detecting test-time distribution shift has emerged as a key capability for
safely deployed machine learning models, with the question being tackled under
various guises in recent years. In this paper, we aim to provide a consolidated
view of the two largest sub-fields within the community: out-of-distribution
(OOD) detection and open-set recognition (OSR). In particular, we aim to
provide rigorous empirical analysis of different methods across settings and
provide actionable takeaways for practitioners and researchers. Concretely, we
make the following contributions: (i) We perform rigorous cross-evaluation
between state-of-the-art methods in the OOD detection and OSR settings and
identify a strong correlation between the performances of methods for them;
(ii) We propose a new, large-scale benchmark setting which we suggest better
disentangles the problem tackled by OOD detection and OSR, re-evaluating
state-of-the-art OOD detection and OSR methods in this setting; (iii) We
surprisingly find that the best performing method on standard benchmarks
(Outlier Exposure) struggles when tested at scale, while scoring rules which
are sensitive to the deep feature magnitude consistently show promise; and (iv)
We conduct empirical analysis to explain these phenomena and highlight
directions for future research. Code:
\url{https://github.com/Visual-AI/Dissect-OOD-OSR} | Accepted to IJCV, preprint version | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Assessing Large Language Models for Online Extremism Research:
Identification, Explanation, and New Knowledge | http://arxiv.org/abs/2408.16749v1 | http://arxiv.org/abs/2408.16749v1 | http://arxiv.org/pdf/2408.16749v1 | 2024-08-29 | 2024-08-29 | [
"Beidi Dong",
"Jin R. Lee",
"Ziwei Zhu",
"Balassubramanian Srinivasan"
] | [
"",
"",
"",
""
] | The United States has experienced a significant increase in violent
extremism, prompting the need for automated tools to detect and limit the
spread of extremist ideology online. This study evaluates the performance of
Bidirectional Encoder Representations from Transformers (BERT) and Generative
Pre-Trained Transformers (GPT) in detecting and classifying online domestic
extremist posts. We collected social media posts containing "far-right" and
"far-left" ideological keywords and manually labeled them as extremist or
non-extremist. Extremist posts were further classified into one or more of five
contributing elements of extremism based on a working definitional framework.
The BERT model's performance was evaluated based on training data size and
knowledge transfer between categories. We also compared the performance of GPT
3.5 and GPT 4 models using different prompts: na\"ive, layperson-definition,
role-playing, and professional-definition. Results showed that the best
performing GPT models outperformed the best performing BERT models, with more
detailed prompts generally yielding better results. However, overly complex
prompts may impair performance. Different versions of GPT have unique
sensitives to what they consider extremist. GPT 3.5 performed better at
classifying far-left extremist posts, while GPT 4 performed better at
classifying far-right extremist posts. Large language models, represented by
GPT models, hold significant potential for online extremism classification
tasks, surpassing traditional BERT models in a zero-shot setting. Future
research should explore human-computer interactions in optimizing GPT models
for extremist detection and classification tasks to develop more efficient
(e.g., quicker, less effort) and effective (e.g., fewer errors or mistakes)
methods for identifying extremist content. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal
Sampling | http://arxiv.org/abs/2408.16737v1 | http://arxiv.org/abs/2408.16737v1 | http://arxiv.org/pdf/2408.16737v1 | 2024-08-29 | 2024-08-29 | [
"Hritik Bansal",
"Arian Hosseini",
"Rishabh Agarwal",
"Vinh Q. Tran",
"Mehran Kazemi"
] | [
"",
"",
"",
"",
""
] | Training on high-quality synthetic data from strong language models (LMs) is
a common strategy to improve the reasoning performance of LMs. In this work, we
revisit whether this strategy is compute-optimal under a fixed inference budget
(e.g., FLOPs). To do so, we investigate the trade-offs between generating
synthetic data using a stronger but more expensive (SE) model versus a weaker
but cheaper (WC) model. We evaluate the generated data across three key
metrics: coverage, diversity, and false positive rate, and show that the data
from WC models may have higher coverage and diversity, but also exhibit higher
false positive rates. We then finetune LMs on data from SE and WC models in
different settings: knowledge distillation, self-improvement, and a novel
weak-to-strong improvement setup where a weaker LM teaches reasoning to a
stronger LM. Our findings reveal that models finetuned on WC-generated data
consistently outperform those trained on SE-generated data across multiple
benchmarks and multiple choices of WC and SE models. These results challenge
the prevailing practice of relying on SE models for synthetic data generation,
suggesting that WC may be the compute-optimal approach for training advanced LM
reasoners. | cs.CL | [
"cs.CL",
"cs.AI"
] |
|||
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming | http://arxiv.org/abs/2408.16725v1 | http://arxiv.org/abs/2408.16725v1 | http://arxiv.org/pdf/2408.16725v1 | 2024-08-29 | 2024-08-29 | [
"Zhifei Xie",
"Changqiao Wu"
] | [
"",
""
] | Recent advances in language models have achieved significant progress.
GPT-4o, as a new milestone, has enabled real-time conversations with humans,
demonstrating near-human natural fluency. Such human-computer interaction
necessitates models with the capability to perform reasoning directly with the
audio modality and generate output in streaming. However, this remains beyond
the reach of current academic models, as they typically depend on extra TTS
systems for speech synthesis, resulting in undesirable latency. This paper
introduces the Mini-Omni, an audio-based end-to-end conversational model,
capable of real-time speech interaction. To achieve this capability, we propose
a text-instructed speech generation method, along with batch-parallel
strategies during inference to further boost the performance. Our method also
helps to retain the original model's language capabilities with minimal
degradation, enabling other works to establish real-time interaction
capabilities. We call this training method "Any Model Can Talk". We also
introduce the VoiceAssistant-400K dataset to fine-tune models optimized for
speech output. To our best knowledge, Mini-Omni is the first fully end-to-end,
open-source model for real-time speech interaction, offering valuable potential
for future research. | 10 pages | cs.AI | [
"cs.AI",
"cs.CL",
"cs.HC",
"cs.LG",
"cs.SD",
"eess.AS"
] |
||
A GREAT Architecture for Edge-Based Graph Problems Like TSP | http://arxiv.org/abs/2408.16717v1 | http://arxiv.org/abs/2408.16717v1 | http://arxiv.org/pdf/2408.16717v1 | 2024-08-29 | 2024-08-29 | [
"Attila Lischka",
"Jiaming Wu",
"Morteza Haghir Chehreghani",
"Balázs Kulcsár"
] | [
"",
"",
"",
""
] | In the last years, many neural network-based approaches have been proposed to
tackle combinatorial optimization problems such as routing problems. Many of
these approaches are based on graph neural networks (GNNs) or related
transformers, operating on the Euclidean coordinates representing the routing
problems. However, GNNs are inherently not well suited to operate on dense
graphs, such as in routing problems. Furthermore, models operating on Euclidean
coordinates cannot be applied to non-Euclidean versions of routing problems
that are often found in real-world settings. To overcome these limitations, we
propose a novel GNN-related edge-based neural model called Graph Edge Attention
Network (GREAT). We evaluate the performance of GREAT in the
edge-classification task to predict optimal edges in the Traveling Salesman
Problem (TSP). We can use such a trained GREAT model to produce sparse TSP
graph instances, keeping only the edges GREAT finds promising. Compared to
other, non-learning-based methods to sparsify TSP graphs, GREAT can produce
very sparse graphs while keeping most of the optimal edges. Furthermore, we
build a reinforcement learning-based GREAT framework which we apply to
Euclidean and non-Euclidean asymmetric TSP. This framework achieves
state-of-the-art results. | 15 pages, 7 figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction
Retriever | http://arxiv.org/abs/2408.16672v1 | http://arxiv.org/abs/2408.16672v1 | http://arxiv.org/pdf/2408.16672v1 | 2024-08-29 | 2024-08-29 | [
"Rohan Jha",
"Bo Wang",
"Michael Günther",
"Saba Sturua",
"Mohammad Kalim Akram",
"Han Xiao"
] | [
"",
"",
"",
"",
"",
""
] | Multi-vector dense models, such as ColBERT, have proven highly effective in
information retrieval. ColBERT's late interaction scoring approximates the
joint query-document attention seen in cross-encoders while maintaining
inference efficiency closer to traditional dense retrieval models, thanks to
its bi-encoder architecture and recent optimizations in indexing and search. In
this paper, we introduce several improvements to the ColBERT model architecture
and training pipeline, leveraging techniques successful in the more established
single-vector embedding model paradigm, particularly those suited for
heterogeneous multilingual data. Our new model, Jina-ColBERT-v2, demonstrates
strong performance across a range of English and multilingual retrieval tasks,
while also cutting storage requirements by up to 50% compared to previous
models. | cs.IR | [
"cs.IR",
"cs.AI",
"cs.CL",
"68T50",
"I.2.7"
] |
|||
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less
Overfitting and Better Diversity | http://arxiv.org/abs/2408.16673v1 | http://arxiv.org/abs/2408.16673v1 | http://arxiv.org/pdf/2408.16673v1 | 2024-08-29 | 2024-08-29 | [
"Ziniu Li",
"Congliang Chen",
"Tian Xu",
"Zeyu Qin",
"Jiancong Xiao",
"Ruoyu Sun",
"Zhi-Quan Luo"
] | [
"",
"",
"",
"",
"",
"",
""
] | Large language models rely on Supervised Fine-Tuning (SFT) to specialize in
downstream tasks. Cross Entropy (CE) loss is the de facto choice in SFT, but it
often leads to overfitting and limited output diversity due to its aggressive
updates to the data distribution. This paper aim to address these issues by
introducing the maximum entropy principle, which favors models with flatter
distributions that still effectively capture the data. Specifically, we develop
a new distribution matching method called GEM, which solves reverse
Kullback-Leibler divergence minimization with an entropy regularizer.
For the SFT of Llama-3-8B models, GEM outperforms CE in several aspects.
First, when applied to the UltraFeedback dataset to develop general
instruction-following abilities, GEM exhibits reduced overfitting, evidenced by
lower perplexity and better performance on the IFEval benchmark. Furthermore,
GEM enhances output diversity, leading to performance gains of up to 7 points
on math reasoning and code generation tasks using best-of-n sampling, even
without domain-specific data. Second, when fine-tuning with domain-specific
datasets for math reasoning and code generation, GEM also shows less
overfitting and improvements of up to 10 points compared with CE. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Iterative Graph Alignment | http://arxiv.org/abs/2408.16667v1 | http://arxiv.org/abs/2408.16667v1 | http://arxiv.org/pdf/2408.16667v1 | 2024-08-29 | 2024-08-29 | [
"Fangyuan Yu",
"Hardeep Singh Arora",
"Matt Johnson"
] | [
"",
"",
""
] | By compressing diverse narratives, LLMs go beyond memorization, achieving
intelligence by capturing generalizable causal relationships. However, they
suffer from local 'representation gaps' due to insufficient training data
diversity, limiting their real-world utility, especially in tasks requiring
strict alignment to rules. Traditional alignment methods relying on heavy human
annotations are inefficient and unscalable. Recent self-alignment techniques
also fall short, as they often depend on self-selection based prompting and
memorization-based learning. To address these issues, we introduce Iterative
Graph Alignment (IGA), an annotation-free rule-based alignment algorithm. A
teacher model (VLM) employs Iterative Graph Prompting (IGP) to create logical
graphs and reference answers. The student model (LLM) identifies local
knowledge gaps by attempting to align its responses with these references,
collaborating with helper models to generate diverse answers. These aligned
responses are then used for iterative supervised fine-tuning (SFT). Our
evaluations across five rule-based scenarios demonstrate IGP's effectiveness,
with a 73.12\% alignment improvement in Claude Sonnet 3.5, and
Llama3-8B-Instruct achieving an 86.20\% improvement, outperforming Claude
Sonnet 3.5 in rule-based alignment. | 12 pages, 4 figures | cs.LG | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.MA"
] |
||
DriveGenVLM: Real-world Video Generation for Vision Language Model based
Autonomous Driving | http://arxiv.org/abs/2408.16647v1 | http://arxiv.org/abs/2408.16647v1 | http://arxiv.org/pdf/2408.16647v1 | 2024-08-29 | 2024-08-29 | [
"Yongjie Fu",
"Anmol Jain",
"Xuan Di",
"Xu Chen",
"Zhaobin Mo"
] | [
"",
"",
"",
"",
""
] | The advancement of autonomous driving technologies necessitates increasingly
sophisticated methods for understanding and predicting real-world scenarios.
Vision language models (VLMs) are emerging as revolutionary tools with
significant potential to influence autonomous driving. In this paper, we
propose the DriveGenVLM framework to generate driving videos and use VLMs to
understand them. To achieve this, we employ a video generation framework
grounded in denoising diffusion probabilistic models (DDPM) aimed at predicting
real-world video sequences. We then explore the adequacy of our generated
videos for use in VLMs by employing a pre-trained model known as Efficient
In-context Learning on Egocentric Videos (EILEV). The diffusion model is
trained with the Waymo open dataset and evaluated using the Fr\'echet Video
Distance (FVD) score to ensure the quality and realism of the generated videos.
Corresponding narrations are provided by EILEV for these generated videos,
which may be beneficial in the autonomous driving domain. These narrations can
enhance traffic scene understanding, aid in navigation, and improve planning
capabilities. The integration of video generation with VLMs in the DriveGenVLM
framework represents a significant step forward in leveraging advanced AI
models to address complex challenges in autonomous driving. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
RLCP: A Reinforcement Learning-based Copyright Protection Method for
Text-to-Image Diffusion Model | http://arxiv.org/abs/2408.16634v1 | http://arxiv.org/abs/2408.16634v1 | http://arxiv.org/pdf/2408.16634v1 | 2024-08-29 | 2024-08-29 | [
"Zhuan Shi",
"Jing Yan",
"Xiaoli Tang",
"Lingjuan Lyu",
"Boi Faltings"
] | [
"",
"",
"",
"",
""
] | The increasing sophistication of text-to-image generative models has led to
complex challenges in defining and enforcing copyright infringement criteria
and protection. Existing methods, such as watermarking and dataset
deduplication, fail to provide comprehensive solutions due to the lack of
standardized metrics and the inherent complexity of addressing copyright
infringement in diffusion models. To deal with these challenges, we propose a
Reinforcement Learning-based Copyright Protection(RLCP) method for
Text-to-Image Diffusion Model, which minimizes the generation of
copyright-infringing content while maintaining the quality of the
model-generated dataset. Our approach begins with the introduction of a novel
copyright metric grounded in copyright law and court precedents on
infringement. We then utilize the Denoising Diffusion Policy Optimization
(DDPO) framework to guide the model through a multi-step decision-making
process, optimizing it using a reward function that incorporates our proposed
copyright metric. Additionally, we employ KL divergence as a regularization
term to mitigate some failure modes and stabilize RL fine-tuning. Experiments
conducted on 3 mixed datasets of copyright and non-copyright images demonstrate
that our approach significantly reduces copyright infringement risk while
maintaining image quality. | arXiv admin note: text overlap with arXiv:2403.12052 by other authors | cs.CY | [
"cs.CY",
"cs.AI",
"cs.CR"
] |
||
Optimizing Automated Picking Systems in Warehouse Robots Using Machine
Learning | http://arxiv.org/abs/2408.16633v1 | http://arxiv.org/abs/2408.16633v1 | http://arxiv.org/pdf/2408.16633v1 | 2024-08-29 | 2024-08-29 | [
"Keqin Li",
"Jin Wang",
"Xubo Wu",
"Xirui Peng",
"Runmian Chang",
"Xiaoyu Deng",
"Yiwen Kang",
"Yue Yang",
"Fanghao Ni",
"Bo Hong"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | With the rapid growth of global e-commerce, the demand for automation in the
logistics industry is increasing. This study focuses on automated picking
systems in warehouses, utilizing deep learning and reinforcement learning
technologies to enhance picking efficiency and accuracy while reducing system
failure rates. Through empirical analysis, we demonstrate the effectiveness of
these technologies in improving robot picking performance and adaptability to
complex environments. The results show that the integrated machine learning
model significantly outperforms traditional methods, effectively addressing the
challenges of peak order processing, reducing operational errors, and improving
overall logistics efficiency. Additionally, by analyzing environmental factors,
this study further optimizes system design to ensure efficient and stable
operation under variable conditions. This research not only provides innovative
solutions for logistics automation but also offers a theoretical and empirical
foundation for future technological development and application. | cs.RO | [
"cs.RO",
"cs.AI"
] |
|||
Maelstrom Networks | http://arxiv.org/abs/2408.16632v1 | http://arxiv.org/abs/2408.16632v1 | http://arxiv.org/pdf/2408.16632v1 | 2024-08-29 | 2024-08-29 | [
"Matthew Evanusa",
"Cornelia Fermüller",
"Yiannis Aloimonos"
] | [
"",
"",
""
] | Artificial Neural Networks has struggled to devise a way to incorporate
working memory into neural networks. While the ``long term'' memory can be seen
as the learned weights, the working memory consists likely more of dynamical
activity, that is missing from feed-forward models. Current state of the art
models such as transformers tend to ``solve'' this by ignoring working memory
entirely and simply process the sequence as an entire piece of data; however
this means the network cannot process the sequence in an online fashion, and
leads to an immense explosion in memory requirements. Here, inspired by a
combination of controls, reservoir computing, deep learning, and recurrent
neural networks, we offer an alternative paradigm that combines the strength of
recurrent networks, with the pattern matching capability of feed-forward neural
networks, which we call the \textit{Maelstrom Networks} paradigm. This paradigm
leaves the recurrent component - the \textit{Maelstrom} - unlearned, and
offloads the learning to a powerful feed-forward network. This allows the
network to leverage the strength of feed-forward training without unrolling the
network, and allows for the memory to be implemented in new neuromorphic
hardware. It endows a neural network with a sequential memory that takes
advantage of the inductive bias that data is organized causally in the temporal
domain, and imbues the network with a state that represents the agent's
``self'', moving through the environment. This could also lead the way to
continual learning, with the network modularized and ``'protected'' from
overwrites that come with new data. In addition to aiding in solving these
performance problems that plague current non-temporal deep networks, this also
could finally lead towards endowing artificial networks with a sense of
``self''. | cs.NE | [
"cs.NE",
"cs.AI"
] |
|||
LLMs generate structurally realistic social networks but overestimate
political homophily | http://arxiv.org/abs/2408.16629v1 | http://arxiv.org/abs/2408.16629v1 | http://arxiv.org/pdf/2408.16629v1 | 2024-08-29 | 2024-08-29 | [
"Serina Chang",
"Alicja Chaszczewicz",
"Emma Wang",
"Maya Josifovska",
"Emma Pierson",
"Jure Leskovec"
] | [
"",
"",
"",
"",
"",
""
] | Generating social networks is essential for many applications, such as
epidemic modeling and social simulations. Prior approaches either involve deep
learning models, which require many observed networks for training, or stylized
models, which are limited in their realism and flexibility. In contrast, LLMs
offer the potential for zero-shot and flexible network generation. However, two
key questions are: (1) are LLM's generated networks realistic, and (2) what are
risks of bias, given the importance of demographics in forming social ties? To
answer these questions, we develop three prompting methods for network
generation and compare the generated networks to real social networks. We find
that more realistic networks are generated with "local" methods, where the LLM
constructs relations for one persona at a time, compared to "global" methods
that construct the entire network at once. We also find that the generated
networks match real networks on many characteristics, including density,
clustering, community structure, and degree. However, we find that LLMs
emphasize political homophily over all other types of homophily and
overestimate political homophily relative to real-world measures. | cs.CY | [
"cs.CY",
"cs.AI",
"cs.SI"
] |
|||
Towards Infusing Auxiliary Knowledge for Distracted Driver Detection | http://arxiv.org/abs/2408.16621v1 | http://arxiv.org/abs/2408.16621v1 | http://arxiv.org/pdf/2408.16621v1 | 2024-08-29 | 2024-08-29 | [
"Ishwar B Balappanawar",
"Ashmit Chamoli",
"Ruwan Wickramarachchi",
"Aditya Mishra",
"Ponnurangam Kumaraguru",
"Amit P. Sheth"
] | [
"",
"",
"",
"",
"",
""
] | Distracted driving is a leading cause of road accidents globally.
Identification of distracted driving involves reliably detecting and
classifying various forms of driver distraction (e.g., texting, eating, or
using in-car devices) from in-vehicle camera feeds to enhance road safety. This
task is challenging due to the need for robust models that can generalize to a
diverse set of driver behaviors without requiring extensive annotated datasets.
In this paper, we propose KiD3, a novel method for distracted driver detection
(DDD) by infusing auxiliary knowledge about semantic relations between entities
in a scene and the structural configuration of the driver's pose. Specifically,
we construct a unified framework that integrates the scene graphs, and driver
pose information with the visual cues in video frames to create a holistic
representation of the driver's actions.Our results indicate that KiD3 achieves
a 13.64% accuracy improvement over the vision-only baseline by incorporating
such auxiliary knowledge with visual information. | Accepted at KiL 2024: Workshop on Knowledge-infused Learning
co-located with 30th ACM KDD Conference | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG",
"I.2.0"
] |
||
Hyperdimensional Vector Tsetlin Machines with Applications to Sequence
Learning and Generation | http://arxiv.org/abs/2408.16620v1 | http://arxiv.org/abs/2408.16620v1 | http://arxiv.org/pdf/2408.16620v1 | 2024-08-29 | 2024-08-29 | [
"Christian D. Blakely"
] | [
""
] | We construct a two-layered model for learning and generating sequential data
that is both computationally fast and competitive with vanilla Tsetlin
machines, adding numerous advantages. Through the use of hyperdimensional
vector computing (HVC) algebras and Tsetlin machine clause structures, we
demonstrate that the combination of both inherits the generality of data
encoding and decoding of HVC with the fast interpretable nature of Tsetlin
machines to yield a powerful machine learning model. We apply the approach in
two areas, namely in forecasting, generating new sequences, and classification.
For the latter, we derive results for the entire UCR Time Series Archive and
compare with the standard benchmarks to see how well the method competes in
time series classification. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Examination of Code generated by Large Language Models | http://arxiv.org/abs/2408.16601v1 | http://arxiv.org/abs/2408.16601v1 | http://arxiv.org/pdf/2408.16601v1 | 2024-08-29 | 2024-08-29 | [
"Robin Beer",
"Alexander Feix",
"Tim Guttzeit",
"Tamara Muras",
"Vincent Müller",
"Maurice Rauscher",
"Florian Schäffler",
"Welf Löwe"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Large language models (LLMs), such as ChatGPT and Copilot, are transforming
software development by automating code generation and, arguably, enable rapid
prototyping, support education, and boost productivity. Therefore, correctness
and quality of the generated code should be on par with manually written code.
To assess the current state of LLMs in generating correct code of high quality,
we conducted controlled experiments with ChatGPT and Copilot: we let the LLMs
generate simple algorithms in Java and Python along with the corresponding unit
tests and assessed the correctness and the quality (coverage) of the generated
(test) codes. We observed significant differences between the LLMs, between the
languages, between algorithm and test codes, and over time. The present paper
reports these results together with the experimental methods allowing repeated
and comparable assessments for more algorithms, languages, and LLMs over time. | cs.SE | [
"cs.SE",
"cs.AI",
"I.2.2"
] |
|||
Enhancing Dialogue Generation in Werewolf Game Through Situation
Analysis and Persuasion Strategies | http://arxiv.org/abs/2408.16586v1 | http://arxiv.org/abs/2408.16586v1 | http://arxiv.org/pdf/2408.16586v1 | 2024-08-29 | 2024-08-29 | [
"Zhiyang Qi",
"Michimasa Inaba"
] | [
"",
""
] | Recent advancements in natural language processing, particularly with large
language models (LLMs) like GPT-4, have significantly enhanced dialogue
systems, enabling them to generate more natural and fluent conversations.
Despite these improvements, challenges persist, such as managing continuous
dialogues, memory retention, and minimizing hallucinations. The AIWolfDial2024
addresses these challenges by employing the Werewolf Game, an incomplete
information game, to test the capabilities of LLMs in complex interactive
environments. This paper introduces a LLM-based Werewolf Game AI, where each
role is supported by situation analysis to aid response generation.
Additionally, for the werewolf role, various persuasion strategies, including
logical appeal, credibility appeal, and emotional appeal, are employed to
effectively persuade other players to align with its actions. | Accepted to the AIWolfDial2024 workshop at INLG 2024 | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
Seeking the Sufficiency and Necessity Causal Features in Multimodal
Representation Learning | http://arxiv.org/abs/2408.16577v1 | http://arxiv.org/abs/2408.16577v1 | http://arxiv.org/pdf/2408.16577v1 | 2024-08-29 | 2024-08-29 | [
"Boyu Chen",
"Junjie Liu",
"Zhu Li",
"Mengyue yang"
] | [
"",
"",
"",
""
] | Learning representations with a high Probability of Necessary and Sufficient
Causes (PNS) has been shown to enhance deep learning models' ability. This task
involves identifying causal features that are both sufficient (guaranteeing the
outcome) and necessary (without which the outcome cannot occur). However,
current research predominantly focuses on unimodal data, and extending PNS
learning to multimodal settings presents significant challenges. The challenges
arise as the conditions for PNS identifiability, Exogeneity and Monotonicity,
need to be reconsidered in a multimodal context, where sufficient and necessary
causal features are distributed across different modalities. To address this,
we first propose conceptualizing multimodal representations as comprising
modality-invariant and modality-specific components. We then analyze PNS
identifiability for each component, while ensuring non-trivial PNS estimation.
Finally, we formulate tractable optimization objectives that enable multimodal
models to learn high-PNS representations, thereby enhancing their predictive
performance. Experiments demonstrate the effectiveness of our method on both
synthetic and real-world data. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks | http://arxiv.org/abs/2408.16537v1 | http://arxiv.org/abs/2408.16537v1 | http://arxiv.org/pdf/2408.16537v1 | 2024-08-29 | 2024-08-29 | [
"Xing Ai",
"Guanyu Zhu",
"Yulin Zhu",
"Yu Zheng",
"Gaolei Li",
"Jianhua Li",
"Kai Zhou"
] | [
"",
"",
"",
"",
"",
"",
""
] | Graph Neural Networks (GNNs) have demonstrated commendable performance for
graph-structured data. Yet, GNNs are often vulnerable to adversarial structural
attacks as embedding generation relies on graph topology. Existing efforts are
dedicated to purifying the maliciously modified structure or applying adaptive
aggregation, thereby enhancing the robustness against adversarial structural
attacks. It is inevitable for a defender to consume heavy computational costs
due to lacking prior knowledge about modified structures. To this end, we
propose an efficient defense method, called Simple and Fast Robust Graph Neural
Network (SFR-GNN), supported by mutual information theory. The SFR-GNN first
pre-trains a GNN model using node attributes and then fine-tunes it over the
modified graph in the manner of contrastive learning, which is free of
purifying modified structures and adaptive aggregation, thus achieving great
efficiency gains. Consequently, SFR-GNN exhibits a 24%--162% speedup compared
to advanced robust models, demonstrating superior robustness for node
classification tasks. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Adaptive Variational Continual Learning via Task-Heuristic Modelling | http://arxiv.org/abs/2408.16517v1 | http://arxiv.org/abs/2408.16517v1 | http://arxiv.org/pdf/2408.16517v1 | 2024-08-29 | 2024-08-29 | [
"Fan Yang"
] | [
""
] | Variational continual learning (VCL) is a turn-key learning algorithm that
has state-of-the-art performance among the best continual learning models. In
our work, we explore an extension of the generalized variational continual
learning (GVCL) model, named AutoVCL, which combines task heuristics for
informed learning and model optimization. We demonstrate that our model
outperforms the standard GVCL with fixed hyperparameters, benefiting from the
automatic adjustment of the hyperparameter based on the difficulty and
similarity of the incoming task compared to the previous tasks. | 4 pages, 2 figures, 3 tables | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
On-device AI: Quantization-aware Training of Transformers in Time-Series | http://arxiv.org/abs/2408.16495v1 | http://arxiv.org/abs/2408.16495v1 | http://arxiv.org/pdf/2408.16495v1 | 2024-08-29 | 2024-08-29 | [
"Tianheng Ling",
"Gregor Schiele"
] | [
"",
""
] | Artificial Intelligence (AI) models for time-series in pervasive computing
keep getting larger and more complicated. The Transformer model is by far the
most compelling of these AI models. However, it is difficult to obtain the
desired performance when deploying such a massive model on a sensor device with
limited resources. My research focuses on optimizing the Transformer model for
time-series forecasting tasks. The optimized model will be deployed as hardware
accelerators on embedded Field Programmable Gate Arrays (FPGAs). I will
investigate the impact of applying Quantization-aware Training to the
Transformer model to reduce its size and runtime memory footprint while
maximizing the advantages of FPGAs. | This paper is accepted by 2023 IEEE International Conference on
Pervasive Computing and Communications(PhD Forum) | 10.1109/PerComWorkshops56833.2023.10150339 | cs.LG | [
"cs.LG",
"cs.AI"
] |
|
Integrating Features for Recognizing Human Activities through Optimized
Parameters in Graph Convolutional Networks and Transformer Architectures | http://arxiv.org/abs/2408.16442v1 | http://arxiv.org/abs/2408.16442v1 | http://arxiv.org/pdf/2408.16442v1 | 2024-08-29 | 2024-08-29 | [
"Mohammad Belal",
"Taimur Hassan",
"Abdelfatah Hassan",
"Nael Alsheikh",
"Noureldin Elhendawi",
"Irfan Hussain"
] | [
"",
"",
"",
"",
"",
""
] | Human activity recognition is a major field of study that employs computer
vision, machine vision, and deep learning techniques to categorize human
actions. The field of deep learning has made significant progress, with
architectures that are extremely effective at capturing human dynamics. This
study emphasizes the influence of feature fusion on the accuracy of activity
recognition. This technique addresses the limitation of conventional models,
which face difficulties in identifying activities because of their limited
capacity to understand spatial and temporal features. The technique employs
sensory data obtained from four publicly available datasets: HuGaDB, PKU-MMD,
LARa, and TUG. The accuracy and F1-score of two deep learning models,
specifically a Transformer model and a Parameter-Optimized Graph Convolutional
Network (PO-GCN), were evaluated using these datasets. The feature fusion
technique integrated the final layer features from both models and inputted
them into a classifier. Empirical evidence demonstrates that PO-GCN outperforms
standard models in activity recognition. HuGaDB demonstrated a 2.3% improvement
in accuracy and a 2.2% increase in F1-score. TUG showed a 5% increase in
accuracy and a 0.5% rise in F1-score. On the other hand, LARa and PKU-MMD
achieved lower accuracies of 64% and 69% respectively. This indicates that the
integration of features enhanced the performance of both the Transformer model
and PO-GCN. | 6 pages, 1 figure, conference | cs.CV | [
"cs.CV",
"cs.AI",
"cs.RO"
] |
||
Gradient-free variational learning with conditional mixture networks | http://arxiv.org/abs/2408.16429v1 | http://arxiv.org/abs/2408.16429v1 | http://arxiv.org/pdf/2408.16429v1 | 2024-08-29 | 2024-08-29 | [
"Conor Heins",
"Hao Wu",
"Dimitrije Markovic",
"Alexander Tschantz",
"Jeff Beck",
"Christopher Buckley"
] | [
"",
"",
"",
"",
"",
""
] | Balancing computational efficiency with robust predictive performance is
crucial in supervised learning, especially for critical applications. Standard
deep learning models, while accurate and scalable, often lack probabilistic
features like calibrated predictions and uncertainty quantification. Bayesian
methods address these issues but can be computationally expensive as model and
data complexity increase. Previous work shows that fast variational methods can
reduce the compute requirements of Bayesian methods by eliminating the need for
gradient computation or sampling, but are often limited to simple models. We
demonstrate that conditional mixture networks (CMNs), a probabilistic variant
of the mixture-of-experts (MoE) model, are suitable for fast, gradient-free
inference and can solve complex classification tasks. CMNs employ linear
experts and a softmax gating network. By exploiting conditional conjugacy and
P\'olya-Gamma augmentation, we furnish Gaussian likelihoods for the weights of
both the linear experts and the gating network. This enables efficient
variational updates using coordinate ascent variational inference (CAVI),
avoiding traditional gradient-based optimization. We validate this approach by
training two-layer CMNs on standard benchmarks from the UCI repository. Our
method, CAVI-CMN, achieves competitive and often superior predictive accuracy
compared to maximum likelihood estimation (MLE) with backpropagation, while
maintaining competitive runtime and full posterior distributions over all model
parameters. Moreover, as input size or the number of experts increases,
computation time scales competitively with MLE and other gradient-based
solutions like black-box variational inference (BBVI), making CAVI-CMN a
promising tool for deep, fast, and gradient-free Bayesian networks. | 16 pages main text (3 figures), including references. 9 pages
supplementary material (5 figures) | cs.LG | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
||
COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion
Estimation | http://arxiv.org/abs/2408.16426v1 | http://arxiv.org/abs/2408.16426v1 | http://arxiv.org/pdf/2408.16426v1 | 2024-08-29 | 2024-08-29 | [
"Jiefeng Li",
"Ye Yuan",
"Davis Rempe",
"Haotian Zhang",
"Pavlo Molchanov",
"Cewu Lu",
"Jan Kautz",
"Umar Iqbal"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Estimating global human motion from moving cameras is challenging due to the
entanglement of human and camera motions. To mitigate the ambiguity, existing
methods leverage learned human motion priors, which however often result in
oversmoothed motions with misaligned 2D projections. To tackle this problem, we
propose COIN, a control-inpainting motion diffusion prior that enables
fine-grained control to disentangle human and camera motions. Although
pre-trained motion diffusion models encode rich motion priors, we find it
non-trivial to leverage such knowledge to guide global motion estimation from
RGB videos. COIN introduces a novel control-inpainting score distillation
sampling method to ensure well-aligned, consistent, and high-quality motion
from the diffusion prior within a joint optimization framework. Furthermore, we
introduce a new human-scene relation loss to alleviate the scale ambiguity by
enforcing consistency among the humans, camera, and scene. Experiments on three
challenging benchmarks demonstrate the effectiveness of COIN, which outperforms
the state-of-the-art methods in terms of global human motion estimation and
camera motion estimation. As an illustrative example, COIN outperforms the
state-of-the-art method by 33% in world joint position error (W-MPJPE) on the
RICH dataset. | ECCV 2024 | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Fourier Spectral Physics Informed Neural Network: An Efficient and
Low-Memory PINN | http://arxiv.org/abs/2408.16414v1 | http://arxiv.org/abs/2408.16414v1 | http://arxiv.org/pdf/2408.16414v1 | 2024-08-29 | 2024-08-29 | [
"Tianchi Yu",
"Yiming Qi",
"Ivan Oseledets",
"Shiyi Chen"
] | [
"",
"",
"",
""
] | With growing investigations into solving partial differential equations by
physics-informed neural networks (PINNs), more accurate and efficient PINNs are
required to meet the practical demands of scientific computing. One bottleneck
of current PINNs is computing the high-order derivatives via automatic
differentiation which often necessitates substantial computing resources. In
this paper, we focus on removing the automatic differentiation of the spatial
derivatives and propose a spectral-based neural network that substitutes the
differential operator with a multiplication. Compared to the PINNs, our
approach requires lower memory and shorter training time. Thanks to the
exponential convergence of the spectral basis, our approach is more accurate.
Moreover, to handle the different situations between physics domain and
spectral domain, we provide two strategies to train networks by their spectral
information. Through a series of comprehensive experiments, We validate the
aforementioned merits of our proposed network. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA",
"physics.comp-ph"
] |
|||
DetectBERT: Towards Full App-Level Representation Learning to Detect
Android Malware | http://arxiv.org/abs/2408.16353v1 | http://arxiv.org/abs/2408.16353v1 | http://arxiv.org/pdf/2408.16353v1 | 2024-08-29 | 2024-08-29 | [
"Tiezhu Sun",
"Nadia Daoudi",
"Kisub Kim",
"Kevin Allix",
"Tegawendé F. Bissyandé",
"Jacques Klein"
] | [
"",
"",
"",
"",
"",
""
] | Recent advancements in ML and DL have significantly improved Android malware
detection, yet many methodologies still rely on basic static analysis,
bytecode, or function call graphs that often fail to capture complex malicious
behaviors. DexBERT, a pre-trained BERT-like model tailored for Android
representation learning, enriches class-level representations by analyzing
Smali code extracted from APKs. However, its functionality is constrained by
its inability to process multiple Smali classes simultaneously. This paper
introduces DetectBERT, which integrates correlated Multiple Instance Learning
(c-MIL) with DexBERT to handle the high dimensionality and variability of
Android malware, enabling effective app-level detection. By treating
class-level features as instances within MIL bags, DetectBERT aggregates these
into a comprehensive app-level representation. Our evaluation demonstrates that
DetectBERT not only surpasses existing state-of-the-art detection methods but
also adapts to evolving malware threats. Moreover, the versatility of the
DetectBERT framework holds promising potential for broader applications in
app-level analysis and other software engineering tasks, offering new avenues
for research and development. | Accepted at ESEM 2024 | cs.SE | [
"cs.SE",
"cs.AI",
"cs.CR"
] |
||
Toward Robust Early Detection of Alzheimer's Disease via an Integrated
Multimodal Learning Approach | http://arxiv.org/abs/2408.16343v1 | http://arxiv.org/abs/2408.16343v1 | http://arxiv.org/pdf/2408.16343v1 | 2024-08-29 | 2024-08-29 | [
"Yifei Chen",
"Shenghao Zhu",
"Zhaojie Fang",
"Chang Liu",
"Binfeng Zou",
"Yuhe Wang",
"Shuo Chang",
"Fan Jia",
"Feiwei Qin",
"Jin Fan",
"Yong Peng",
"Changmiao Wang"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by
memory loss, executive dysfunction, and personality changes. Early diagnosis is
challenging due to subtle symptoms and varied presentations, often leading to
misdiagnosis with traditional unimodal diagnostic methods due to their limited
scope. This study introduces an advanced multimodal classification model that
integrates clinical, cognitive, neuroimaging, and EEG data to enhance
diagnostic accuracy. The model incorporates a feature tagger with a tabular
data coding architecture and utilizes the TimesBlock module to capture
intricate temporal patterns in Electroencephalograms (EEG) data. By employing
Cross-modal Attention Aggregation module, the model effectively fuses Magnetic
Resonance Imaging (MRI) spatial information with EEG temporal data,
significantly improving the distinction between AD, Mild Cognitive Impairment,
and Normal Cognition. Simultaneously, we have constructed the first AD
classification dataset that includes three modalities: EEG, MRI, and tabular
data. Our innovative approach aims to facilitate early diagnosis and
intervention, potentially slowing the progression of AD. The source code and
our private ADMC dataset are available at https://github.com/JustlfC03/MSTNet. | 5 pages, 2 figures | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Self-Improving Diffusion Models with Synthetic Data | http://arxiv.org/abs/2408.16333v1 | http://arxiv.org/abs/2408.16333v1 | http://arxiv.org/pdf/2408.16333v1 | 2024-08-29 | 2024-08-29 | [
"Sina Alemohammad",
"Ahmed Imtiaz Humayun",
"Shruti Agarwal",
"John Collomosse",
"Richard Baraniuk"
] | [
"",
"",
"",
"",
""
] | The artificial intelligence (AI) world is running out of real data for
training increasingly large generative models, resulting in accelerating
pressure to train on synthetic data. Unfortunately, training new generative
models with synthetic data from current or past generation models creates an
autophagous (self-consuming) loop that degrades the quality and/or diversity of
the synthetic data in what has been termed model autophagy disorder (MAD) and
model collapse. Current thinking around model autophagy recommends that
synthetic data is to be avoided for model training lest the system deteriorate
into MADness. In this paper, we take a different tack that treats synthetic
data differently from real data. Self-IMproving diffusion models with Synthetic
data (SIMS) is a new training concept for diffusion models that uses
self-synthesized data to provide negative guidance during the generation
process to steer a model's generative process away from the non-ideal synthetic
data manifold and towards the real data distribution. We demonstrate that SIMS
is capable of self-improvement; it establishes new records based on the
Fr\'echet inception distance (FID) metric for CIFAR-10 and ImageNet-64
generation and achieves competitive results on FFHQ-64 and ImageNet-512.
Moreover, SIMS is, to the best of our knowledge, the first prophylactic
generative AI algorithm that can be iteratively trained on self-generated
synthetic data without going MAD. As a bonus, SIMS can adjust a diffusion
model's synthetic data distribution to match any desired in-domain target
distribution to help mitigate biases and ensure fairness. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
Guided Reasoning: A Non-Technical Introduction | http://arxiv.org/abs/2408.16331v1 | http://arxiv.org/abs/2408.16331v1 | http://arxiv.org/pdf/2408.16331v1 | 2024-08-29 | 2024-08-29 | [
"Gregor Betz"
] | [
""
] | We introduce the concept and a default implementation of Guided Reasoning. A
multi-agent system is a Guided Reasoning system iff one agent (the guide)
primarily interacts with other agents in order to improve reasoning quality. We
describe Logikon's default implementation of Guided Reasoning in non-technical
terms. This is a living document we'll gradually enrich with more detailed
information and examples.
Code: https://github.com/logikon-ai/logikon | cs.AI | [
"cs.AI",
"cs.HC"
] |
|||
FA-YOLO: Research On Efficient Feature Selection YOLO Improved Algorithm
Based On FMDS and AGMF Modules | http://arxiv.org/abs/2408.16313v1 | http://arxiv.org/abs/2408.16313v1 | http://arxiv.org/pdf/2408.16313v1 | 2024-08-29 | 2024-08-29 | [
"Yukang Huo",
"Mingyuan Yao",
"Qingbin Tian",
"Tonghao Wang",
"Ruifeng Wang",
"Haihua Wang"
] | [
"",
"",
"",
"",
"",
""
] | Over the past few years, the YOLO series of models has emerged as one of the
dominant methodologies in the realm of object detection. Many studies have
advanced these baseline models by modifying their architectures, enhancing data
quality, and developing new loss functions. However, current models still
exhibit deficiencies in processing feature maps, such as overlooking the fusion
of cross-scale features and a static fusion approach that lacks the capability
for dynamic feature adjustment. To address these issues, this paper introduces
an efficient Fine-grained Multi-scale Dynamic Selection Module (FMDS Module),
which applies a more effective dynamic feature selection and fusion method on
fine-grained multi-scale feature maps, significantly enhancing the detection
accuracy of small, medium, and large-sized targets in complex environments.
Furthermore, this paper proposes an Adaptive Gated Multi-branch Focus Fusion
Module (AGMF Module), which utilizes multiple parallel branches to perform
complementary fusion of various features captured by the gated unit branch,
FMDS Module branch, and TripletAttention branch. This approach further enhances
the comprehensiveness, diversity, and integrity of feature fusion. This paper
has integrated the FMDS Module, AGMF Module, into Yolov9 to develop a novel
object detection model named FA-YOLO. Extensive experimental results show that
under identical experimental conditions, FA-YOLO achieves an outstanding 66.1%
mean Average Precision (mAP) on the PASCAL VOC 2007 dataset, representing 1.0%
improvement over YOLOv9's 65.1%. Additionally, the detection accuracies of
FA-YOLO for small, medium, and large targets are 44.1%, 54.6%, and 70.8%,
respectively, showing improvements of 2.0%, 3.1%, and 0.9% compared to YOLOv9's
42.1%, 51.5%, and 69.9%. | 11 pages and 4 figures | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
Safe Bayesian Optimization for High-Dimensional Control Systems via
Additive Gaussian Processes | http://arxiv.org/abs/2408.16307v1 | http://arxiv.org/abs/2408.16307v1 | http://arxiv.org/pdf/2408.16307v1 | 2024-08-29 | 2024-08-29 | [
"Hongxuan Wang",
"Xiaocong Li",
"Adrish Bhaumik",
"Prahlad Vadakkepat"
] | [
"",
"",
"",
""
] | Controller tuning and optimization have been among the most fundamental
problems in robotics and mechatronic systems. The traditional methodology is
usually model-based, but its performance heavily relies on an accurate
mathematical model of the system. In control applications with complex
dynamics, obtaining a precise model is often challenging, leading us towards a
data-driven approach. While optimizing a single controller has been explored by
various researchers, it remains a challenge to obtain the optimal controller
parameters safely and efficiently when multiple controllers are involved. In
this paper, we propose a high-dimensional safe Bayesian optimization method
based on additive Gaussian processes to optimize multiple controllers
simultaneously and safely. Additive Gaussian kernels replace the traditional
squared-exponential kernels or Mat\'ern kernels, enhancing the efficiency with
which Gaussian processes update information on unknown functions. Experimental
results on a permanent magnet synchronous motor (PMSM) demonstrate that
compared to existing safe Bayesian optimization algorithms, our method can
obtain optimal parameters more efficiently while ensuring safety. | cs.RO | [
"cs.RO",
"cs.AI"
] |
|||
Physics of Language Models: Part 2.2, How to Learn From Mistakes on
Grade-School Math Problems | http://arxiv.org/abs/2408.16293v1 | http://arxiv.org/abs/2408.16293v1 | http://arxiv.org/pdf/2408.16293v1 | 2024-08-29 | 2024-08-29 | [
"Tian Ye",
"Zicheng Xu",
"Yuanzhi Li",
"Zeyuan Allen-Zhu"
] | [
"",
"",
"",
""
] | Language models have demonstrated remarkable performance in solving reasoning
tasks; however, even the strongest models still occasionally make reasoning
mistakes. Recently, there has been active research aimed at improving reasoning
accuracy, particularly by using pretrained language models to "self-correct"
their mistakes via multi-round prompting. In this paper, we follow this line of
work but focus on understanding the usefulness of incorporating
"error-correction" data directly into the pretraining stage. This data consists
of erroneous solution steps immediately followed by their corrections. Using a
synthetic math dataset, we show promising results: this type of pretrain data
can help language models achieve higher reasoning accuracy directly (i.e.,
through simple auto-regression, without multi-round prompting) compared to
pretraining on the same amount of error-free data. We also delve into many
details, such as (1) how this approach differs from beam search, (2) how such
data can be prepared, (3) whether masking is needed on the erroneous tokens,
(4) the amount of error required, (5) whether such data can be deferred to the
fine-tuning stage, and many others. | arXiv admin note: text overlap with arXiv:2407.20311 | cs.CL | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
||
OpenFGL: A Comprehensive Benchmarks for Federated Graph Learning | http://arxiv.org/abs/2408.16288v1 | http://arxiv.org/abs/2408.16288v1 | http://arxiv.org/pdf/2408.16288v1 | 2024-08-29 | 2024-08-29 | [
"Xunkai Li",
"Yinlin Zhu",
"Boyang Pang",
"Guochen Yan",
"Yeyu Yan",
"Zening Li",
"Zhengyu Wu",
"Wentao Zhang",
"Rong-Hua Li",
"Guoren Wang"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Federated graph learning (FGL) has emerged as a promising distributed
training paradigm for graph neural networks across multiple local systems
without direct data sharing. This approach is particularly beneficial in
privacy-sensitive scenarios and offers a new perspective on addressing
scalability challenges in large-scale graph learning. Despite the proliferation
of FGL, the diverse motivations from practical applications, spanning various
research backgrounds and experimental settings, pose a significant challenge to
fair evaluation. To fill this gap, we propose OpenFGL, a unified benchmark
designed for the primary FGL scenarios: Graph-FL and Subgraph-FL. Specifically,
OpenFGL includes 38 graph datasets from 16 application domains, 8 federated
data simulation strategies that emphasize graph properties, and 5 graph-based
downstream tasks. Additionally, it offers 18 recently proposed SOTA FGL
algorithms through a user-friendly API, enabling a thorough comparison and
comprehensive evaluation of their effectiveness, robustness, and efficiency.
Empirical results demonstrate the ability of FGL while also revealing its
potential limitations, offering valuable insights for future exploration in
this thriving field. | Under Review | cs.LG | [
"cs.LG",
"cs.AI",
"cs.DB",
"cs.SI"
] |
||
Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal
Grounding | http://arxiv.org/abs/2408.16272v1 | http://arxiv.org/abs/2408.16272v1 | http://arxiv.org/pdf/2408.16272v1 | 2024-08-29 | 2024-08-29 | [
"Kaijing Ma",
"Haojian Huang",
"Jin Chen",
"Haodong Chen",
"Pengliang Ji",
"Xianghao Zang",
"Han Fang",
"Chao Ban",
"Hao Sun",
"Mulin Chen",
"Xuelong Li"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Existing Video Temporal Grounding (VTG) models excel in accuracy but often
overlook open-world challenges posed by open-vocabulary queries and untrimmed
videos. This leads to unreliable predictions for noisy, corrupted, and
out-of-distribution data. Adapting VTG models to dynamically estimate
uncertainties based on user input can address this issue. To this end, we
introduce SRAM, a robust network module that benefits from a two-stage
cross-modal alignment task. More importantly, it integrates Deep Evidential
Regression (DER) to explicitly and thoroughly quantify uncertainty during
training, thus allowing the model to say "I do not know" in scenarios beyond
its handling capacity. However, the direct application of traditional DER
theory and its regularizer reveals structural flaws, leading to unintended
constraints in VTG tasks. In response, we develop a simple yet effective
Geom-regularizer that enhances the uncertainty learning framework from the
ground up. To the best of our knowledge, this marks the first successful
attempt of DER in VTG. Our extensive quantitative and qualitative results
affirm the effectiveness, robustness, and interpretability of our modules and
the uncertainty learning paradigm in VTG tasks. The code will be made
available. | Ongoing work: 28pages, 19 figures, 7 tables. Code is available at:
https://kaijing.space/SRAM/ | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
LoraMap: Harnessing the Power of LoRA Connections | http://arxiv.org/abs/2408.16264v1 | http://arxiv.org/abs/2408.16264v1 | http://arxiv.org/pdf/2408.16264v1 | 2024-08-29 | 2024-08-29 | [
"Hyeryun Park",
"Jeongwon Kwak",
"Dongsuk Jang",
"Sumin Park",
"Jinwook Choi"
] | [
"",
"",
"",
"",
""
] | Large Language Models (LLMs) can benefit from mitigating hallucinations
through fact-checking and overcoming substantial computational overhead with
parameter-efficient techniques such as Low-Rank Adaptation (LoRA). While some
studies have explored the parallel integration of multiple LoRAs, these
approaches need attention to the connections between them. This paper
investigates methods to establish connections among multiple LoRAs. We create
three reasoning datasets tailored to fact-checking and fine-tune individual
LoRAs, allowing them to view and reason from diverse perspectives. Then, we
explore strategies for allocating these reasoning LoRAs and introduce LoraMap,
an approach to map connections between them. The results on the fact-checking
task demonstrate that the performance of LoraMap is superior to LoraHub, an
existing LoRA composition method. LoraMap also outperforms with significantly
fewer parameters than LoraConcat, which concatenates LoRAs and further
fine-tunes them. | 13 pages, 9 figures, 5 tables | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep
State Space Models | http://arxiv.org/abs/2408.16261v1 | http://arxiv.org/abs/2408.16261v1 | http://arxiv.org/pdf/2408.16261v1 | 2024-08-29 | 2024-08-29 | [
"Sekitoshi Kanai",
"Yasutoshi Ida",
"Kazuki Adachi",
"Mihiro Uchida",
"Tsukasa Yoshida",
"Shin'ya Yamaguchi"
] | [
"",
"",
"",
"",
"",
""
] | This study investigates a method to evaluate time-series datasets in terms of
the performance of deep neural networks (DNNs) with state space models (deep
SSMs) trained on the dataset. SSMs have attracted attention as components
inside DNNs to address time-series data. Since deep SSMs have powerful
representation capacities, training datasets play a crucial role in solving a
new task. However, the effectiveness of training datasets cannot be known until
deep SSMs are actually trained on them. This can increase the cost of data
collection for new tasks, as a trial-and-error process of data collection and
time-consuming training are needed to achieve the necessary performance. To
advance the practical use of deep SSMs, the metric of datasets to estimate the
performance early in the training can be one key element. To this end, we
introduce the concept of data evaluation methods used in system identification.
In system identification of linear dynamical systems, the effectiveness of
datasets is evaluated by using the spectrum of input signals. We introduce this
concept to deep SSMs, which are nonlinear dynamical systems. We propose the
K-spectral metric, which is the sum of the top-K spectra of signals inside deep
SSMs, by focusing on the fact that each layer of a deep SSM can be regarded as
a linear dynamical system. Our experiments show that the K-spectral metric has
a large absolute value of the correlation coefficient with the performance and
can be used to evaluate the quality of training datasets. | 11 pages, 5 figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Coalitions of AI-based Methods Predict 15-Year Risks of Breast Cancer
Metastasis Using Real-World Clinical Data with AUC up to 0.9 | http://arxiv.org/abs/2408.16256v1 | http://arxiv.org/abs/2408.16256v1 | http://arxiv.org/pdf/2408.16256v1 | 2024-08-29 | 2024-08-29 | [
"Xia Jiang",
"Yijun Zhou",
"Alan Wells",
"Adam Brufsky"
] | [
"",
"",
"",
""
] | Breast cancer is one of the two cancers responsible for the most deaths in
women, with about 42,000 deaths each year in the US. That there are over
300,000 breast cancers newly diagnosed each year suggests that only a fraction
of the cancers result in mortality. Thus, most of the women undergo seemingly
curative treatment for localized cancers, but a significant later succumb to
metastatic disease for which current treatments are only temporizing for the
vast majority. The current prognostic metrics are of little actionable value
for 4 of the 5 women seemingly cured after local treatment, and many women are
exposed to morbid and even mortal adjuvant therapies unnecessarily, with these
adjuvant therapies reducing metastatic recurrence by only a third. Thus, there
is a need for better prognostics to target aggressive treatment at those who
are likely to relapse and spare those who were actually cured. While there is a
plethora of molecular and tumor-marker assays in use and under-development to
detect recurrence early, these are time consuming, expensive and still often
un-validated as to actionable prognostic utility. A different approach would
use large data techniques to determine clinical and histopathological
parameters that would provide accurate prognostics using existing data. Herein,
we report on machine learning, together with grid search and Bayesian Networks
to develop algorithms that present a AUC of up to 0.9 in ROC analyses, using
only extant data. Such algorithms could be rapidly translated to clinical
management as they do not require testing beyond routine tumor evaluations. | cs.LG | [
"cs.LG",
"cs.AI",
"cs.NE",
"q-bio.QM"
] |
|||
Enhancing Conditional Image Generation with Explainable Latent Space
Manipulation | http://arxiv.org/abs/2408.16232v1 | http://arxiv.org/abs/2408.16232v1 | http://arxiv.org/pdf/2408.16232v1 | 2024-08-29 | 2024-08-29 | [
"Kshitij Pathania"
] | [
""
] | In the realm of image synthesis, achieving fidelity to a reference image
while adhering to conditional prompts remains a significant challenge. This
paper proposes a novel approach that integrates a diffusion model with latent
space manipulation and gradient-based selective attention mechanisms to address
this issue. Leveraging Grad-SAM (Gradient-based Selective Attention
Manipulation), we analyze the cross attention maps of the cross attention
layers and gradients for the denoised latent vector, deriving importance scores
of elements of denoised latent vector related to the subject of interest. Using
this information, we create masks at specific timesteps during denoising to
preserve subjects while seamlessly integrating the reference image features.
This approach ensures the faithful formation of subjects based on conditional
prompts, while concurrently refining the background for a more coherent
composition. Our experiments on places365 dataset demonstrate promising
results, with our proposed model achieving the lowest mean and median Frechet
Inception Distance (FID) scores compared to baseline models, indicating
superior fidelity preservation. Furthermore, our model exhibits competitive
performance in aligning the generated images with provided textual
descriptions, as evidenced by high CLIP scores. These results highlight the
effectiveness of our approach in both fidelity preservation and textual context
preservation, offering a significant advancement in text-to-image synthesis
tasks. | 7 pages , 5 figures | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG",
"26B10, 53A35,",
"I.2.10; I.4.10"
] |
||
Anchor-Controlled Generative Adversarial Network for High-Fidelity
Electromagnetic and Structurally Diverse Metasurface Design | http://arxiv.org/abs/2408.16231v1 | http://arxiv.org/abs/2408.16231v1 | http://arxiv.org/pdf/2408.16231v1 | 2024-08-29 | 2024-08-29 | [
"Yunhui Zeng",
"Hongkun Cao",
"Xin Jin"
] | [
"",
"",
""
] | In optoelectronics, designing free-form metasurfaces presents significant
challenges, particularly in achieving high electromagnetic response fidelity
due to the complex relationship between physical structures and electromagnetic
behaviors. A key difficulty arises from the one-to-many mapping dilemma, where
multiple distinct physical structures can yield similar electromagnetic
responses, complicating the design process. This paper introduces a novel
generative framework, the Anchor-controlled Generative Adversarial Network
(AcGAN), which prioritizes electromagnetic fidelity while effectively
navigating the one-to-many challenge to create structurally diverse
metasurfaces. Unlike existing methods that mainly replicate physical
appearances, AcGAN excels in generating a variety of structures that, despite
their differences in physical attributes, exhibit similar electromagnetic
responses, thereby accommodating fabrication constraints and tolerances. We
introduce the Spectral Overlap Coefficient (SOC) as a precise metric to measure
the spectral fidelity between generated designs and their targets.
Additionally, a cluster-guided controller refines input processing, ensuring
multi-level spectral integration and enhancing electromagnetic fidelity. The
integration of AnchorNet into our loss function facilitates a nuanced
assessment of electromagnetic qualities, supported by a dynamic loss weighting
strategy that optimizes spectral alignment. Collectively, these innovations
represent a transformative stride in metasurface inverse design, advancing
electromagnetic response-oriented engineering and overcoming the complexities
of the one-to-many mapping dilemma.Empirical evidence underscores AcGAN's
effectiveness in streamlining the design process, achieving superior
electromagnetic precision, and fostering a broad spectrum of design
possibilities. | physics.optics | [
"physics.optics",
"cs.AI",
"physics.app-ph"
] |
|||
LLaVA-SG: Leveraging Scene Graphs as Visual Semantic Expression in
Vision-Language Models | http://arxiv.org/abs/2408.16224v1 | http://arxiv.org/abs/2408.16224v1 | http://arxiv.org/pdf/2408.16224v1 | 2024-08-29 | 2024-08-29 | [
"Jingyi Wang",
"Jianzhong Ju",
"Jian Luan",
"Zhidong Deng"
] | [
"",
"",
"",
""
] | Recent advances in large vision-language models (VLMs) typically employ
vision encoders based on the Vision Transformer (ViT) architecture. The
division of the images into patches by ViT results in a fragmented perception,
thereby hindering the visual understanding capabilities of VLMs. In this paper,
we propose an innovative enhancement to address this limitation by introducing
a Scene Graph Expression (SGE) module in VLMs. This module extracts and
structurally expresses the complex semantic information within images, thereby
improving the foundational perception and understanding abilities of VLMs.
Extensive experiments demonstrate that integrating our SGE module significantly
enhances the VLM's performance in vision-language tasks, indicating its
effectiveness in preserving intricate semantic details and facilitating better
visual understanding. Code and data would be available. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
SSDM: Scalable Speech Dysfluency Modeling | http://arxiv.org/abs/2408.16221v1 | http://arxiv.org/abs/2408.16221v1 | http://arxiv.org/pdf/2408.16221v1 | 2024-08-29 | 2024-08-29 | [
"Jiachen Lian",
"Xuanru Zhou",
"Zoe Ezzes",
"Jet Vonk",
"Brittany Morin",
"David Baquirin",
"Zachary Mille",
"Maria Luisa Gorno Tempini",
"Gopala Anumanchipalli"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Speech dysfluency modeling is the core module for spoken language learning,
and speech therapy. However, there are three challenges. First, current
state-of-the-art solutions suffer from poor scalability. Second, there is a
lack of a large-scale dysfluency corpus. Third, there is not an effective
learning framework. In this paper, we propose \textit{SSDM: Scalable Speech
Dysfluency Modeling}, which (1) adopts articulatory gestures as scalable forced
alignment; (2) introduces connectionist subsequence aligner (CSA) to achieve
dysfluency alignment; (3) introduces a large-scale simulated dysfluency corpus
called Libri-Dys; and (4) develops an end-to-end system by leveraging the power
of large language models (LLMs). We expect SSDM to serve as a standard in the
area of dysfluency modeling. Demo is available at
\url{https://eureka235.github.io}. | eess.AS | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.SD"
] |
|||
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language
Models for Chest X-ray Interpretation | http://arxiv.org/abs/2408.16213v1 | http://arxiv.org/abs/2408.16213v1 | http://arxiv.org/pdf/2408.16213v1 | 2024-08-29 | 2024-08-29 | [
"Jonggwon Park",
"Soobum Kim",
"Byungmu Yoon",
"Jihun Hyun",
"Kyoyun Choi"
] | [
"",
"",
"",
"",
""
] | The rapid evolution of artificial intelligence, especially in large language
models (LLMs), has significantly impacted various domains, including
healthcare. In chest X-ray (CXR) analysis, previous studies have employed LLMs,
but with limitations: either underutilizing the multi-tasking capabilities of
LLMs or lacking clinical accuracy. This paper presents M4CXR, a multi-modal LLM
designed to enhance CXR interpretation. The model is trained on a visual
instruction-following dataset that integrates various task-specific datasets in
a conversational format. As a result, the model supports multiple tasks such as
medical report generation (MRG), visual grounding, and visual question
answering (VQA). M4CXR achieves state-of-the-art clinical accuracy in MRG by
employing a chain-of-thought prompting strategy, in which it identifies
findings in CXR images and subsequently generates corresponding reports. The
model is adaptable to various MRG scenarios depending on the available inputs,
such as single-image, multi-image, and multi-study contexts. In addition to
MRG, M4CXR performs visual grounding at a level comparable to specialized
models and also demonstrates outstanding performance in VQA. Both quantitative
and qualitative assessments reveal M4CXR's versatility in MRG, visual
grounding, and VQA, while consistently maintaining clinical accuracy. | cs.CV | [
"cs.CV",
"cs.AI",
"cs.CL"
] |
|||
Short-Term Electricity-Load Forecasting by Deep Learning: A
Comprehensive Survey | http://arxiv.org/abs/2408.16202v1 | http://arxiv.org/abs/2408.16202v1 | http://arxiv.org/pdf/2408.16202v1 | 2024-08-29 | 2024-08-29 | [
"Qi Dong",
"Rubing Huang",
"Chenhui Cui",
"Dave Towey",
"Ling Zhou",
"Jinyu Tian",
"Jianzhou Wang"
] | [
"",
"",
"",
"",
"",
"",
""
] | Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of
the immediate demand (in the next few hours to several days) for the power
system. Various external factors, such as weather changes and the emergence of
new electricity consumption scenarios, can impact electricity demand, causing
load data to fluctuate and become non-linear, which increases the complexity
and difficulty of STELF. In the past decade, deep learning has been applied to
STELF, modeling and predicting electricity demand with high accuracy, and
contributing significantly to the development of STELF. This paper provides a
comprehensive survey on deep-learning-based STELF over the past ten years. It
examines the entire forecasting process, including data pre-processing, feature
extraction, deep-learning modeling and optimization, and results evaluation.
This paper also identifies some research challenges and potential research
directions to be further investigated in future work. | cs.LG | [
"cs.LG",
"cs.AI"
] |
|||
PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object
Detection in Bird's-Eye-View | http://arxiv.org/abs/2408.16200v1 | http://arxiv.org/abs/2408.16200v1 | http://arxiv.org/pdf/2408.16200v1 | 2024-08-29 | 2024-08-29 | [
"Zichen Yu",
"Quanli Liu",
"Wei Wang",
"Liyong Zhang",
"Xiaoguang Zhao"
] | [
"",
"",
"",
"",
""
] | Recently, LSS-based multi-view 3D object detection provides an economical and
deployment-friendly solution for autonomous driving. However, all the existing
LSS-based methods transform multi-view image features into a Cartesian
Bird's-Eye-View(BEV) representation, which does not take into account the
non-uniform image information distribution and hardly exploits the view
symmetry. In this paper, in order to adapt the image information distribution
and preserve the view symmetry by regular convolution, we propose to employ the
polar BEV representation to substitute the Cartesian BEV representation. To
achieve this, we elaborately tailor three modules: a polar view transformer to
generate the polar BEV representation, a polar temporal fusion module for
fusing historical polar BEV features and a polar detection head to predict the
polar-parameterized representation of the object. In addition, we design a 2D
auxiliary detection head and a spatial attention enhancement module to improve
the quality of feature extraction in perspective view and BEV, respectively.
Finally, we integrate the above improvements into a novel multi-view 3D object
detector, PolarBEVDet. Experiments on nuScenes show that PolarBEVDet achieves
the superior performance. The code is available at
https://github.com/Yzichen/PolarBEVDet.git. | 11 pages, 6 figures | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
A More Unified Theory of Transfer Learning | http://arxiv.org/abs/2408.16189v1 | http://arxiv.org/abs/2408.16189v1 | http://arxiv.org/pdf/2408.16189v1 | 2024-08-29 | 2024-08-29 | [
"Steve Hanneke",
"Samory Kpotufe"
] | [
"",
""
] | We show that some basic moduli of continuity $\delta$ -- which measure how
fast target risk decreases as source risk decreases -- appear to be at the root
of many of the classical relatedness measures in transfer learning and related
literature. Namely, bounds in terms of $\delta$ recover many of the existing
bounds in terms of other measures of relatedness -- both in regression and
classification -- and can at times be tighter.
We are particularly interested in general situations where the learner has
access to both source data and some or no target data. The unified perspective
allowed by the moduli $\delta$ allow us to extend many existing notions of
relatedness at once to these scenarios involving target data: interestingly,
while $\delta$ itself might not be efficiently estimated, adaptive procedures
exist -- based on reductions to confidence sets -- which can get nearly tight
rates in terms of $\delta$ with no prior distributional knowledge. Such
adaptivity to unknown $\delta$ immediately implies adaptivity to many classical
relatedness notions, in terms of combined source and target samples' sizes. | stat.ML | [
"stat.ML",
"cs.AI",
"cs.LG",
"math.ST",
"stat.TH"
] |
|||
Real-Time Energy Pricing in New Zealand: An Evolving Stream Analysis | http://arxiv.org/abs/2408.16187v1 | http://arxiv.org/abs/2408.16187v1 | http://arxiv.org/pdf/2408.16187v1 | 2024-08-29 | 2024-08-29 | [
"Yibin Sun",
"Heitor Murilo Gomes",
"Bernhard Pfahringer",
"Albert Bifet"
] | [
"",
"",
"",
""
] | This paper introduces a group of novel datasets representing real-time
time-series and streaming data of energy prices in New Zealand, sourced from
the Electricity Market Information (EMI) website maintained by the New Zealand
government. The datasets are intended to address the scarcity of proper
datasets for streaming regression learning tasks. We conduct extensive analyses
and experiments on these datasets, covering preprocessing techniques,
regression tasks, prediction intervals, concept drift detection, and anomaly
detection. Our experiments demonstrate the datasets' utility and highlight the
challenges and opportunities for future research in energy price forecasting. | 12 Pages, 8 figures, short version accepted by PRICAI | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
LLM-assisted Labeling Function Generation for Semantic Type Detection | http://arxiv.org/abs/2408.16173v1 | http://arxiv.org/abs/2408.16173v1 | http://arxiv.org/pdf/2408.16173v1 | 2024-08-28 | 2024-08-28 | [
"Chenjie Li",
"Dan Zhang",
"Jin Wang"
] | [
"",
"",
""
] | Detecting semantic types of columns in data lake tables is an important
application. A key bottleneck in semantic type detection is the availability of
human annotation due to the inherent complexity of data lakes. In this paper,
we propose using programmatic weak supervision to assist in annotating the
training data for semantic type detection by leveraging labeling functions. One
challenge in this process is the difficulty of manually writing labeling
functions due to the large volume and low quality of the data lake table
datasets. To address this issue, we explore employing Large Language Models
(LLMs) for labeling function generation and introduce several prompt
engineering strategies for this purpose. We conduct experiments on real-world
web table datasets. Based on the initial results, we perform extensive analysis
and provide empirical insights and future directions for researchers in this
field. | VLDB'24-DATAI | cs.DB | [
"cs.DB",
"cs.AI"
] |
||
Simulating realistic short tandem repeat capillary electrophoretic
signal using a generative adversarial network | http://arxiv.org/abs/2408.16169v1 | http://arxiv.org/abs/2408.16169v1 | http://arxiv.org/pdf/2408.16169v1 | 2024-08-28 | 2024-08-28 | [
"Duncan Taylor",
"Melissa Humphries"
] | [
"",
""
] | DNA profiles are made up from multiple series of electrophoretic signal
measuring fluorescence over time. Typically, human DNA analysts 'read' DNA
profiles using their experience to distinguish instrument noise, artefactual
signal, and signal corresponding to DNA fragments of interest. Recent work has
developed an artificial neural network, ANN, to carry out the task of
classifying fluorescence types into categories in DNA profile electrophoretic
signal. But the creation of the necessarily large amount of labelled training
data for the ANN is time consuming and expensive, and a limiting factor in the
ability to robustly train the ANN. If realistic, prelabelled, training data
could be simulated then this would remove the barrier to training an ANN with
high efficacy. Here we develop a generative adversarial network, GAN, modified
from the pix2pix GAN to achieve this task. With 1078 DNA profiles we train the
GAN and achieve the ability to simulate DNA profile information, and then use
the generator from the GAN as a 'realism filter' that applies the noise and
artefact elements exhibited in typical electrophoretic signal. | 29 pages, 9 Figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
FRACTURED-SORRY-Bench: Framework for Revealing Attacks in Conversational
Turns Undermining Refusal Efficacy and Defenses over SORRY-Bench | http://arxiv.org/abs/2408.16163v1 | http://arxiv.org/abs/2408.16163v1 | http://arxiv.org/pdf/2408.16163v1 | 2024-08-28 | 2024-08-28 | [
"Aman Priyanshu",
"Supriti Vijay"
] | [
"",
""
] | This paper introduces FRACTURED-SORRY-Bench, a framework for evaluating the
safety of Large Language Models (LLMs) against multi-turn conversational
attacks. Building upon the SORRY-Bench dataset, we propose a simple yet
effective method for generating adversarial prompts by breaking down harmful
queries into seemingly innocuous sub-questions. Our approach achieves a maximum
increase of +46.22\% in Attack Success Rates (ASRs) across GPT-4, GPT-4o,
GPT-4o-mini, and GPT-3.5-Turbo models compared to baseline methods. We
demonstrate that this technique poses a challenge to current LLM safety
measures and highlights the need for more robust defenses against subtle,
multi-turn attacks. | 4 pages, 2 tables | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
Improving Generalization of Speech Separation in Real-World Scenarios:
Strategies in Simulation, Optimization, and Evaluation | http://arxiv.org/abs/2408.16126v1 | http://arxiv.org/abs/2408.16126v1 | http://arxiv.org/pdf/2408.16126v1 | 2024-08-28 | 2024-08-28 | [
"Ke Chen",
"Jiaqi Su",
"Taylor Berg-Kirkpatrick",
"Shlomo Dubnov",
"Zeyu Jin"
] | [
"",
"",
"",
"",
""
] | Achieving robust speech separation for overlapping speakers in various
acoustic environments with noise and reverberation remains an open challenge.
Although existing datasets are available to train separators for specific
scenarios, they do not effectively generalize across diverse real-world
scenarios. In this paper, we present a novel data simulation pipeline that
produces diverse training data from a range of acoustic environments and
content, and propose new training paradigms to improve quality of a general
speech separation model. Specifically, we first introduce AC-SIM, a data
simulation pipeline that incorporates broad variations in both content and
acoustics. Then we integrate multiple training objectives into the permutation
invariant training (PIT) to enhance separation quality and generalization of
the trained model. Finally, we conduct comprehensive objective and human
listening experiments across separation architectures and benchmarks to
validate our methods, demonstrating substantial improvement of generalization
on both non-homologous and real-world test sets. | In Proceedings of the 25th Annual Conference of the International
Speech Communication Association, Interspeech 2024 | cs.SD | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] |
||
ChartEye: A Deep Learning Framework for Chart Information Extraction | http://arxiv.org/abs/2408.16123v1 | http://arxiv.org/abs/2408.16123v1 | http://arxiv.org/pdf/2408.16123v1 | 2024-08-28 | 2024-08-28 | [
"Osama Mustafa",
"Muhammad Khizer Ali",
"Momina Moetesum",
"Imran Siddiqi"
] | [
"",
"",
"",
""
] | The widespread use of charts and infographics as a means of data
visualization in various domains has inspired recent research in automated
chart understanding. However, information extraction from chart images is a
complex multitasked process due to style variations and, as a consequence, it
is challenging to design an end-to-end system. In this study, we propose a deep
learning-based framework that provides a solution for key steps in the chart
information extraction pipeline. The proposed framework utilizes hierarchal
vision transformers for the tasks of chart-type and text-role classification,
while YOLOv7 for text detection. The detected text is then enhanced using Super
Resolution Generative Adversarial Networks to improve the recognition output of
the OCR. Experimental results on a benchmark dataset show that our proposed
framework achieves excellent performance at every stage with F1-scores of 0.97
for chart-type classification, 0.91 for text-role classification, and a mean
Average Precision of 0.95 for text detection. | 8 Pages, and 11 Figures | 10.1109/DICTA60407.2023.00082 | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
|
Data Formulator 2: Iteratively Creating Rich Visualizations with AI | http://arxiv.org/abs/2408.16119v1 | http://arxiv.org/abs/2408.16119v1 | http://arxiv.org/pdf/2408.16119v1 | 2024-08-28 | 2024-08-28 | [
"Chenglong Wang",
"Bongshin Lee",
"Steven Drucker",
"Dan Marshall",
"Jianfeng Gao"
] | [
"",
"",
"",
"",
""
] | To create rich visualizations, data analysts often need to iterate back and
forth among data processing and chart specification to achieve their goals. To
achieve this, analysts need not only proficiency in data transformation and
visualization tools but also efforts to manage the branching history consisting
of many different versions of data and charts. Recent LLM-powered AI systems
have greatly improved visualization authoring experiences, for example by
mitigating manual data transformation barriers via LLMs' code generation
ability. However, these systems do not work well for iterative visualization
authoring, because they often require analysts to provide, in a single turn, a
text-only prompt that fully describes the complex visualization task to be
performed, which is unrealistic to both users and models in many cases. In this
paper, we present Data Formulator 2, an LLM-powered visualization system to
address these challenges. With Data Formulator 2, users describe their
visualization intent with blended UI and natural language inputs, and data
transformation are delegated to AI. To support iteration, Data Formulator 2
lets users navigate their iteration history and reuse previous designs towards
new ones so that they don't need to start from scratch every time. In a user
study with eight participants, we observed that Data Formulator 2 allows
participants to develop their own iteration strategies to complete challenging
data exploration sessions. | cs.HC | [
"cs.HC",
"cs.AI"
] |
|||
Logic-Enhanced Language Model Agents for Trustworthy Social Simulations | http://arxiv.org/abs/2408.16081v1 | http://arxiv.org/abs/2408.16081v1 | http://arxiv.org/pdf/2408.16081v1 | 2024-08-28 | 2024-08-28 | [
"Agnieszka Mensfelt",
"Kostas Stathis",
"Vince Trencsenyi"
] | [
"",
"",
""
] | We introduce the Logic-Enhanced Language Model Agents (LELMA) framework, a
novel approach to enhance the trustworthiness of social simulations that
utilize large language models (LLMs). While LLMs have gained attention as
agents for simulating human behaviour, their applicability in this role is
limited by issues such as inherent hallucinations and logical inconsistencies.
LELMA addresses these challenges by integrating LLMs with symbolic AI, enabling
logical verification of the reasoning generated by LLMs. This verification
process provides corrective feedback, refining the reasoning output. The
framework consists of three main components: an LLM-Reasoner for producing
strategic reasoning, an LLM-Translator for mapping natural language reasoning
to logic queries, and a Solver for evaluating these queries. This study focuses
on decision-making in game-theoretic scenarios as a model of human interaction.
Experiments involving the Hawk-Dove game, Prisoner's Dilemma, and Stag Hunt
highlight the limitations of state-of-the-art LLMs, GPT-4 Omni and Gemini 1.0
Pro, in producing correct reasoning in these contexts. LELMA demonstrates high
accuracy in error detection and improves the reasoning correctness of LLMs via
self-refinement, particularly in GPT-4 Omni. | Source code: https://github.com/dicelab-rhul/LELMA | cs.AI | [
"cs.AI",
"cs.CL",
"cs.GT",
"cs.LO"
] |
||
Verification methods for international AI agreements | http://arxiv.org/abs/2408.16074v1 | http://arxiv.org/abs/2408.16074v1 | http://arxiv.org/pdf/2408.16074v1 | 2024-08-28 | 2024-08-28 | [
"Akash R. Wasil",
"Tom Reed",
"Jack William Miller",
"Peter Barnett"
] | [
"",
"",
"",
""
] | What techniques can be used to verify compliance with international
agreements about advanced AI development? In this paper, we examine 10
verification methods that could detect two types of potential violations:
unauthorized AI training (e.g., training runs above a certain FLOP threshold)
and unauthorized data centers. We divide the verification methods into three
categories: (a) national technical means (methods requiring minimal or no
access from suspected non-compliant nations), (b) access-dependent methods
(methods that require approval from the nation suspected of unauthorized
activities), and (c) hardware-dependent methods (methods that require rules
around advanced hardware). For each verification method, we provide a
description, historical precedents, and possible evasion techniques. We
conclude by offering recommendations for future work related to the
verification and enforcement of international AI governance agreements. | cs.CY | [
"cs.CY",
"cs.AI"
] |
|||
Using Large Language Models to Create AI Personas for Replication and
Prediction of Media Effects: An Empirical Test of 133 Published Experimental
Research Findings | http://arxiv.org/abs/2408.16073v1 | http://arxiv.org/abs/2408.16073v1 | http://arxiv.org/pdf/2408.16073v1 | 2024-08-28 | 2024-08-28 | [
"Leo Yeykelis",
"Kaavya Pichai",
"James J. Cummings",
"Byron Reeves"
] | [
"",
"",
"",
""
] | This report analyzes the potential for large language models (LLMs) to
expedite accurate replication of published message effects studies. We tested
LLM-powered participants (personas) by replicating 133 experimental findings
from 14 papers containing 45 recent studies in the Journal of Marketing
(January 2023-May 2024). We used a new software tool, Viewpoints AI
(https://viewpoints.ai/), that takes study designs, stimuli, and measures as
input, automatically generates prompts for LLMs to act as a specified sample of
unique personas, and collects their responses to produce a final output in the
form of a complete dataset and statistical analysis. The underlying LLM used
was Anthropic's Claude Sonnet 3.5. We generated 19,447 AI personas to replicate
these studies with the exact same sample attributes, study designs, stimuli,
and measures reported in the original human research. Our LLM replications
successfully reproduced 76% of the original main effects (84 out of 111),
demonstrating strong potential for AI-assisted replication of studies in which
people respond to media stimuli. When including interaction effects, the
overall replication rate was 68% (90 out of 133). The use of LLMs to replicate
and accelerate marketing research on media effects is discussed with respect to
the replication crisis in social science, potential solutions to
generalizability problems in sampling subjects and experimental conditions, and
the ability to rapidly test consumer responses to various media stimuli. We
also address the limitations of this approach, particularly in replicating
complex interaction effects in media response studies, and suggest areas for
future research and improvement in AI-assisted experimental replication of
media effects. | 24 pages, 3 figures, 2 tables | cs.CL | [
"cs.CL",
"cs.AI"
] |
||
Identification of Prognostic Biomarkers for Stage III Non-Small Cell
Lung Carcinoma in Female Nonsmokers Using Machine Learning | http://arxiv.org/abs/2408.16068v1 | http://arxiv.org/abs/2408.16068v1 | http://arxiv.org/pdf/2408.16068v1 | 2024-08-28 | 2024-08-28 | [
"Huili Zheng",
"Qimin Zhang",
"Yiru Gong",
"Zheyan Liu",
"Shaohan Chen"
] | [
"",
"",
"",
"",
""
] | Lung cancer remains a leading cause of cancer-related deaths globally, with
non-small cell lung cancer (NSCLC) being the most common subtype. This study
aimed to identify key biomarkers associated with stage III NSCLC in non-smoking
females using gene expression profiling from the GDS3837 dataset. Utilizing
XGBoost, a machine learning algorithm, the analysis achieved a strong
predictive performance with an AUC score of 0.835. The top biomarkers
identified - CCAAT enhancer binding protein alpha (C/EBP-alpha), lactate
dehydrogenase A4 (LDHA), UNC-45 myosin chaperone B (UNC-45B), checkpoint kinase
1 (CHK1), and hypoxia-inducible factor 1 subunit alpha (HIF-1-alpha) - have
been validated in the literature as being significantly linked to lung cancer.
These findings highlight the potential of these biomarkers for early diagnosis
and personalized therapy, emphasizing the value of integrating machine learning
with molecular profiling in cancer research. | This paper has been accepted for publication in the IEEE ICBASE 2024
conference | q-bio.GN | [
"q-bio.GN",
"cs.AI",
"stat.ML"
] |
||
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of
Encoders | http://arxiv.org/abs/2408.15998v1 | http://arxiv.org/abs/2408.15998v1 | http://arxiv.org/pdf/2408.15998v1 | 2024-08-28 | 2024-08-28 | [
"Min Shi",
"Fuxiao Liu",
"Shihao Wang",
"Shijia Liao",
"Subhashree Radhakrishnan",
"De-An Huang",
"Hongxu Yin",
"Karan Sapra",
"Yaser Yacoob",
"Humphrey Shi",
"Bryan Catanzaro",
"Andrew Tao",
"Jan Kautz",
"Zhiding Yu",
"Guilin Liu"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | The ability to accurately interpret complex visual information is a crucial
topic of multimodal large language models (MLLMs). Recent work indicates that
enhanced visual perception significantly reduces hallucinations and improves
performance on resolution-sensitive tasks, such as optical character
recognition and document analysis. A number of recent MLLMs achieve this goal
using a mixture of vision encoders. Despite their success, there is a lack of
systematic comparisons and detailed ablation studies addressing critical
aspects, such as expert selection and the integration of multiple vision
experts. This study provides an extensive exploration of the design space for
MLLMs using a mixture of vision encoders and resolutions. Our findings reveal
several underlying principles common to various existing strategies, leading to
a streamlined yet effective design approach. We discover that simply
concatenating visual tokens from a set of complementary vision encoders is as
effective as more complex mixing architectures or strategies. We additionally
introduce Pre-Alignment to bridge the gap between vision-focused encoders and
language tokens, enhancing model coherence. The resulting family of MLLMs,
Eagle, surpasses other leading open-source models on major MLLM benchmarks.
Models and code: https://github.com/NVlabs/Eagle | Github: https://github.com/NVlabs/Eagle, HuggingFace:
https://huggingface.co/NVEagle | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] |
||
Mamba or Transformer for Time Series Forecasting? Mixture of Universals
(MoU) Is All You Need | http://arxiv.org/abs/2408.15997v1 | http://arxiv.org/abs/2408.15997v1 | http://arxiv.org/pdf/2408.15997v1 | 2024-08-28 | 2024-08-28 | [
"Sijia Peng",
"Yun Xiong",
"Yangyong Zhu",
"Zhiqiang Shen"
] | [
"",
"",
"",
""
] | Time series forecasting requires balancing short-term and long-term
dependencies for accurate predictions. Existing methods mainly focus on
long-term dependency modeling, neglecting the complexities of short-term
dynamics, which may hinder performance. Transformers are superior in modeling
long-term dependencies but are criticized for their quadratic computational
cost. Mamba provides a near-linear alternative but is reported less effective
in time series longterm forecasting due to potential information loss. Current
architectures fall short in offering both high efficiency and strong
performance for long-term dependency modeling. To address these challenges, we
introduce Mixture of Universals (MoU), a versatile model to capture both
short-term and long-term dependencies for enhancing performance in time series
forecasting. MoU is composed of two novel designs: Mixture of Feature
Extractors (MoF), an adaptive method designed to improve time series patch
representations for short-term dependency, and Mixture of Architectures (MoA),
which hierarchically integrates Mamba, FeedForward, Convolution, and
Self-Attention architectures in a specialized order to model long-term
dependency from a hybrid perspective. The proposed approach achieves
state-of-the-art performance while maintaining relatively low computational
costs. Extensive experiments on seven real-world datasets demonstrate the
superiority of MoU. Code is available at https://github.com/lunaaa95/mou/. | Code at https://github.com/lunaaa95/mou/ | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
Spatio-Temporal Context Prompting for Zero-Shot Action Detection | http://arxiv.org/abs/2408.15996v2 | http://arxiv.org/abs/2408.15996v2 | http://arxiv.org/pdf/2408.15996v2 | 2024-08-28 | 2024-08-29 | [
"Wei-Jhe Huang",
"Min-Hung Chen",
"Shang-Hong Lai"
] | [
"",
"",
""
] | Spatio-temporal action detection encompasses the tasks of localizing and
classifying individual actions within a video. Recent works aim to enhance this
process by incorporating interaction modeling, which captures the relationship
between people and their surrounding context. However, these approaches have
primarily focused on fully-supervised learning, and the current limitation lies
in the lack of generalization capability to recognize unseen action categories.
In this paper, we aim to adapt the pretrained image-language models to detect
unseen actions. To this end, we propose a method which can effectively leverage
the rich knowledge of visual-language models to perform Person-Context
Interaction. Meanwhile, our Context Prompting module will utilize contextual
information to prompt labels, thereby enhancing the generation of more
representative text features. Moreover, to address the challenge of recognizing
distinct actions by multiple people at the same timestamp, we design the
Interest Token Spotting mechanism which employs pretrained visual knowledge to
find each person's interest context tokens, and then these tokens will be used
for prompting to generate text features tailored to each individual. To
evaluate the ability to detect unseen actions, we propose a comprehensive
benchmark on J-HMDB, UCF101-24, and AVA datasets. The experiments show that our
method achieves superior results compared to previous approaches and can be
further extended to multi-action videos, bringing it closer to real-world
applications. The code and data can be found in
https://webber2933.github.io/ST-CLIP-project-page. | Project page: https://webber2933.github.io/ST-CLIP-project-page | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
CoGen: Learning from Feedback with Coupled Comprehension and Generation | http://arxiv.org/abs/2408.15992v1 | http://arxiv.org/abs/2408.15992v1 | http://arxiv.org/pdf/2408.15992v1 | 2024-08-28 | 2024-08-28 | [
"Mustafa Omer Gul",
"Yoav Artzi"
] | [
"",
""
] | Systems with both language comprehension and generation capabilities can
benefit from the tight connection between the two. This work studies coupling
comprehension and generation with focus on continually learning from
interaction with users. We propose techniques to tightly integrate the two
capabilities for both learning and inference. We situate our studies in
two-player reference games, and deploy various models for thousands of
interactions with human users, while learning from interaction feedback
signals. We show dramatic improvements in performance over time, with
comprehension-generation coupling leading to performance improvements up to 26%
in absolute terms and up to 17% higher accuracies compared to a non-coupled
system. Our analysis also shows coupling has substantial qualitative impact on
the system's language, making it significantly more human-like. | 17 pages, 9 figures | cs.CL | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] |
||
In-Context Imitation Learning via Next-Token Prediction | http://arxiv.org/abs/2408.15980v1 | http://arxiv.org/abs/2408.15980v1 | http://arxiv.org/pdf/2408.15980v1 | 2024-08-28 | 2024-08-28 | [
"Letian Fu",
"Huang Huang",
"Gaurav Datta",
"Lawrence Yunliang Chen",
"William Chung-Ho Panitch",
"Fangchen Liu",
"Hui Li",
"Ken Goldberg"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | We explore how to enhance next-token prediction models to perform in-context
imitation learning on a real robot, where the robot executes new tasks by
interpreting contextual information provided during the input phase, without
updating its underlying policy parameters. We propose In-Context Robot
Transformer (ICRT), a causal transformer that performs autoregressive
prediction on sensorimotor trajectories without relying on any linguistic data
or reward function. This formulation enables flexible and training-free
execution of new tasks at test time, achieved by prompting the model with
sensorimotor trajectories of the new task composing of image observations,
actions and states tuples, collected through human teleoperation. Experiments
with a Franka Emika robot demonstrate that the ICRT can adapt to new tasks
specified by prompts, even in environment configurations that differ from both
the prompt and the training data. In a multitask environment setup, ICRT
significantly outperforms current state-of-the-art next-token prediction models
in robotics on generalizing to unseen tasks. Code, checkpoints and data are
available on https://icrt.dev/ | cs.RO | [
"cs.RO",
"cs.AI"
] |
|||
WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task
Execution with Strategic Exploration | http://arxiv.org/abs/2408.15978v1 | http://arxiv.org/abs/2408.15978v1 | http://arxiv.org/pdf/2408.15978v1 | 2024-08-28 | 2024-08-28 | [
"Yao Zhang",
"Zijian Ma",
"Yunpu Ma",
"Zhen Han",
"Yu Wu",
"Volker Tresp"
] | [
"",
"",
"",
"",
"",
""
] | LLM-based autonomous agents often fail to execute complex web tasks that
require dynamic interaction due to the inherent uncertainty and complexity of
these environments. Existing LLM-based web agents typically rely on rigid,
expert-designed policies specific to certain states and actions, which lack the
flexibility and generalizability needed to adapt to unseen tasks. In contrast,
humans excel by exploring unknowns, continuously adapting strategies, and
resolving ambiguities through exploration. To emulate human-like adaptability,
web agents need strategic exploration and complex decision-making. Monte Carlo
Tree Search (MCTS) is well-suited for this, but classical MCTS struggles with
vast action spaces, unpredictable state transitions, and incomplete information
in web tasks. In light of this, we develop WebPilot, a multi-agent system with
a dual optimization strategy that improves MCTS to better handle complex web
environments. Specifically, the Global Optimization phase involves generating a
high-level plan by breaking down tasks into manageable subtasks and
continuously refining this plan, thereby focusing the search process and
mitigating the challenges posed by vast action spaces in classical MCTS.
Subsequently, the Local Optimization phase executes each subtask using a
tailored MCTS designed for complex environments, effectively addressing
uncertainties and managing incomplete information. Experimental results on
WebArena and MiniWoB++ demonstrate the effectiveness of WebPilot. Notably, on
WebArena, WebPilot achieves SOTA performance with GPT-4, achieving a 93%
relative increase in success rate over the concurrent tree search-based method.
WebPilot marks a significant advancement in general autonomous agent
capabilities, paving the way for more advanced and reliable decision-making in
practical environments. | cs.AI | [
"cs.AI"
] |
|||
Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex
Optimization Problems | http://arxiv.org/abs/2408.15969v1 | http://arxiv.org/abs/2408.15969v1 | http://arxiv.org/pdf/2408.15969v1 | 2024-08-28 | 2024-08-28 | [
"Ibrahim K. Ozaslan",
"Panagiotis Patrinos",
"Mihailo R. Jovanović"
] | [
"",
"",
""
] | We examine stability properties of primal-dual gradient flow dynamics for
composite convex optimization problems with multiple, possibly nonsmooth, terms
in the objective function under the generalized consensus constraint. The
proposed dynamics are based on the proximal augmented Lagrangian and they
provide a viable alternative to ADMM which faces significant challenges from
both analysis and implementation viewpoints in large-scale multi-block
scenarios. In contrast to customized algorithms with individualized convergence
guarantees, we provide a systematic approach for solving a broad class of
challenging composite optimization problems. We leverage various structural
properties to establish global (exponential) convergence guarantees for the
proposed dynamics. Our assumptions are much weaker than those required to prove
(exponential) stability of various primal-dual dynamics as well as (linear)
convergence of discrete-time methods, e.g., standard two-block and multi-block
ADMM and EXTRA algorithms. Finally, we show necessity of some of our structural
assumptions for exponential stability and provide computational experiments to
demonstrate the convenience of the proposed dynamics for parallel and
distributed computing applications. | 31 pages; 4 figures | math.OC | [
"math.OC",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] |
||
More Text, Less Point: Towards 3D Data-Efficient Point-Language
Understanding | http://arxiv.org/abs/2408.15966v1 | http://arxiv.org/abs/2408.15966v1 | http://arxiv.org/pdf/2408.15966v1 | 2024-08-28 | 2024-08-28 | [
"Yuan Tang",
"Xu Han",
"Xianzhi Li",
"Qiao Yu",
"Jinfeng Xu",
"Yixue Hao",
"Long Hu",
"Min Chen"
] | [
"",
"",
"",
"",
"",
"",
"",
""
] | Enabling Large Language Models (LLMs) to comprehend the 3D physical world
remains a significant challenge. Due to the lack of large-scale 3D-text pair
datasets, the success of LLMs has yet to be replicated in 3D understanding. In
this paper, we rethink this issue and propose a new task: 3D Data-Efficient
Point-Language Understanding. The goal is to enable LLMs to achieve robust 3D
object understanding with minimal 3D point cloud and text data pairs. To
address this task, we introduce GreenPLM, which leverages more text data to
compensate for the lack of 3D data. First, inspired by using CLIP to align
images and text, we utilize a pre-trained point cloud-text encoder to map the
3D point cloud space to the text space. This mapping leaves us to seamlessly
connect the text space with LLMs. Once the point-text-LLM connection is
established, we further enhance text-LLM alignment by expanding the
intermediate text space, thereby reducing the reliance on 3D point cloud data.
Specifically, we generate 6M free-text descriptions of 3D objects, and design a
three-stage training strategy to help LLMs better explore the intrinsic
connections between different modalities. To achieve efficient modality
alignment, we design a zero-parameter cross-attention module for token pooling.
Extensive experimental results show that GreenPLM requires only 12% of the 3D
training data used by existing state-of-the-art models to achieve superior 3D
understanding. Remarkably, GreenPLM also achieves competitive performance using
text-only data. The code and weights are available at:
https://github.com/TangYuan96/GreenPLM. | cs.CV | [
"cs.CV",
"cs.AI",
"cs.CL"
] |
|||
Atari-GPT: Investigating the Capabilities of Multimodal Large Language
Models as Low-Level Policies for Atari Games | http://arxiv.org/abs/2408.15950v1 | http://arxiv.org/abs/2408.15950v1 | http://arxiv.org/pdf/2408.15950v1 | 2024-08-28 | 2024-08-28 | [
"Nicholas R. Waytowich",
"Devin White",
"MD Sunbeam",
"Vinicius G. Goecks"
] | [
"",
"",
"",
""
] | Recent advancements in large language models (LLMs) have expanded their
capabilities beyond traditional text-based tasks to multimodal domains,
integrating visual, auditory, and textual data. While multimodal LLMs have been
extensively explored for high-level planning in domains like robotics and
games, their potential as low-level controllers remains largely untapped. This
paper explores the application of multimodal LLMs as low-level controllers in
the domain of Atari video games, introducing Atari game performance as a new
benchmark for evaluating the ability of multimodal LLMs to perform low-level
control tasks. Unlike traditional reinforcement learning (RL) and imitation
learning (IL) methods that require extensive computational resources as well as
reward function specification, these LLMs utilize pre-existing multimodal
knowledge to directly engage with game environments. Our study assesses
multiple multimodal LLMs performance against traditional RL agents, human
players, and random agents, focusing on their ability to understand and
interact with complex visual scenes and formulate strategic responses.
Additionally, we examine the impact of In-Context Learning (ICL) by
incorporating human-demonstrated game-play trajectories to enhance the models
contextual understanding. Through this investigation, we aim to determine the
extent to which multimodal LLMs can leverage their extensive training to
effectively function as low-level controllers, thereby redefining potential
applications in dynamic and visually complex environments. Additional results
and videos are available at our project webpage:
https://sites.google.com/view/atari-gpt/. | Currently under review | cs.AI | [
"cs.AI"
] |
||
Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot
Learning | http://arxiv.org/abs/2408.15924v1 | http://arxiv.org/abs/2408.15924v1 | http://arxiv.org/pdf/2408.15924v1 | 2024-08-28 | 2024-08-28 | [
"Bingchen Yan"
] | [
""
] | Few-shot image classification is a challenging task in the field of machine
learning, involving the identification of new categories using a limited number
of labeled samples. In recent years, methods based on local descriptors have
made significant progress in this area. However, the key to improving
classification accuracy lies in effectively filtering background noise and
accurately selecting critical local descriptors highly relevant to image
category information.
To address this challenge, we propose an innovative weighted adaptive
threshold filtering (WATF) strategy for local descriptors. This strategy can
dynamically adjust based on the current task and image context, thereby
selecting local descriptors most relevant to the image category. This enables
the model to better focus on category-related information while effectively
mitigating interference from irrelevant background regions.
To evaluate the effectiveness of our method, we adopted the N-way K-shot
experimental framework. Experimental results show that our method not only
improves the clustering effect of selected local descriptors but also
significantly enhances the discriminative ability between image categories.
Notably, our method maintains a simple and lightweight design philosophy
without introducing additional learnable parameters. This feature ensures
consistency in filtering capability during both training and testing phases,
further enhancing the reliability and practicality of the method. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
Leveraging Open Knowledge for Advancing Task Expertise in Large Language
Models | http://arxiv.org/abs/2408.15915v1 | http://arxiv.org/abs/2408.15915v1 | http://arxiv.org/pdf/2408.15915v1 | 2024-08-28 | 2024-08-28 | [
"Yuncheng Yang",
"Yulei Qin",
"Tong Wu",
"Zihan Xu",
"Gang Li",
"Pengcheng Guo",
"Hang Shao",
"Yucheng Shi",
"Ke Li",
"Xing Sun",
"Jie Yang",
"Yun Gu"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
"",
""
] | The cultivation of expertise for large language models (LLMs) to solve tasks
of specific areas often requires special-purpose tuning with calibrated
behaviors on the expected stable outputs. To avoid huge cost brought by manual
preparation of instruction datasets and training resources up to hundreds of
hours, the exploitation of open knowledge including a wealth of low rank
adaptation (LoRA) models and instruction datasets serves as a good starting
point. However, existing methods on model and data selection focus on the
performance of general-purpose capabilities while neglecting the knowledge gap
exposed in domain-specific deployment. In the present study, we propose to
bridge such gap by introducing few human-annotated samples (i.e., K-shot) for
advancing task expertise of LLMs with open knowledge. Specifically, we develop
an efficient and scalable pipeline to cost-efficiently produce task experts
where K-shot data intervene in selecting the most promising expert candidates
and the task-relevant instructions. A mixture-of-expert (MoE) system is built
to make the best use of individual-yet-complementary knowledge between multiple
experts. We unveil the two keys to the success of a MoE system, 1) the abidance
by K-shot, and 2) the insistence on diversity. For the former, we ensure that
models that truly possess problem-solving abilities on K-shot are selected
rather than those blind guessers. Besides, during data selection, instructions
that share task-relevant contexts with K-shot are prioritized. For the latter,
we highlight the diversity of constituting experts and that of the fine-tuning
instructions throughout the model and data selection process. Extensive
experimental results confirm the superiority of our approach over existing
methods on utilization of open knowledge across various tasks. Codes and models
will be released later. | 28 pages, 12 tables, 10 figures | cs.CV | [
"cs.CV",
"cs.AI",
"cs.CL"
] |
||
Efficient $k$-NN Search in IoT Data: Overlap Optimization in Tree-Based
Indexing Structures | http://arxiv.org/abs/2408.16036v1 | http://arxiv.org/abs/2408.16036v1 | http://arxiv.org/pdf/2408.16036v1 | 2024-08-28 | 2024-08-28 | [
"Ala-Eddine Benrazek",
"Zineddine Kouahla",
"Brahim Farou",
"Hamid Seridi",
"Ibtissem Kemouguette"
] | [
"",
"",
"",
"",
""
] | The proliferation of interconnected devices in the Internet of Things (IoT)
has led to an exponential increase in data, commonly known as Big IoT Data.
Efficient retrieval of this heterogeneous data demands a robust indexing
mechanism for effective organization. However, a significant challenge remains:
the overlap in data space partitions during index construction. This overlap
increases node access during search and retrieval, resulting in higher resource
consumption, performance bottlenecks, and impedes system scalability. To
address this issue, we propose three innovative heuristics designed to quantify
and strategically reduce data space partition overlap. The volume-based method
(VBM) offers a detailed assessment by calculating the intersection volume
between partitions, providing deeper insights into spatial relationships. The
distance-based method (DBM) enhances efficiency by using the distance between
partition centers and radii to evaluate overlap, offering a streamlined yet
accurate approach. Finally, the object-based method (OBM) provides a practical
solution by counting objects across multiple partitions, delivering an
intuitive understanding of data space dynamics. Experimental results
demonstrate the effectiveness of these methods in reducing search time,
underscoring their potential to improve data space partitioning and enhance
overall system performance. | 28 pages, 21 figures, 1 table | cs.DB | [
"cs.DB",
"cs.AI",
"cs.IR",
"cs.PF",
"68P05, 68T01, 68P20",
"E.1; H.2; H.3; I.2"
] |
||
Nexus: Specialization meets Adaptability for Efficiently Training
Mixture of Experts | http://arxiv.org/abs/2408.15901v1 | http://arxiv.org/abs/2408.15901v1 | http://arxiv.org/pdf/2408.15901v1 | 2024-08-28 | 2024-08-28 | [
"Nikolas Gritsch",
"Qizhen Zhang",
"Acyr Locatelli",
"Sara Hooker",
"Ahmet Üstün"
] | [
"",
"",
"",
"",
""
] | Efficiency, specialization, and adaptability to new data distributions are
qualities that are hard to combine in current Large Language Models. The
Mixture of Experts (MoE) architecture has been the focus of significant
research because its inherent conditional computation enables such desirable
properties. In this work, we focus on "upcycling" dense expert models into an
MoE, aiming to improve specialization while also adding the ability to adapt to
new tasks easily. We introduce Nexus, an enhanced MoE architecture with
adaptive routing where the model learns to project expert embeddings from
domain representations. This approach allows Nexus to flexibly add new experts
after the initial upcycling through separately trained dense models, without
requiring large-scale MoE training for unseen data domains. Our experiments
show that Nexus achieves a relative gain of up to 2.1% over the baseline for
initial upcycling, and a 18.8% relative gain for extending the MoE with a new
expert by using limited finetuning data. This flexibility of Nexus is crucial
to enable an open-source ecosystem where every user continuously assembles
their own MoE-mix according to their needs. | cs.CL | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
|||
Airfoil Diffusion: Denoising Diffusion Model For Conditional Airfoil
Generation | http://arxiv.org/abs/2408.15898v1 | http://arxiv.org/abs/2408.15898v1 | http://arxiv.org/pdf/2408.15898v1 | 2024-08-28 | 2024-08-28 | [
"Reid Graves",
"Amir Barati Farimani"
] | [
"",
""
] | The design of aerodynamic shapes, such as airfoils, has traditionally
required significant computational resources and relied on predefined design
parameters, which limit the potential for novel shape synthesis. In this work,
we introduce a data-driven methodology for airfoil generation using a diffusion
model. Trained on a dataset of preexisting airfoils, our model can generate an
arbitrary number of new airfoils from random vectors, which can be conditioned
on specific aerodynamic performance metrics such as lift and drag, or geometric
criteria. Our results demonstrate that the diffusion model effectively produces
airfoil shapes with realistic aerodynamic properties, offering substantial
improvements in efficiency, flexibility, and the potential for discovering
innovative airfoil designs. This approach significantly expands the design
space, facilitating the synthesis of high-performance aerodynamic shapes that
transcend the limitations of traditional methods. | 12 Pages, 6 figures | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
A New Method for Cross-Lingual-based Semantic Role Labeling | http://arxiv.org/abs/2408.15896v1 | http://arxiv.org/abs/2408.15896v1 | http://arxiv.org/pdf/2408.15896v1 | 2024-08-28 | 2024-08-28 | [
"Mohammad Ebrahimi",
"Behrouz Minaei Bidgoli",
"Nasim Khozouei"
] | [
"",
"",
""
] | Semantic role labeling is a crucial task in natural language processing,
enabling better comprehension of natural language. However, the lack of
annotated data in multiple languages has posed a challenge for researchers. To
address this, a deep learning algorithm based on model transfer has been
proposed. The algorithm utilizes a dataset consisting of the English portion of
CoNLL2009 and a corpus of semantic roles in Persian. To optimize the efficiency
of training, only ten percent of the educational data from each language is
used. The results of the proposed model demonstrate significant improvements
compared to Niksirt et al.'s model. In monolingual mode, the proposed model
achieved a 2.05 percent improvement on F1-score, while in cross-lingual mode,
the improvement was even more substantial, reaching 6.23 percent. Worth noting
is that the compared model only trained two of the four stages of semantic role
labeling and employed golden data for the remaining two stages. This suggests
that the actual superiority of the proposed model surpasses the reported
numbers by a significant margin. The development of cross-lingual methods for
semantic role labeling holds promise, particularly in addressing the scarcity
of annotated data for various languages. These advancements pave the way for
further research in understanding and processing natural language across
different linguistic contexts. | cs.CL | [
"cs.CL",
"cs.AI",
"cs.LG"
] |
|||
Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble
Approach Using Kolmogorov-Arnold Networks | http://arxiv.org/abs/2408.15886v2 | http://arxiv.org/abs/2408.15886v2 | http://arxiv.org/pdf/2408.15886v2 | 2024-08-28 | 2024-08-29 | [
"Amar Amouri",
"Mohamad Mahmoud Al Rahhal",
"Yakoub Bazi",
"Ismail Butun",
"Imad Mahgoub"
] | [
"",
"",
"",
"",
""
] | In recent years, the evolution of machine learning techniques has
significantly impacted the field of intrusion detection, particularly within
the context of the Internet of Things (IoT). As IoT networks expand, the need
for robust security measures to counteract potential threats has become
increasingly critical. This paper introduces a hybrid Intrusion Detection
System (IDS) that synergistically combines Kolmogorov-Arnold Networks (KANs)
with the XGBoost algorithm. Our proposed IDS leverages the unique capabilities
of KANs, which utilize learnable activation functions to model complex
relationships within data, alongside the powerful ensemble learning techniques
of XGBoost, known for its high performance in classification tasks. This hybrid
approach not only enhances the detection accuracy but also improves the
interpretability of the model, making it suitable for dynamic and intricate IoT
environments. Experimental evaluations demonstrate that our hybrid IDS achieves
an impressive detection accuracy exceeding 99% in distinguishing between benign
and malicious activities. Additionally, we were able to achieve F1 scores,
precision, and recall that exceeded 98%. Furthermore, we conduct a comparative
analysis against traditional Multi-Layer Perceptron (MLP) networks, assessing
performance metrics such as Precision, Recall, and F1-score. The results
underscore the efficacy of integrating KANs with XGBoost, highlighting the
potential of this innovative approach to significantly strengthen the security
framework of IoT networks. | To be presented at the 11th International Symposium on Networks,
Computers and Communications (ISNCC'24) will be held in Washington DC- USA,
from October 22 to 25, 2024. Accepted (6 pages and 5 figures) | cs.CR | [
"cs.CR",
"cs.AI"
] |
||
Persuasion Games using Large Language Models | http://arxiv.org/abs/2408.15879v1 | http://arxiv.org/abs/2408.15879v1 | http://arxiv.org/pdf/2408.15879v1 | 2024-08-28 | 2024-08-28 | [
"Ganesh Prasath Ramani",
"Shirish Karande",
"Santhosh V",
"Yash Bhatia"
] | [
"",
"",
"",
""
] | Large Language Models (LLMs) have emerged as formidable instruments capable
of comprehending and producing human-like text. This paper explores the
potential of LLMs, to shape human perspectives and subsequently influence their
decisions on particular tasks. This capability finds applications in diverse
domains such as Investment, Credit cards and Insurance, wherein they assist
users in selecting appropriate insurance policies, investment plans, Credit
cards, Retail, as well as in Behavioral Change Support Systems (BCSS).
We present a sophisticated multi-agent framework wherein a consortium of
agents operate in collaborative manner. The primary agent engages directly with
users through persuasive dialogue, while the auxiliary agents perform tasks
such as information retrieval, response analysis, development of persuasion
strategies, and validation of facts. Empirical evidence from our experiments
demonstrates that this collaborative methodology significantly enhances the
persuasive efficacy of the LLM. We analyze user resistance to persuasive
efforts continuously and counteract it by employing a combination of rule-based
and LLM-based resistance-persuasion mapping techniques.
We employ simulated personas and generate conversations in insurance,
banking, and retail domains to evaluate the proficiency of large language
models (LLMs) in recognizing, adjusting to, and influencing various personality
types. Concurrently, we examine the resistance mechanisms employed by LLM
simulated personas. Persuasion is quantified via measurable surveys before and
after interaction, LLM-generated scores on conversation, and user decisions
(purchase or non-purchase). | cs.AI | [
"cs.AI",
"cs.CL"
] |
|||
Robust Statistical Scaling of Outlier Scores: Improving the Quality of
Outlier Probabilities for Outliers (Extended Version) | http://arxiv.org/abs/2408.15874v1 | http://arxiv.org/abs/2408.15874v1 | http://arxiv.org/pdf/2408.15874v1 | 2024-08-28 | 2024-08-28 | [
"Philipp Röchner",
"Henrique O. Marques",
"Ricardo J. G. B. Campello",
"Arthur Zimek",
"Franz Rothlauf"
] | [
"",
"",
"",
"",
""
] | Outlier detection algorithms typically assign an outlier score to each
observation in a dataset, indicating the degree to which an observation is an
outlier. However, these scores are often not comparable across algorithms and
can be difficult for humans to interpret. Statistical scaling addresses this
problem by transforming outlier scores into outlier probabilities without using
ground-truth labels, thereby improving interpretability and comparability
across algorithms. However, the quality of this transformation can be different
for outliers and inliers. Missing outliers in scenarios where they are of
particular interest - such as healthcare, finance, or engineering - can be
costly or dangerous. Thus, ensuring good probabilities for outliers is
essential. This paper argues that statistical scaling, as commonly used in the
literature, does not produce equally good probabilities for outliers as for
inliers. Therefore, we propose robust statistical scaling, which uses robust
estimators to improve the probabilities for outliers. We evaluate several
variants of our method against other outlier score transformations for
real-world datasets and outlier detection algorithms, where it can improve the
probabilities for outliers. | 15 pages, 4 figures, accepted for publication in SISAP 2024 | cs.LG | [
"cs.LG",
"cs.AI"
] |
||
GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video
Generative Model | http://arxiv.org/abs/2408.15868v1 | http://arxiv.org/abs/2408.15868v1 | http://arxiv.org/pdf/2408.15868v1 | 2024-08-28 | 2024-08-28 | [
"Yongjie Fu",
"Yunlong Li",
"Xuan Di"
] | [
"",
"",
""
] | Autonomous driving training requires a diverse range of datasets encompassing
various traffic conditions, weather scenarios, and road types. Traditional data
augmentation methods often struggle to generate datasets that represent rare
occurrences. To address this challenge, we propose GenDDS, a novel approach for
generating driving scenarios generation by leveraging the capabilities of
Stable Diffusion XL (SDXL), an advanced latent diffusion model. Our methodology
involves the use of descriptive prompts to guide the synthesis process, aimed
at producing realistic and diverse driving scenarios. With the power of the
latest computer vision techniques, such as ControlNet and Hotshot-XL, we have
built a complete pipeline for video generation together with SDXL. We employ
the KITTI dataset, which includes real-world driving videos, to train the
model. Through a series of experiments, we demonstrate that our model can
generate high-quality driving videos that closely replicate the complexity and
variability of real-world driving scenarios. This research contributes to the
development of sophisticated training data for autonomous driving systems and
opens new avenues for creating virtual environments for simulation and
validation purposes. | cs.CV | [
"cs.CV",
"cs.AI"
] |
|||
Retrieval-Augmented Instruction Tuning for Automated Process Engineering
Calculations : A Tool-Chaining Problem-Solving Framework with Attributable
Reflection | http://arxiv.org/abs/2408.15866v1 | http://arxiv.org/abs/2408.15866v1 | http://arxiv.org/pdf/2408.15866v1 | 2024-08-28 | 2024-08-28 | [
"Sagar Srinivas Sakhinana",
"Geethan Sannidhi",
"Venkataramana Runkana"
] | [
"",
"",
""
] | The current technology landscape lacks a foundational AI model for solving
process engineering calculations. In this work, we introduce a novel autonomous
agent framework leveraging Retrieval-Augmented Instruction-Tuning (RAIT) to
enhance open, customizable small code language models (SLMs) for these
calculations. By combining instruction tuned code SLMs with Retrieval-Augmented
Code Generation (RACG) using external tools, the agent generates, debugs, and
optimizes code from natural language specifications. Our approach addresses the
limitations of the current lack of a foundational AI model for specialized
process engineering tasks and offers benefits of explainability, knowledge
editing, and cost-effectiveness. Additionally, we curate custom datasets of
chemical and process engineering problems and solutions to overcome data
scarcity. Experimental results show that our framework matches the performance
of large-scale proprietary models on benchmark datasets, proving its
effectiveness and usability. | Accepted for publication at ML4CCE workshop at ECML PKDD 2024. Please
find the link: https://ml4cce-ecml.com/#agenda | cs.SE | [
"cs.SE",
"cs.AI",
"cs.LG"
] |
||
microYOLO: Towards Single-Shot Object Detection on Microcontrollers | http://arxiv.org/abs/2408.15865v1 | http://arxiv.org/abs/2408.15865v1 | http://arxiv.org/pdf/2408.15865v1 | 2024-08-28 | 2024-08-28 | [
"Mark Deutel",
"Christopher Mutschler",
"Jürgen Teich"
] | [
"",
"",
""
] | This work-in-progress paper presents results on the feasibility of
single-shot object detection on microcontrollers using YOLO. Single-shot object
detectors like YOLO are widely used, however due to their complexity mainly on
larger GPU-based platforms. We present microYOLO, which can be used on Cortex-M
based microcontrollers, such as the OpenMV H7 R2, achieving about 3.5 FPS when
classifying 128x128 RGB images while using less than 800 KB Flash and less than
350 KB RAM. Furthermore, we share experimental results for three different
object detection tasks, analyzing the accuracy of microYOLO on them. | Published at the ECML PKDD Conference 2023, at the 4th Workshop on
IoT, Edge, and Mobile for Embedded Machine Learning | cs.CV | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
||
Knowledge Navigator: LLM-guided Browsing Framework for Exploratory
Search in Scientific Literature | http://arxiv.org/abs/2408.15836v1 | http://arxiv.org/abs/2408.15836v1 | http://arxiv.org/pdf/2408.15836v1 | 2024-08-28 | 2024-08-28 | [
"Uri Katz",
"Mosh Levy",
"Yoav Goldberg"
] | [
"",
"",
""
] | The exponential growth of scientific literature necessitates advanced tools
for effective knowledge exploration. We present Knowledge Navigator, a system
designed to enhance exploratory search abilities by organizing and structuring
the retrieved documents from broad topical queries into a navigable, two-level
hierarchy of named and descriptive scientific topics and subtopics. This
structured organization provides an overall view of the research themes in a
domain, while also enabling iterative search and deeper knowledge discovery
within specific subtopics by allowing users to refine their focus and retrieve
additional relevant documents. Knowledge Navigator combines LLM capabilities
with cluster-based methods to enable an effective browsing method. We
demonstrate our approach's effectiveness through automatic and manual
evaluations on two novel benchmarks, CLUSTREC-COVID and SCITOC. Our code,
prompts, and benchmarks are made publicly available. | cs.IR | [
"cs.IR",
"cs.AI",
"cs.CL"
] |
|||
Object Detection for Vehicle Dashcams using Transformers | http://arxiv.org/abs/2408.15809v1 | http://arxiv.org/abs/2408.15809v1 | http://arxiv.org/pdf/2408.15809v1 | 2024-08-28 | 2024-08-28 | [
"Osama Mustafa",
"Khizer Ali",
"Anam Bibi",
"Imran Siddiqi",
"Momina Moetesum"
] | [
"",
"",
"",
"",
""
] | The use of intelligent automation is growing significantly in the automotive
industry, as it assists drivers and fleet management companies, thus increasing
their productivity. Dash cams are now been used for this purpose which enables
the instant identification and understanding of multiple objects and
occurrences in the surroundings. In this paper, we propose a novel approach for
object detection in dashcams using transformers. Our system is based on the
state-of-the-art DEtection TRansformer (DETR), which has demonstrated strong
performance in a variety of conditions, including different weather and
illumination scenarios. The use of transformers allows for the consideration of
contextual information in decisionmaking, improving the accuracy of object
detection. To validate our approach, we have trained our DETR model on a
dataset that represents real-world conditions. Our results show that the use of
intelligent automation through transformers can significantly enhance the
capabilities of dashcam systems. The model achieves an mAP of 0.95 on
detection. | 7 Pages, and 6 Figures | cs.CV | [
"cs.CV",
"cs.AI"
] |
||
ModalityMirror: Improving Audio Classification in Modality Heterogeneity
Federated Learning with Multimodal Distillation | http://arxiv.org/abs/2408.15803v1 | http://arxiv.org/abs/2408.15803v1 | http://arxiv.org/pdf/2408.15803v1 | 2024-08-28 | 2024-08-28 | [
"Tiantian Feng",
"Tuo Zhang",
"Salman Avestimehr",
"Shrikanth S. Narayanan"
] | [
"",
"",
"",
""
] | Multimodal Federated Learning frequently encounters challenges of client
modality heterogeneity, leading to undesired performances for secondary
modality in multimodal learning. It is particularly prevalent in audiovisual
learning, with audio is often assumed to be the weaker modality in recognition
tasks. To address this challenge, we introduce ModalityMirror to improve audio
model performance by leveraging knowledge distillation from an audiovisual
federated learning model. ModalityMirror involves two phases: a modality-wise
FL stage to aggregate uni-modal encoders; and a federated knowledge
distillation stage on multi-modality clients to train an unimodal student
model. Our results demonstrate that ModalityMirror significantly improves the
audio classification compared to the state-of-the-art FL methods such as
Harmony, particularly in audiovisual FL facing video missing. Our approach
unlocks the potential for exploiting the diverse modality spectrum inherent in
multi-modal FL. | eess.AS | [
"eess.AS",
"cs.AI",
"cs.SD"
] |
|||
Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing | http://arxiv.org/abs/2408.15800v1 | http://arxiv.org/abs/2408.15800v1 | http://arxiv.org/pdf/2408.15800v1 | 2024-08-28 | 2024-08-28 | [
"Kenneth Stewart",
"Michael Neumeier",
"Sumit Bam Shrestha",
"Garrick Orchard",
"Emre Neftci"
] | [
"",
"",
"",
"",
""
] | Achieving personalized intelligence at the edge with real-time learning
capabilities holds enormous promise in enhancing our daily experiences and
helping decision making, planning, and sensing. However, efficient and reliable
edge learning remains difficult with current technology due to the lack of
personalized data, insufficient hardware capabilities, and inherent challenges
posed by online learning.
Over time and across multiple developmental stages, the brain has evolved to
efficiently incorporate new knowledge by gradually building on previous
knowledge. In this work, we emulate the multiple stages of learning with
digital neuromorphic technology that simulates the neural and synaptic
processes of the brain using two stages of learning. First, a meta-training
stage trains the hyperparameters of synaptic plasticity for one-shot learning
using a differentiable simulation of the neuromorphic hardware. This
meta-training process refines a hardware local three-factor synaptic plasticity
rule and its associated hyperparameters to align with the trained task domain.
In a subsequent deployment stage, these optimized hyperparameters enable fast,
data-efficient, and accurate learning of new classes. We demonstrate our
approach using event-driven vision sensor data and the Intel Loihi neuromorphic
processor with its plasticity dynamics, achieving real-time one-shot learning
of new classes that is vastly improved over transfer learning. Our methodology
can be deployed with arbitrary plasticity models and can be applied to
situations demanding quick learning and adaptation at the edge, such as
navigating unfamiliar environments or learning unexpected categories of data
through user engagement. | 17 page journal article. Submitted to IOP NCE | cs.NE | [
"cs.NE",
"cs.AI"
] |
||
Evaluating Named Entity Recognition Using Few-Shot Prompting with Large
Language Models | http://arxiv.org/abs/2408.15796v1 | http://arxiv.org/abs/2408.15796v1 | http://arxiv.org/pdf/2408.15796v1 | 2024-08-28 | 2024-08-28 | [
"Hédi Zhegidi",
"Ludovic Moncla"
] | [
"",
""
] | This paper evaluates Few-Shot Prompting with Large Language Models for Named
Entity Recognition (NER). Traditional NER systems rely on extensive labeled
datasets, which are costly and time-consuming to obtain. Few-Shot Prompting or
in-context learning enables models to recognize entities with minimal examples.
We assess state-of-the-art models like GPT-4 in NER tasks, comparing their
few-shot performance to fully supervised benchmarks. Results show that while
there is a performance gap, large models excel in adapting to new entity types
and domains with very limited data. We also explore the effects of prompt
engineering, guided output format and context length on performance. This study
underscores Few-Shot Learning's potential to reduce the need for large labeled
datasets, enhancing NER scalability and accessibility. | Github repo: https://github.com/GEODE-project/ner-llm | cs.IR | [
"cs.IR",
"cs.AI"
] |
||
LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language
Models | http://arxiv.org/abs/2408.15778v1 | http://arxiv.org/abs/2408.15778v1 | http://arxiv.org/pdf/2408.15778v1 | 2024-08-28 | 2024-08-28 | [
"Jiayi Gui",
"Yiming Liu",
"Jiale Cheng",
"Xiaotao Gu",
"Xiao Liu",
"Hongning Wang",
"Yuxiao Dong",
"Jie Tang",
"Minlie Huang"
] | [
"",
"",
"",
"",
"",
"",
"",
"",
""
] | Large Language Models (LLMs) have demonstrated notable capabilities across
various tasks, showcasing complex problem-solving abilities. Understanding and
executing complex rules, along with multi-step planning, are fundamental to
logical reasoning and critical for practical LLM agents and decision-making
systems. However, evaluating LLMs as effective rule-based executors and
planners remains underexplored. In this paper, we introduce LogicGame, a novel
benchmark designed to evaluate the comprehensive rule understanding, execution,
and planning capabilities of LLMs. Unlike traditional benchmarks, LogicGame
provides diverse games that contain a series of rules with an initial state,
requiring models to comprehend and apply predefined regulations to solve
problems. We create simulated scenarios in which models execute or plan
operations to achieve specific outcomes. These game scenarios are specifically
designed to distinguish logical reasoning from mere knowledge by relying
exclusively on predefined rules. This separation allows for a pure assessment
of rule-based reasoning capabilities. The evaluation considers not only final
outcomes but also intermediate steps, providing a comprehensive assessment of
model performance. Moreover, these intermediate steps are deterministic and can
be automatically verified. LogicGame defines game scenarios with varying
difficulty levels, from simple rule applications to complex reasoning chains,
in order to offer a precise evaluation of model performance on rule
understanding and multi-step execution. Utilizing LogicGame, we test various
LLMs and identify notable shortcomings in their rule-based logical reasoning
abilities. | cs.AI | [
"cs.AI",
"cs.CL"
] |
|||
Easy, Interpretable, Effective: openSMILE for voice deepfake detection | http://arxiv.org/abs/2408.15775v2 | http://arxiv.org/abs/2408.15775v2 | http://arxiv.org/pdf/2408.15775v2 | 2024-08-28 | 2024-08-29 | [
"Octavian Pascu",
"Dan Oneata",
"Horia Cucu",
"Nicolas M. Müller"
] | [
"",
"",
"",
""
] | In this paper, we demonstrate that attacks in the latest ASVspoof5 dataset --
a de facto standard in the field of voice authenticity and deepfake detection
-- can be identified with surprising accuracy using a small subset of very
simplistic features. These are derived from the openSMILE library, and are
scalar-valued, easy to compute, and human interpretable. For example, attack
A10`s unvoiced segments have a mean length of 0.09 +- 0.02, while bona fide
instances have a mean length of 0.18 +- 0.07. Using this feature alone, a
threshold classifier achieves an Equal Error Rate (EER) of 10.3% for attack
A10. Similarly, across all attacks, we achieve up to 0.8% EER, with an overall
EER of 15.7 +- 6.0%. We explore the generalization capabilities of these
features and find that some of them transfer effectively between attacks,
primarily when the attacks originate from similar Text-to-Speech (TTS)
architectures. This finding may indicate that voice anti-spoofing is, in part,
a problem of identifying and remembering signatures or fingerprints of
individual TTS systems. This allows to better understand anti-spoofing models
and their challenges in real-world application. | eess.AS | [
"eess.AS",
"cs.AI",
"cs.SD"
] |
|||
A Survey on Evaluation of Multimodal Large Language Models | http://arxiv.org/abs/2408.15769v1 | http://arxiv.org/abs/2408.15769v1 | http://arxiv.org/pdf/2408.15769v1 | 2024-08-28 | 2024-08-28 | [
"Jiaxing Huang",
"Jingyi Zhang"
] | [
"",
""
] | Multimodal Large Language Models (MLLMs) mimic human perception and reasoning
system by integrating powerful Large Language Models (LLMs) with various
modality encoders (e.g., vision, audio), positioning LLMs as the "brain" and
various modality encoders as sensory organs. This framework endows MLLMs with
human-like capabilities, and suggests a potential pathway towards achieving
artificial general intelligence (AGI). With the emergence of all-round MLLMs
like GPT-4V and Gemini, a multitude of evaluation methods have been developed
to assess their capabilities across different dimensions. This paper presents a
systematic and comprehensive review of MLLM evaluation methods, covering the
following key aspects: (1) the background of MLLMs and their evaluation; (2)
"what to evaluate" that reviews and categorizes existing MLLM evaluation tasks
based on the capabilities assessed, including general multimodal recognition,
perception, reasoning and trustworthiness, and domain-specific applications
such as socioeconomic, natural sciences and engineering, medical usage, AI
agent, remote sensing, video and audio processing, 3D point cloud analysis, and
others; (3) "where to evaluate" that summarizes MLLM evaluation benchmarks into
general and specific benchmarks; (4) "how to evaluate" that reviews and
illustrates MLLM evaluation steps and metrics; Our overarching goal is to
provide valuable insights for researchers in the field of MLLM evaluation,
thereby facilitating the development of more capable and reliable MLLMs. We
emphasize that evaluation should be regarded as a critical discipline,
essential for advancing the field of MLLMs. | cs.CV | [
"cs.CV",
"cs.AI",
"cs.CL"
] |
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