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Optimizing Structured Data Processing through Robotic Process Automation

http://arxiv.org/abs/2408.14791v1

http://arxiv.org/abs/2408.14791v1

http://arxiv.org/pdf/2408.14791v1

2024-08-27

2024-08-27

Robotic Process Automation (RPA) has emerged as a game-changing technology in data extraction, revolutionizing the way organizations process and analyze large volumes of documents such as invoices, purchase orders, and payment advices. This study investigates the use of RPA for structured data extraction and evaluates its advantages over manual processes. By comparing human-performed tasks with those executed by RPA software bots, we assess efficiency and accuracy in data extraction from invoices, focusing on the effectiveness of the RPA system. Through four distinct scenarios involving varying numbers of invoices, we measure efficiency in terms of time and effort required for task completion, as well as accuracy by comparing error rates between manual and RPA processes. Our findings highlight the significant efficiency gains achieved by RPA, with bots completing tasks in significantly less time compared to manual efforts across all cases. Moreover, the RPA system consistently achieves perfect accuracy, mitigating the risk of errors and enhancing process reliability. These results underscore the transformative potential of RPA in optimizing operational efficiency, reducing human labor costs, and improving overall business performance.

This manuscript has been accepted for publication in the journal Revue d'Intelligence Artificielle

cs.AI

GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks

http://arxiv.org/abs/2408.14780v2

http://arxiv.org/abs/2408.14780v2

http://arxiv.org/pdf/2408.14780v2

2024-08-27

2024-08-28

Neural networks are powerful function approximators, yet their ``black-box" nature often renders them opaque and difficult to interpret. While many post-hoc explanation methods exist, they typically fail to capture the underlying reasoning processes of the networks. A truly interpretable neural network would be trained similarly to conventional models using techniques such as backpropagation, but additionally provide insights into the learned input-output relationships. In this work, we introduce the concept of interpretability pipelineing, to incorporate multiple interpretability techniques to outperform each individual technique. To this end, we first evaluate several architectures that promise such interpretability, with a particular focus on two recent models selected for their potential to incorporate interpretability into standard neural network architectures while still leveraging backpropagation: the Growing Interpretable Neural Network (GINN) and Kolmogorov Arnold Networks (KAN). We analyze the limitations and strengths of each and introduce a novel interpretable neural network GINN-KAN that synthesizes the advantages of both models. When tested on the Feynman symbolic regression benchmark datasets, GINN-KAN outperforms both GINN and KAN. To highlight the capabilities and the generalizability of this approach, we position GINN-KAN as an alternative to conventional black-box networks in Physics-Informed Neural Networks (PINNs). We expect this to have far-reaching implications in the application of deep learning pipelines in the natural sciences. Our experiments with this interpretable PINN on 15 different partial differential equations demonstrate that GINN-KAN augmented PINNs outperform PINNs with black-box networks in solving differential equations and surpass the capabilities of both GINN and KAN.

cs.LG

MROVSeg: Breaking the Resolution Curse of Vision-Language Models in Open-Vocabulary Semantic Segmentation

http://arxiv.org/abs/2408.14776v1

http://arxiv.org/abs/2408.14776v1

http://arxiv.org/pdf/2408.14776v1

2024-08-27

2024-08-27

Open-vocabulary semantic segmentation aims to segment and recognize semantically meaningful regions based on text-based descriptions during inference. A typical solution to address this task is to leverage powerful vision-language models (VLMs), such as CLIP, to bridge the gap between open- and close-vocabulary recognition. As VLMs are usually pretrained with low-resolution images (e.g. $224\times224$), most previous methods operate only on downscaled images. We question this design as low resolution features often fail to preserve fine details. Although employing additional image backbones for high-resolution inputs can mitigate this issue, it may also introduce significant computation overhead. Therefore, we propose MROVSeg, a multi-resolution training framework for open-vocabulary semantic segmentation with a single pretrained CLIP backbone, that uses sliding windows to slice the high-resolution input into uniform patches, each matching the input size of the well-trained image encoder. Its key components include a Multi-Res Adapter, which restores the spatial geometry and grasps local-global correspondences across patches by learnable convolutional and scale attention layers. To achieve accurate segmentation, we introduce Multi-grained Masked Attention scheme to aggregate multi-grained semantics by performing cross-attention between object queries and multi-resolution CLIP features within the region of interests. Through comprehensive experiments, we demonstrate the superiority of MROVSeg on well-established open-vocabulary semantic segmentation benchmarks, particularly for high-resolution inputs, establishing new standards for open-vocabulary semantic segmentation.

Technical report

cs.CV

A global AI community requires language-diverse publishing

http://arxiv.org/abs/2408.14772v1

http://arxiv.org/abs/2408.14772v1

http://arxiv.org/pdf/2408.14772v1

2024-08-27

2024-08-27

In this provocation, we discuss the English dominance of the AI research community, arguing that the requirement for English language publishing upholds and reinforces broader regimes of extraction in AI. While large language models and machine translation have been celebrated as a way to break down barriers, we regard their use as a symptom of linguistic exclusion of scientists and potential readers. We propose alternative futures for a healthier publishing culture, organized around three themes: administering conferences in the languages of the country in which they are held, instructing peer reviewers not to adjudicate the language appropriateness of papers, and offering opportunities to publish and present in multiple languages. We welcome new translations of this piece. Please contact the authors if you would like to contribute one.

Translations by Michael Hardy (Guarani), Vandana Sarin and Vivek Sarin (Hindi), Roshna Omer Abdulrahman (Soran\^i Kurdish), Gabriel Poesia (Portuguese), and Mat\'ias Grinberg (Spanish). In the proceedings of the Global AI Cultures Workshop at the Twelfth International Conference on Learning Representations (ICLR) 2024, Vienna, Austria, May 7-11, 2024

cs.CL

Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generation

http://arxiv.org/abs/2408.14754v1

http://arxiv.org/abs/2408.14754v1

http://arxiv.org/pdf/2408.14754v1

2024-08-27

2024-08-27

Dual-energy computed tomography (DECT) has been widely used to obtain quantitative elemental composition of imaged subjects for personalized and precise medical diagnosis. Compared with DECT leveraging advanced X-ray source and/or detector technologies, the use of the sequential-scanning data acquisition scheme to implement DECT may make a broader impact on clinical practice because this scheme requires no specialized hardware designs and can be directly implemented into conventional CT systems. However, since the concentration of iodinated contrast agent in the imaged subject varies over time, sequentially scanned data sets acquired at two tube potentials are temporally inconsistent. As existing material basis image reconstruction approaches assume that the data sets acquired at two tube potentials are temporally consistent, the violation of this assumption results in inaccurate quantification of material concentration. In this work, we developed sequential-scanning DECT imaging using high temporal resolution image reconstruction and error-compensated material basis image generation, ACCELERATION in short, to address the technical challenge induced by temporal inconsistency of sequentially scanned data sets and improve quantification accuracy of material concentration in sequential-scanning DECT. ACCELERATION has been validated and evaluated using numerical simulation data sets generated from clinical human subject exams and experimental human subject studies. Results demonstrated the improvement of quantification accuracy and image quality using ACCELERATION.

physics.med-ph

CoopASD: Cooperative Machine Anomalous Sound Detection with Privacy Concerns

http://arxiv.org/abs/2408.14753v1

http://arxiv.org/abs/2408.14753v1

http://arxiv.org/pdf/2408.14753v1

2024-08-27

2024-08-27

Machine anomalous sound detection (ASD) has emerged as one of the most promising applications in the Industrial Internet of Things (IIoT) due to its unprecedented efficacy in mitigating risks of malfunctions and promoting production efficiency. Previous works mainly investigated the machine ASD task under centralized settings. However, developing the ASD system under decentralized settings is crucial in practice, since the machine data are dispersed in various factories and the data should not be explicitly shared due to privacy concerns. To enable these factories to cooperatively develop a scalable ASD model while preserving their privacy, we propose a novel framework named CoopASD, where each factory trains an ASD model on its local dataset, and a central server aggregates these local models periodically. We employ a pre-trained model as the backbone of the ASD model to improve its robustness and develop specialized techniques to stabilize the model under a completely non-iid and domain shift setting. Compared with previous state-of-the-art (SOTA) models trained in centralized settings, CoopASD showcases competitive results with negligible degradation of 0.08%. We also conduct extensive ablation studies to demonstrate the effectiveness of CoopASD.

Accepted by GLOBECOM 2024

cs.SD

Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper

http://arxiv.org/abs/2408.14747v1

http://arxiv.org/abs/2408.14747v1

http://arxiv.org/pdf/2408.14747v1

2024-08-27

2024-08-27

Reinforcement Learning (RL) training is predominantly conducted in cost-effective and controlled simulation environments. However, the transfer of these trained models to real-world tasks often presents unavoidable challenges. This research explores the direct training of RL algorithms in controlled yet realistic real-world settings for the execution of dexterous manipulation. The benchmarking results of three RL algorithms trained on intricate in-hand manipulation tasks within practical real-world contexts are presented. Our study not only demonstrates the practicality of RL training in authentic real-world scenarios, facilitating direct real-world applications, but also provides insights into the associated challenges and considerations. Additionally, our experiences with the employed experimental methods are shared, with the aim of empowering and engaging fellow researchers and practitioners in this dynamic field of robotics.

Australasian conference on robotics and automation (ACRA 2023)

cs.RO

RSTeller: Scaling Up Visual Language Modeling in Remote Sensing with Rich Linguistic Semantics from Openly Available Data and Large Language Models

http://arxiv.org/abs/2408.14744v1

http://arxiv.org/abs/2408.14744v1

http://arxiv.org/pdf/2408.14744v1

2024-08-27

2024-08-27

Abundant, well-annotated multimodal data in remote sensing are pivotal for aligning complex visual remote sensing (RS) scenes with human language, enabling the development of specialized vision language models across diverse RS interpretation tasks. However, annotating RS images with rich linguistic semantics at scale demands expertise in RS and substantial human labor, making it costly and often impractical. In this study, we propose a workflow that leverages large language models (LLMs) to generate multimodal datasets with semantically rich captions at scale from plain OpenStreetMap (OSM) data for images sourced from the Google Earth Engine (GEE) platform. This approach facilitates the generation of paired remote sensing data and can be readily scaled up using openly available data. Within this framework, we present RSTeller, a multimodal dataset comprising over 1 million RS images, each accompanied by multiple descriptive captions. Extensive experiments demonstrate that RSTeller enhances the performance of multiple existing vision language models for RS scene understanding through continual pre-training. Our methodology significantly reduces the manual effort and expertise needed for annotating remote sensing imagery while democratizing access to high-quality annotated data. This advancement fosters progress in visual language modeling and encourages broader participation in remote sensing research and applications. The RSTeller dataset is available at https://github.com/SlytherinGe/RSTeller.

Submitted to ISPRS

cs.CV

TART: Boosting Clean Accuracy Through Tangent Direction Guided Adversarial Training

http://arxiv.org/abs/2408.14728v1

http://arxiv.org/abs/2408.14728v1

http://arxiv.org/pdf/2408.14728v1

2024-08-27

2024-08-27

Adversarial training has been shown to be successful in enhancing the robustness of deep neural networks against adversarial attacks. However, this robustness is accompanied by a significant decline in accuracy on clean data. In this paper, we propose a novel method, called Tangent Direction Guided Adversarial Training (TART), that leverages the tangent space of the data manifold to ameliorate the existing adversarial defense algorithms. We argue that training with adversarial examples having large normal components significantly alters the decision boundary and hurts accuracy. TART mitigates this issue by estimating the tangent direction of adversarial examples and allocating an adaptive perturbation limit according to the norm of their tangential component. To the best of our knowledge, our paper is the first work to consider the concept of tangent space and direction in the context of adversarial defense. We validate the effectiveness of TART through extensive experiments on both simulated and benchmark datasets. The results demonstrate that TART consistently boosts clean accuracy while retaining a high level of robustness against adversarial attacks. Our findings suggest that incorporating the geometric properties of data can lead to more effective and efficient adversarial training methods.

cs.LG

XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model

http://arxiv.org/abs/2408.16021v1

http://arxiv.org/abs/2408.16021v1

http://arxiv.org/pdf/2408.16021v1

2024-08-27

2024-08-27

In the rapidly evolving field of cybersecurity, the integration of flow-level and packet-level information for real-time intrusion detection remains a largely untapped area of research. This paper introduces "XG-NID," a novel framework that, to the best of our knowledge, is the first to fuse flow-level and packet-level data within a heterogeneous graph structure, offering a comprehensive analysis of network traffic. Leveraging a heterogeneous graph neural network (GNN) with graph-level classification, XG-NID uniquely enables real-time inference while effectively capturing the intricate relationships between flow and packet payload data. Unlike traditional GNN-based methodologies that predominantly analyze historical data, XG-NID is designed to accommodate the heterogeneous nature of network traffic, providing a robust and real-time defense mechanism. Our framework extends beyond mere classification; it integrates Large Language Models (LLMs) to generate detailed, human-readable explanations and suggest potential remedial actions, ensuring that the insights produced are both actionable and comprehensible. Additionally, we introduce a new set of flow features based on temporal information, further enhancing the contextual and explainable inferences provided by our model. To facilitate practical application and accessibility, we developed "GNN4ID," an open-source tool that enables the extraction and transformation of raw network traffic into the proposed heterogeneous graph structure, seamlessly integrating flow and packet-level data. Our comprehensive quantitative comparative analysis demonstrates that XG-NID achieves an F1 score of 97\% in multi-class classification, outperforming existing baseline and state-of-the-art methods. This sets a new standard in Network Intrusion Detection Systems by combining innovative data fusion with enhanced interpretability and real-time capabilities.

19 pages, 6 figures

cs.CR

PAT: Pruning-Aware Tuning for Large Language Models

http://arxiv.org/abs/2408.14721v1

http://arxiv.org/abs/2408.14721v1

http://arxiv.org/pdf/2408.14721v1

2024-08-27

2024-08-27

Large language models (LLMs) excel in language tasks, especially with supervised fine-tuning after pre-training. However, their substantial memory and computational requirements hinder practical applications. Structural pruning, which reduces less significant weight dimensions, is one solution. Yet, traditional post-hoc pruning often leads to significant performance loss, with limited recovery from further fine-tuning due to reduced capacity. Since the model fine-tuning refines the general and chaotic knowledge in pre-trained models, we aim to incorporate structural pruning with the fine-tuning, and propose the Pruning-Aware Tuning (PAT) paradigm to eliminate model redundancy while preserving the model performance to the maximum extend. Specifically, we insert the innovative Hybrid Sparsification Modules (HSMs) between the Attention and FFN components to accordingly sparsify the upstream and downstream linear modules. The HSM comprises a lightweight operator and a globally shared trainable mask. The lightweight operator maintains a training overhead comparable to that of LoRA, while the trainable mask unifies the channels to be sparsified, ensuring structural pruning. Additionally, we propose the Identity Loss which decouples the transformation and scaling properties of the HSMs to enhance training robustness. Extensive experiments demonstrate that PAT excels in both performance and efficiency. For example, our Llama2-7b model with a 25\% pruning ratio achieves 1.33$\times$ speedup while outperforming the LoRA-finetuned model by up to 1.26\% in accuracy with a similar training cost. Code: https://github.com/kriskrisliu/PAT_Pruning-Aware-Tuning

cs.LG

Residual-based Adaptive Huber Loss (RAHL) -- Design of an improved Huber loss for CQI prediction in 5G networks

http://arxiv.org/abs/2408.14718v1

http://arxiv.org/abs/2408.14718v1

http://arxiv.org/pdf/2408.14718v1

2024-08-27

2024-08-27

The Channel Quality Indicator (CQI) plays a pivotal role in 5G networks, optimizing infrastructure dynamically to ensure high Quality of Service (QoS). Recent research has focused on improving CQI estimation in 5G networks using machine learning. In this field, the selection of the proper loss function is critical for training an accurate model. Two commonly used loss functions are Mean Squared Error (MSE) and Mean Absolute Error (MAE). Roughly speaking, MSE put more weight on outliers, MAE on the majority. Here, we argue that the Huber loss function is more suitable for CQI prediction, since it combines the benefits of both MSE and MAE. To achieve this, the Huber loss transitions smoothly between MSE and MAE, controlled by a user-defined hyperparameter called delta. However, finding the right balance between sensitivity to small errors (MAE) and robustness to outliers (MSE) by manually choosing the optimal delta is challenging. To address this issue, we propose a novel loss function, named Residual-based Adaptive Huber Loss (RAHL). In RAHL, a learnable residual is added to the delta, enabling the model to adapt based on the distribution of errors in the data. Our approach effectively balances model robustness against outliers while preserving inlier data precision. The widely recognized Long Short-Term Memory (LSTM) model is employed in conjunction with RAHL, showcasing significantly improved results compared to the aforementioned loss functions. The obtained results affirm the superiority of RAHL, offering a promising avenue for enhanced CQI prediction in 5G networks.

https://sol.sbc.org.br/index.php/sbrc/article/view/29822/29625

cs.NI

Text2SQL is Not Enough: Unifying AI and Databases with TAG

http://arxiv.org/abs/2408.14717v1

http://arxiv.org/abs/2408.14717v1

http://arxiv.org/pdf/2408.14717v1

2024-08-27

2024-08-27

AI systems that serve natural language questions over databases promise to unlock tremendous value. Such systems would allow users to leverage the powerful reasoning and knowledge capabilities of language models (LMs) alongside the scalable computational power of data management systems. These combined capabilities would empower users to ask arbitrary natural language questions over custom data sources. However, existing methods and benchmarks insufficiently explore this setting. Text2SQL methods focus solely on natural language questions that can be expressed in relational algebra, representing a small subset of the questions real users wish to ask. Likewise, Retrieval-Augmented Generation (RAG) considers the limited subset of queries that can be answered with point lookups to one or a few data records within the database. We propose Table-Augmented Generation (TAG), a unified and general-purpose paradigm for answering natural language questions over databases. The TAG model represents a wide range of interactions between the LM and database that have been previously unexplored and creates exciting research opportunities for leveraging the world knowledge and reasoning capabilities of LMs over data. We systematically develop benchmarks to study the TAG problem and find that standard methods answer no more than 20% of queries correctly, confirming the need for further research in this area. We release code for the benchmark at https://github.com/TAG-Research/TAG-Bench.

cs.DB

StyleSpeech: Parameter-efficient Fine Tuning for Pre-trained Controllable Text-to-Speech

http://arxiv.org/abs/2408.14713v1

http://arxiv.org/abs/2408.14713v1

http://arxiv.org/pdf/2408.14713v1

2024-08-27

2024-08-27

This paper introduces StyleSpeech, a novel Text-to-Speech~(TTS) system that enhances the naturalness and accuracy of synthesized speech. Building upon existing TTS technologies, StyleSpeech incorporates a unique Style Decorator structure that enables deep learning models to simultaneously learn style and phoneme features, improving adaptability and efficiency through the principles of Lower Rank Adaptation~(LoRA). LoRA allows efficient adaptation of style features in pre-trained models. Additionally, we introduce a novel automatic evaluation metric, the LLM-Guided Mean Opinion Score (LLM-MOS), which employs large language models to offer an objective and robust protocol for automatically assessing TTS system performance. Extensive testing on benchmark datasets shows that our approach markedly outperforms existing state-of-the-art baseline methods in producing natural, accurate, and high-quality speech. These advancements not only pushes the boundaries of current TTS system capabilities, but also facilitate the application of TTS system in more dynamic and specialized, such as interactive virtual assistants, adaptive audiobooks, and customized voice for gaming. Speech samples can be found in https://style-speech.vercel.app

cs.SD

Artificial Intelligence in Landscape Architecture: A Survey

http://arxiv.org/abs/2408.14700v1

http://arxiv.org/abs/2408.14700v1

http://arxiv.org/pdf/2408.14700v1

2024-08-26

2024-08-26

The development history of landscape architecture (LA) reflects the human pursuit of environmental beautification and ecological balance. With the advancement of artificial intelligence (AI) technologies that simulate and extend human intelligence, immense opportunities have been provided for LA, offering scientific and technological support throughout the entire workflow. In this article, we comprehensively review the applications of AI technology in the field of LA. First, we introduce the many potential benefits that AI brings to the design, planning, and management aspects of LA. Secondly, we discuss how AI can assist the LA field in solving its current development problems, including urbanization, environmental degradation and ecological decline, irrational planning, insufficient management and maintenance, and lack of public participation. Furthermore, we summarize the key technologies and practical cases of applying AI in the LA domain, from design assistance to intelligent management, all of which provide innovative solutions for the planning, design, and maintenance of LA. Finally, we look ahead to the problems and opportunities in LA, emphasizing the need to combine human expertise and judgment for rational decision-making. This article provides both theoretical and practical guidance for LA designers, researchers, and technology developers. The successful integration of AI technology into LA holds great promise for enhancing the field's capabilities and achieving more sustainable, efficient, and user-friendly outcomes.

Preprint. 3 figures, 2 tables

cs.AI

Smart Multi-Modal Search: Contextual Sparse and Dense Embedding Integration in Adobe Express

http://arxiv.org/abs/2408.14698v2

http://arxiv.org/abs/2408.14698v2

http://arxiv.org/pdf/2408.14698v2

2024-08-26

2024-08-29

As user content and queries become increasingly multi-modal, the need for effective multi-modal search systems has grown. Traditional search systems often rely on textual and metadata annotations for indexed images, while multi-modal embeddings like CLIP enable direct search using text and image embeddings. However, embedding-based approaches face challenges in integrating contextual features such as user locale and recency. Building a scalable multi-modal search system requires fine-tuning several components. This paper presents a multi-modal search architecture and a series of AB tests that optimize embeddings and multi-modal technologies in Adobe Express template search. We address considerations such as embedding model selection, the roles of embeddings in matching and ranking, and the balance between dense and sparse embeddings. Our iterative approach demonstrates how utilizing sparse, dense, and contextual features enhances short and long query search, significantly reduces null rates (over 70\%), and increases click-through rates (CTR). Our findings provide insights into developing robust multi-modal search systems, thereby enhancing relevance for complex queries.

CIKM 2024 (International Conference on Information and Knowledge Management), Multimodal Search and Recommendations Workshop

cs.IR

Training-Free Activation Sparsity in Large Language Models

http://arxiv.org/abs/2408.14690v1

http://arxiv.org/abs/2408.14690v1

http://arxiv.org/pdf/2408.14690v1

2024-08-26

2024-08-26

Activation sparsity can enable practical inference speedups in large language models (LLMs) by reducing the compute and memory-movement required for matrix multiplications during the forward pass. However, existing methods face limitations that inhibit widespread adoption. Some approaches are tailored towards older models with ReLU-based sparsity, while others require extensive continued pre-training on up to hundreds of billions of tokens. This paper describes TEAL, a simple training-free method that applies magnitude-based activation sparsity to hidden states throughout the entire model. TEAL achieves 40-50% model-wide sparsity with minimal performance degradation across Llama-2, Llama-3, and Mistral families, with sizes varying from 7B to 70B. We improve existing sparse kernels and demonstrate wall-clock decoding speed-ups of up to 1.53$\times$ and 1.8$\times$ at 40% and 50% model-wide sparsity. TEAL is compatible with weight quantization, enabling further efficiency gains.

cs.CL

Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems

http://arxiv.org/abs/2408.14678v1

http://arxiv.org/abs/2408.14678v1

http://arxiv.org/pdf/2408.14678v1

2024-08-26

2024-08-26

Knowledge Distillation (KD) is a powerful approach for compressing a large model into a smaller, more efficient model, particularly beneficial for latency-sensitive applications like recommender systems. However, current KD research predominantly focuses on Computer Vision (CV) and NLP tasks, overlooking unique data characteristics and challenges inherent to recommender systems. This paper addresses these overlooked challenges, specifically: (1) mitigating data distribution shifts between teacher and student models, (2) efficiently identifying optimal teacher configurations within time and budgetary constraints, and (3) enabling computationally efficient and rapid sharing of teacher labels to support multiple students. We present a robust KD system developed and rigorously evaluated on multiple large-scale personalized video recommendation systems within Google. Our live experiment results demonstrate significant improvements in student model performance while ensuring consistent and reliable generation of high quality teacher labels from a continuous data stream of data.

cs.IR

KGPrune: a Web Application to Extract Subgraphs of Interest from Wikidata with Analogical Pruning

http://arxiv.org/abs/2408.14658v1

http://arxiv.org/abs/2408.14658v1

http://arxiv.org/pdf/2408.14658v1

2024-08-26

2024-08-26

Knowledge graphs (KGs) have become ubiquitous publicly available knowledge sources, and are nowadays covering an ever increasing array of domains. However, not all knowledge represented is useful or pertaining when considering a new application or specific task. Also, due to their increasing size, handling large KGs in their entirety entails scalability issues. These two aspects asks for efficient methods to extract subgraphs of interest from existing KGs. To this aim, we introduce KGPrune, a Web Application that, given seed entities of interest and properties to traverse, extracts their neighboring subgraphs from Wikidata. To avoid topical drift, KGPrune relies on a frugal pruning algorithm based on analogical reasoning to only keep relevant neighbors while pruning irrelevant ones. The interest of KGPrune is illustrated by two concrete applications, namely, bootstrapping an enterprise KG and extracting knowledge related to looted artworks.

Accepted as a demo paper at ECAI 2024

cs.AI

Emergent Language in Open-Ended Environments

http://arxiv.org/abs/2408.14649v1

http://arxiv.org/abs/2408.14649v1

http://arxiv.org/pdf/2408.14649v1

2024-08-26

2024-08-26

Emergent language research has made significant progress in recent years, but still largely fails to explore how communication emerges in more complex and situated multi-agent systems. Existing setups often employ a reference game, which limits the range of language emergence phenomena that can be studied, as the game consists of a single, purely language-based interaction between the agents. In this paper, we address these limitations and explore the emergence and utility of token-based communication in open-ended multi-agent environments, where situated agents interact with the environment through movement and communication over multiple time-steps. Specifically, we introduce two novel cooperative environments: Multi-Agent Pong and Collectors. These environments are interesting because optimal performance requires the emergence of a communication protocol, but moderate success can be achieved without one. By employing various methods from explainable AI research, such as saliency maps, perturbation, and diagnostic classifiers, we are able to track and interpret the agents' language channel use over time. We find that the emerging communication is sparse, with the agents only generating meaningful messages and acting upon incoming messages in states where they cannot succeed without coordination.

10 pages, 4 figures, 4 tables, preprint

cs.AI

Visions of Destruction: Exploring a Potential of Generative AI in Interactive Art

http://arxiv.org/abs/2408.14644v1

http://arxiv.org/abs/2408.14644v1

http://arxiv.org/pdf/2408.14644v1

2024-08-26

2024-08-26

This paper explores the potential of generative AI within interactive art, employing a practice-based research approach. It presents the interactive artwork "Visions of Destruction" as a detailed case study, highlighting its innovative use of generative AI to create a dynamic, audience-responsive experience. This artwork applies gaze-based interaction to dynamically alter digital landscapes, symbolizing the impact of human activities on the environment by generating contemporary collages created with AI, trained on data about human damage to nature, and guided by audience interaction. The transformation of pristine natural scenes into human-made and industrialized landscapes through viewer interaction serves as a stark reminder of environmental degradation. The paper thoroughly explores the technical challenges and artistic innovations involved in creating such an interactive art installation, emphasizing the potential of generative AI to revolutionize artistic expression, audience engagement, and especially the opportunities for the interactive art field. It offers insights into the conceptual framework behind the artwork, aiming to evoke a deeper understanding and reflection on the Anthropocene era and human-induced climate change. This study contributes significantly to the field of creative AI and interactive art, blending technology and environmental consciousness in a compelling, thought-provoking manner.

10.1145/3678698.3687185

cs.HC

Effect of Adaptation Rate and Cost Display in a Human-AI Interaction Game

http://arxiv.org/abs/2408.14640v1

http://arxiv.org/abs/2408.14640v1

http://arxiv.org/pdf/2408.14640v1

2024-08-26

2024-08-26

As interactions between humans and AI become more prevalent, it is critical to have better predictors of human behavior in these interactions. We investigated how changes in the AI's adaptive algorithm impact behavior predictions in two-player continuous games. In our experiments, the AI adapted its actions using a gradient descent algorithm under different adaptation rates while human participants were provided cost feedback. The cost feedback was provided by one of two types of visual displays: (a) cost at the current joint action vector, or (b) cost in a local neighborhood of the current joint action vector. Our results demonstrate that AI adaptation rate can significantly affect human behavior, having the ability to shift the outcome between two game theoretic equilibrium. We observed that slow adaptation rates shift the outcome towards the Nash equilibrium, while fast rates shift the outcome towards the human-led Stackelberg equilibrium. The addition of localized cost information had the effect of shifting outcomes towards Nash, compared to the outcomes from cost information at only the current joint action vector. Future work will investigate other effects that influence the convergence of gradient descent games.

cs.AI

Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006

http://arxiv.org/abs/2408.14626v1

http://arxiv.org/abs/2408.14626v1

http://arxiv.org/pdf/2408.14626v1

2024-08-26

2024-08-26

This study explores the development of a hybrid deep convolutional neural network (DCNN) model enhanced by autoencoders and data augmentation techniques to predict critical heat flux (CHF) with high accuracy. By augmenting the original input features using three different autoencoder configurations, the model's predictive capabilities were significantly improved. The hybrid models were trained and tested on a dataset of 7225 samples, with performance metrics including the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), and normalized root-mean-squared error (NRMSE) used for evaluation. Among the tested models, the DCNN_3F-A2 configuration demonstrated the highest accuracy, achieving an R2 of 0.9908 during training and 0.9826 during testing, outperforming the base model and other augmented versions. These results suggest that the proposed hybrid approach, combining deep learning with feature augmentation, offers a robust solution for CHF prediction, with the potential to generalize across a wider range of conditions.

11 pages, 6 figures

cs.LG

On Centralized Critics in Multi-Agent Reinforcement Learning

http://arxiv.org/abs/2408.14597v1

http://arxiv.org/abs/2408.14597v1

http://arxiv.org/pdf/2408.14597v1

2024-08-26

2024-08-26

Centralized Training for Decentralized Execution where agents are trained offline in a centralized fashion and execute online in a decentralized manner, has become a popular approach in Multi-Agent Reinforcement Learning (MARL). In particular, it has become popular to develop actor-critic methods that train decentralized actors with a centralized critic where the centralized critic is allowed access global information of the entire system, including the true system state. Such centralized critics are possible given offline information and are not used for online execution. While these methods perform well in a number of domains and have become a de facto standard in MARL, using a centralized critic in this context has yet to be sufficiently analyzed theoretically or empirically. In this paper, we therefore formally analyze centralized and decentralized critic approaches, and analyze the effect of using state-based critics in partially observable environments. We derive theories contrary to the common intuition: critic centralization is not strictly beneficial, and using state values can be harmful. We further prove that, in particular, state-based critics can introduce unexpected bias and variance compared to history-based critics. Finally, we demonstrate how the theory applies in practice by comparing different forms of critics on a wide range of common multi-agent benchmarks. The experiments show practical issues such as the difficulty of representation learning with partial observability, which highlights why the theoretical problems are often overlooked in the literature.

Journal of Artificial Intelligence Research 77 (2023): 295-354

cs.AI

How to build trust in answers given by Generative AI for specific, and vague, financial questions

http://arxiv.org/abs/2408.14593v1

http://arxiv.org/abs/2408.14593v1

http://arxiv.org/pdf/2408.14593v1

2024-08-26

2024-08-26

Purpose: Generative artificial intelligence (GenAI) has progressed in its ability and has seen explosive growth in adoption. However, the consumer's perspective on its use, particularly in specific scenarios such as financial advice, is unclear. This research develops a model of how to build trust in the advice given by GenAI when answering financial questions. Design/methodology/approach: The model is tested with survey data using structural equation modelling (SEM) and multi-group analysis (MGA). The MGA compares two scenarios, one where the consumer makes a specific question and one where a vague question is made. Findings: This research identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one. Humanness has a different effect in the two scenarios. When a financial question is specific, human-like interaction does not strengthen trust, while (1) when a question is vague, humanness builds trust. The four ways to build trust in both scenarios are (2) human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness and finally (5) ease of use and support. Originality/value: This research contributes to a better understanding of the consumer's perspective when using GenAI for financial questions and highlights the importance of understanding GenAI in specific contexts from specific stakeholders.

Journal of Electronic Business & Digital Economics, pp.1-15

10.1108/JEBDE-11-2023-0028

cs.HC

DIAGen: Diverse Image Augmentation with Generative Models

http://arxiv.org/abs/2408.14584v1

http://arxiv.org/abs/2408.14584v1

http://arxiv.org/pdf/2408.14584v1

2024-08-26

2024-08-26

Simple data augmentation techniques, such as rotations and flips, are widely used to enhance the generalization power of computer vision models. However, these techniques often fail to modify high-level semantic attributes of a class. To address this limitation, researchers have explored generative augmentation methods like the recently proposed DA-Fusion. Despite some progress, the variations are still largely limited to textural changes, thus falling short on aspects like varied viewpoints, environment, weather conditions, or even class-level semantic attributes (eg, variations in a dog's breed). To overcome this challenge, we propose DIAGen, building upon DA-Fusion. First, we apply Gaussian noise to the embeddings of an object learned with Textual Inversion to diversify generations using a pre-trained diffusion model's knowledge. Second, we exploit the general knowledge of a text-to-text generative model to guide the image generation of the diffusion model with varied class-specific prompts. Finally, we introduce a weighting mechanism to mitigate the impact of poorly generated samples. Experimental results across various datasets show that DIAGen not only enhances semantic diversity but also improves the performance of subsequent classifiers. The advantages of DIAGen over standard augmentations and the DA-Fusion baseline are particularly pronounced with out-of-distribution samples.

Accepted for publication in GCPR 2024

cs.CV

EVINCE: Optimizing Adversarial LLM Dialogues via Conditional Statistics and Information Theory

http://arxiv.org/abs/2408.14575v1

http://arxiv.org/abs/2408.14575v1

http://arxiv.org/pdf/2408.14575v1

2024-08-26

2024-08-26

This paper introduces EVINCE (Entropy and Variation IN Conditional Exchanges), a dialogue framework advancing Artificial General Intelligence (AGI) by enhancing versatility, adaptivity, and reasoning in large language models (LLMs). Leveraging adversarial debate and a novel dual entropy theory, EVINCE improves prediction accuracy, robustness, and stability in LLMs by integrating statistical modeling, information theory, and machine learning to balance diverse perspective exploration with strong prior exploitation. The framework's effectiveness is demonstrated through consistent convergence of information-theoretic metrics, particularly improved mutual information, fostering productive LLM collaboration. We apply EVINCE to healthcare, showing improved disease diagnosis, and discuss its broader implications for decision-making across domains. This work provides theoretical foundations and empirical validation for EVINCE, paving the way for advancements in LLM collaboration and AGI development.

19 pages, 7 figures, four tables

cs.AI

CURLoRA: Stable LLM Continual Fine-Tuning and Catastrophic Forgetting Mitigation

http://arxiv.org/abs/2408.14572v1

http://arxiv.org/abs/2408.14572v1

http://arxiv.org/pdf/2408.14572v1

2024-08-26

2024-08-26

This paper introduces CURLoRA, a novel approach to fine-tuning large language models (LLMs) that leverages CUR matrix decomposition in the context of Low-Rank Adaptation (LoRA). Our method addresses two critical challenges in LLM fine-tuning: mitigating catastrophic forgetting during continual learning and reducing the number of trainable parameters. We propose a unique modification to the CUR decomposition process, utilizing inverted probabilities for column and row selection which acts as an implicit regularization, and initializing the $U$ matrix as a zero matrix, and only fine-tuning it. We demonstrate through experiments on multiple datasets that CURLoRA outperforms standard LoRA in mitigating catastrophic forgetting. It maintains model stability and performance across tasks while significantly reducing the number of trainable parameters. Our results show that CURLoRA achieves very good and stable task accuracy while maintaining base model's perplexity scores fixed compared to LoRA upon continual fine-tuning, particularly in scenarios with limited data.

Code available at https://github.com/MNoorFawi/curlora

10.5281/zenodo.12730055

cs.LG

Improving Clinical Note Generation from Complex Doctor-Patient Conversation

http://arxiv.org/abs/2408.14568v1

http://arxiv.org/abs/2408.14568v1

http://arxiv.org/pdf/2408.14568v1

2024-08-26

2024-08-26

Writing clinical notes and documenting medical exams is a critical task for healthcare professionals, serving as a vital component of patient care documentation. However, manually writing these notes is time-consuming and can impact the amount of time clinicians can spend on direct patient interaction and other tasks. Consequently, the development of automated clinical note generation systems has emerged as a clinically meaningful area of research within AI for health. In this paper, we present three key contributions to the field of clinical note generation using large language models (LLMs). First, we introduce CliniKnote, a comprehensive dataset consisting of 1,200 complex doctor-patient conversations paired with their full clinical notes. This dataset, created and curated by medical experts with the help of modern neural networks, provides a valuable resource for training and evaluating models in clinical note generation tasks. Second, we propose the K-SOAP (Keyword, Subjective, Objective, Assessment, and Plan) note format, which enhances traditional SOAP~\cite{podder2023soap} (Subjective, Objective, Assessment, and Plan) notes by adding a keyword section at the top, allowing for quick identification of essential information. Third, we develop an automatic pipeline to generate K-SOAP notes from doctor-patient conversations and benchmark various modern LLMs using various metrics. Our results demonstrate significant improvements in efficiency and performance compared to standard LLM finetuning methods.

cs.CL

A Survey of Camouflaged Object Detection and Beyond

http://arxiv.org/abs/2408.14562v1

http://arxiv.org/abs/2408.14562v1

http://arxiv.org/pdf/2408.14562v1

2024-08-26

2024-08-26

Camouflaged Object Detection (COD) refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings, posing a significant challenge for computer vision systems. In recent years, COD has garnered widespread attention due to its potential applications in surveillance, wildlife conservation, autonomous systems, and more. While several surveys on COD exist, they often have limitations in terms of the number and scope of papers covered, particularly regarding the rapid advancements made in the field since mid-2023. To address this void, we present the most comprehensive review of COD to date, encompassing both theoretical frameworks and practical contributions to the field. This paper explores various COD methods across four domains, including both image-level and video-level solutions, from the perspectives of traditional and deep learning approaches. We thoroughly investigate the correlations between COD and other camouflaged scenario methods, thereby laying the theoretical foundation for subsequent analyses. Beyond object-level detection, we also summarize extended methods for instance-level tasks, including camouflaged instance segmentation, counting, and ranking. Additionally, we provide an overview of commonly used benchmarks and evaluation metrics in COD tasks, conducting a comprehensive evaluation of deep learning-based techniques in both image and video domains, considering both qualitative and quantitative performance. Finally, we discuss the limitations of current COD models and propose 9 promising directions for future research, focusing on addressing inherent challenges and exploring novel, meaningful technologies. For those interested, a curated list of COD-related techniques, datasets, and additional resources can be found at https://github.com/ChunmingHe/awesome-concealed-object-segmentation

26 pages, 10 figures, 8 tables

cs.CV

Revisiting Image Captioning Training Paradigm via Direct CLIP-based Optimization

http://arxiv.org/abs/2408.14547v1

http://arxiv.org/abs/2408.14547v1

http://arxiv.org/pdf/2408.14547v1

2024-08-26

2024-08-26

The conventional training approach for image captioning involves pre-training a network using teacher forcing and subsequent fine-tuning with Self-Critical Sequence Training to maximize hand-crafted captioning metrics. However, when attempting to optimize modern and higher-quality metrics like CLIP-Score and PAC-Score, this training method often encounters instability and fails to acquire the genuine descriptive capabilities needed to produce fluent and informative captions. In this paper, we propose a new training paradigm termed Direct CLIP-Based Optimization (DiCO). Our approach jointly learns and optimizes a reward model that is distilled from a learnable captioning evaluator with high human correlation. This is done by solving a weighted classification problem directly inside the captioner. At the same time, DiCO prevents divergence from the original model, ensuring that fluency is maintained. DiCO not only exhibits improved stability and enhanced quality in the generated captions but also aligns more closely with human preferences compared to existing methods, especially in modern metrics. Additionally, it maintains competitive performance in traditional metrics. Our source code and trained models are publicly available at https://github.com/aimagelab/DiCO.

BMVC 2024

cs.CV

Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning

http://arxiv.org/abs/2408.14472v1

http://arxiv.org/abs/2408.14472v1

http://arxiv.org/pdf/2408.14472v1

2024-08-26

2024-08-26

Humanoid robots, with their human-like skeletal structure, are especially suited for tasks in human-centric environments. However, this structure is accompanied by additional challenges in locomotion controller design, especially in complex real-world environments. As a result, existing humanoid robots are limited to relatively simple terrains, either with model-based control or model-free reinforcement learning. In this work, we introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control, which demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains. All scenarios run the same learned neural network with zero-shot sim-to-real transfer, indicating the superior robustness and generalization capability of the proposed method.

Robotics: Science and Systems (RSS), 2024. (Best Paper Award Finalist)

cs.RO

K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences

http://arxiv.org/abs/2408.14468v1

http://arxiv.org/abs/2408.14468v1

http://arxiv.org/pdf/2408.14468v1

2024-08-26

2024-08-26

The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank models with human preferences. However, traditional Arena methods, while established, require an excessive number of comparisons for ranking to converge and are vulnerable to preference noise in voting, suggesting the need for better approaches tailored to contemporary evaluation challenges. In this paper, we introduce K-Sort Arena, an efficient and reliable platform based on a key insight: images and videos possess higher perceptual intuitiveness than texts, enabling rapid evaluation of multiple samples simultaneously. Consequently, K-Sort Arena employs K-wise comparisons, allowing K models to engage in free-for-all competitions, which yield much richer information than pairwise comparisons. To enhance the robustness of the system, we leverage probabilistic modeling and Bayesian updating techniques. We propose an exploration-exploitation-based matchmaking strategy to facilitate more informative comparisons. In our experiments, K-Sort Arena exhibits 16.3x faster convergence compared to the widely used ELO algorithm. To further validate the superiority and obtain a comprehensive leaderboard, we collect human feedback via crowdsourced evaluations of numerous cutting-edge text-to-image and text-to-video models. Thanks to its high efficiency, K-Sort Arena can continuously incorporate emerging models and update the leaderboard with minimal votes. Our project has undergone several months of internal testing and is now available at https://huggingface.co/spaces/ksort/K-Sort-Arena

Project page: https://huggingface.co/spaces/ksort/K-Sort-Arena

cs.AI

Temporal Ensemble Logic

http://arxiv.org/abs/2408.14443v1

http://arxiv.org/abs/2408.14443v1

http://arxiv.org/pdf/2408.14443v1

2024-08-26

2024-08-26

We introduce Temporal Ensemble Logic (TEL), a monadic, first-order modal logic for linear-time temporal reasoning. TEL includes primitive temporal constructs such as ``always up to $t$ time later'' ($\Box_t$), ``sometimes before $t$ time in the future'' ($\Diamond_t$), and ``$t$-time later'' $\varphi_t$. TEL has been motivated from the requirement for rigor and reproducibility for cohort specification and discovery in clinical and population health research, to fill a gap in formalizing temporal reasoning in biomedicine. In this paper, we first introduce TEL in a general set up, with discrete and dense time as special cases. We then focus on the theoretical development of discrete TEL on the temporal domain of positive integers $\mathbb{N}^+$, denoted as ${\rm TEL}_{\mathbb{N}^+}$. ${\rm TEL}_{\mathbb{N}^+}$ is strictly more expressive than the standard monadic second order logic, characterized by B\"{u}chi automata. We present its formal semantics, a proof system, and provide a proof for the undecidability of the satisfiability of ${\rm TEL}_{\mathbb{N}^+}$. We also discuss expressiveness and decidability fragments for ${\rm TEL}_{\mathbb{N}^+}$, followed by illustrative applications.

47 pages, 2 figures

cs.LO

Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification

http://arxiv.org/abs/2408.14441v1

http://arxiv.org/abs/2408.14441v1

http://arxiv.org/pdf/2408.14441v1

2024-08-26

2024-08-26

Exploiting both audio and visual modalities for video classification is a challenging task, as the existing methods require large model architectures, leading to high computational complexity and resource requirements. Smaller architectures, on the other hand, struggle to achieve optimal performance. In this paper, we propose Attend-Fusion, an audio-visual (AV) fusion approach that introduces a compact model architecture specifically designed to capture intricate audio-visual relationships in video data. Through extensive experiments on the challenging YouTube-8M dataset, we demonstrate that Attend-Fusion achieves an F1 score of 75.64\% with only 72M parameters, which is comparable to the performance of larger baseline models such as Fully-Connected Late Fusion (75.96\% F1 score, 341M parameters). Attend-Fusion achieves similar performance to the larger baseline model while reducing the model size by nearly 80\%, highlighting its efficiency in terms of model complexity. Our work demonstrates that the Attend-Fusion model effectively combines audio and visual information for video classification, achieving competitive performance with significantly reduced model size. This approach opens new possibilities for deploying high-performance video understanding systems in resource-constrained environments across various applications.

cs.CV

Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview

http://arxiv.org/abs/2408.14437v1

http://arxiv.org/abs/2408.14437v1

http://arxiv.org/pdf/2408.14437v1

2024-08-26

2024-08-26

Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuromorphic systems that fully exploit the computational advantages of sparse SNNs.

IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC 2024)

cs.AR

Social perception of faces in a vision-language model

http://arxiv.org/abs/2408.14435v1

http://arxiv.org/abs/2408.14435v1

http://arxiv.org/pdf/2408.14435v1

2024-08-26

2024-08-26

We explore social perception of human faces in CLIP, a widely used open-source vision-language model. To this end, we compare the similarity in CLIP embeddings between different textual prompts and a set of face images. Our textual prompts are constructed from well-validated social psychology terms denoting social perception. The face images are synthetic and are systematically and independently varied along six dimensions: the legally protected attributes of age, gender, and race, as well as facial expression, lighting, and pose. Independently and systematically manipulating face attributes allows us to study the effect of each on social perception and avoids confounds that can occur in wild-collected data due to uncontrolled systematic correlations between attributes. Thus, our findings are experimental rather than observational. Our main findings are three. First, while CLIP is trained on the widest variety of images and texts, it is able to make fine-grained human-like social judgments on face images. Second, age, gender, and race do systematically impact CLIP's social perception of faces, suggesting an undesirable bias in CLIP vis-a-vis legally protected attributes. Most strikingly, we find a strong pattern of bias concerning the faces of Black women, where CLIP produces extreme values of social perception across different ages and facial expressions. Third, facial expression impacts social perception more than age and lighting as much as age. The last finding predicts that studies that do not control for unprotected visual attributes may reach the wrong conclusions on bias. Our novel method of investigation, which is founded on the social psychology literature and on the experiments involving the manipulation of individual attributes, yields sharper and more reliable observations than previous observational methods and may be applied to study biases in any vision-language model.

cs.CV

Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications

http://arxiv.org/abs/2408.14432v2

http://arxiv.org/abs/2408.14432v2

http://arxiv.org/pdf/2408.14432v2

2024-08-26

2024-08-28

Contextual bandits serve as a fundamental algorithmic framework for optimizing recommendation decisions online. Though extensive attention has been paid to tailoring contextual bandits for recommendation applications, the "herding effects" in user feedback have been ignored. These herding effects bias user feedback toward historical ratings, breaking down the assumption of unbiased feedback inherent in contextual bandits. This paper develops a novel variant of the contextual bandit that is tailored to address the feedback bias caused by the herding effects. A user feedback model is formulated to capture this feedback bias. We design the TS-Conf (Thompson Sampling under Conformity) algorithm, which employs posterior sampling to balance the exploration and exploitation tradeoff. We prove an upper bound for the regret of the algorithm, revealing the impact of herding effects on learning speed. Extensive experiments on datasets demonstrate that TS-Conf outperforms four benchmark algorithms. Analysis reveals that TS-Conf effectively mitigates the negative impact of herding effects, resulting in faster learning and improved recommendation accuracy.

Published as a conference paper at PRICAI 2024

cs.LG

CHARTOM: A Visual Theory-of-Mind Benchmark for Multimodal Large Language Models

http://arxiv.org/abs/2408.14419v1

http://arxiv.org/abs/2408.14419v1

http://arxiv.org/pdf/2408.14419v1

2024-08-26

2024-08-26

We introduce CHARTOM, a visual theory-of-mind benchmark for multimodal large language models. CHARTOM consists of specially designed data visualizing charts. Given a chart, a language model needs to not only correctly comprehend the chart (the FACT question) but also judge if the chart will be misleading to a human reader (the MIND question). Both questions have significant societal benefits. We detail the construction of the CHARTOM benchmark including its calibration on human performance.

cs.AI

MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues

http://arxiv.org/abs/2408.14418v1

http://arxiv.org/abs/2408.14418v1

http://arxiv.org/pdf/2408.14418v1

2024-08-26

2024-08-26

Automatic Speech Recognition (ASR) systems are pivotal in transcribing speech into text, yet the errors they introduce can significantly degrade the performance of downstream tasks like summarization. This issue is particularly pronounced in clinical dialogue summarization, a low-resource domain where supervised data for fine-tuning is scarce, necessitating the use of ASR models as black-box solutions. Employing conventional data augmentation for enhancing the noise robustness of summarization models is not feasible either due to the unavailability of sufficient medical dialogue audio recordings and corresponding ASR transcripts. To address this challenge, we propose MEDSAGE, an approach for generating synthetic samples for data augmentation using Large Language Models (LLMs). Specifically, we leverage the in-context learning capabilities of LLMs and instruct them to generate ASR-like errors based on a few available medical dialogue examples with audio recordings. Experimental results show that LLMs can effectively model ASR noise, and incorporating this noisy data into the training process significantly improves the robustness and accuracy of medical dialogue summarization systems. This approach addresses the challenges of noisy ASR outputs in critical applications, offering a robust solution to enhance the reliability of clinical dialogue summarization.

cs.CL

Language-specific Calibration for Pruning Multilingual Language Models

http://arxiv.org/abs/2408.14398v2

http://arxiv.org/abs/2408.14398v2

http://arxiv.org/pdf/2408.14398v2

2024-08-26

2024-08-28

Recent advances in large language model (LLM) pruning have shown state-of-the-art compression results in post-training and retraining-free settings while maintaining high predictive performance. However, such research mainly considers calibrating pruning using English text, despite the multilingual nature of modern LLMs and their frequent uses in non-English languages. In this paper, we set out to explore effective strategies for calibrating the pruning of multilingual language models. We present the first comprehensive empirical study, comparing different calibration languages for pruning multilingual models across diverse tasks, models, and state-of-the-art pruning techniques. Our results present practical suggestions, for example, calibrating in the target language can efficiently yield lower perplexity, but does not necessarily benefit downstream tasks. Our further analysis experiments unveil that calibration in the target language mainly contributes to preserving language-specific features related to fluency and coherence, but might not contribute to capturing language-agnostic features such as language understanding and reasoning. Last, we provide practical recommendations for future practitioners.

cs.CL

Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphs

http://arxiv.org/abs/2408.14397v1

http://arxiv.org/abs/2408.14397v1

http://arxiv.org/pdf/2408.14397v1

2024-08-26

2024-08-26

Recent advancements in artificial intelligence have significantly improved the automatic generation of radiology reports. However, existing evaluation methods fail to reveal the models' understanding of radiological images and their capacity to achieve human-level granularity in descriptions. To bridge this gap, we introduce a system, named ReXKG, which extracts structured information from processed reports to construct a comprehensive radiology knowledge graph. We then propose three metrics to evaluate the similarity of nodes (ReXKG-NSC), distribution of edges (ReXKG-AMS), and coverage of subgraphs (ReXKG-SCS) across various knowledge graphs. We conduct an in-depth comparative analysis of AI-generated and human-written radiology reports, assessing the performance of both specialist and generalist models. Our study provides a deeper understanding of the capabilities and limitations of current AI models in radiology report generation, offering valuable insights for improving model performance and clinical applicability.

Code is available at: https://github.com/rajpurkarlab/ReXKG

cs.AI

Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning

http://arxiv.org/abs/2408.14387v1

http://arxiv.org/abs/2408.14387v1

http://arxiv.org/pdf/2408.14387v1

2024-08-26

2024-08-26

Spatio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. However, existing methods are limited by their ability to handle large, complex datasets. To overcome this limitation, we introduce a hybrid approach that combines the strengths of open-source large and small-scale language models (LLMs and LMs) with traditional forecasting methods. We augment traditional methods with dynamic prompting and a grouped-query, multi-head attention mechanism to more effectively capture both intra-series and inter-series dependencies in evolving nonlinear time series data. In addition, we facilitate on-premises customization by fine-tuning smaller open-source LMs for time series trend analysis utilizing descriptions generated by open-source large LMs on consumer-grade hardware using Low-Rank Adaptation with Activation Memory Reduction (LoRA-AMR) technique to reduce computational overhead and activation storage memory demands while preserving inference latency. We combine language model processing for time series trend analysis with traditional time series representation learning method for cross-modal integration, achieving robust and accurate forecasts. The framework effectiveness is demonstrated through extensive experiments on various real-world datasets, outperforming existing methods by significant margins in terms of forecast accuracy.

Paper published at the Deployable AI (DAI) workshop at AAAI-2024

cs.LG

Probing Causality Manipulation of Large Language Models

http://arxiv.org/abs/2408.14380v1

http://arxiv.org/abs/2408.14380v1

http://arxiv.org/pdf/2408.14380v1

2024-08-26

2024-08-26

Large language models (LLMs) have shown various ability on natural language processing, including problems about causality. It is not intuitive for LLMs to command causality, since pretrained models usually work on statistical associations, and do not focus on causes and effects in sentences. So that probing internal manipulation of causality is necessary for LLMs. This paper proposes a novel approach to probe causality manipulation hierarchically, by providing different shortcuts to models and observe behaviors. We exploit retrieval augmented generation (RAG) and in-context learning (ICL) for models on a designed causality classification task. We conduct experiments on mainstream LLMs, including GPT-4 and some smaller and domain-specific models. Our results suggest that LLMs can detect entities related to causality and recognize direct causal relationships. However, LLMs lack specialized cognition for causality, merely treating them as part of the global semantic of the sentence.

cs.CL

SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery

http://arxiv.org/abs/2408.14371v1

http://arxiv.org/abs/2408.14371v1

http://arxiv.org/pdf/2408.14371v1

2024-08-26

2024-08-26

In this paper, we address Generalized Category Discovery, aiming to simultaneously uncover novel categories and accurately classify known ones. Traditional methods, which lean heavily on self-supervision and contrastive learning, often fall short when distinguishing between fine-grained categories. To address this, we introduce a novel concept called `self-expertise', which enhances the model's ability to recognize subtle differences and uncover unknown categories. Our approach combines unsupervised and supervised self-expertise strategies to refine the model's discernment and generalization. Initially, hierarchical pseudo-labeling is used to provide `soft supervision', improving the effectiveness of self-expertise. Our supervised technique differs from traditional methods by utilizing more abstract positive and negative samples, aiding in the formation of clusters that can generalize to novel categories. Meanwhile, our unsupervised strategy encourages the model to sharpen its category distinctions by considering within-category examples as `hard' negatives. Supported by theoretical insights, our empirical results showcase that our method outperforms existing state-of-the-art techniques in Generalized Category Discovery across several fine-grained datasets. Our code is available at: https://github.com/SarahRastegar/SelEx.

Accepted by ECCV 2024

cs.CV

GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal Conditioned Policy

http://arxiv.org/abs/2408.14368v1

http://arxiv.org/abs/2408.14368v1

http://arxiv.org/pdf/2408.14368v1

2024-08-26

2024-08-26

The robotics community has consistently aimed to achieve generalizable robot manipulation with flexible natural language instructions. One of the primary challenges is that obtaining robot data fully annotated with both actions and texts is time-consuming and labor-intensive. However, partially annotated data, such as human activity videos without action labels and robot play data without language labels, is much easier to collect. Can we leverage these data to enhance the generalization capability of robots? In this paper, we propose GR-MG, a novel method which supports conditioning on both a language instruction and a goal image. During training, GR-MG samples goal images from trajectories and conditions on both the text and the goal image or solely on the image when text is unavailable. During inference, where only the text is provided, GR-MG generates the goal image via a diffusion-based image-editing model and condition on both the text and the generated image. This approach enables GR-MG to leverage large amounts of partially annotated data while still using language to flexibly specify tasks. To generate accurate goal images, we propose a novel progress-guided goal image generation model which injects task progress information into the generation process, significantly improving the fidelity and the performance. In simulation experiments, GR-MG improves the average number of tasks completed in a row of 5 from 3.35 to 4.04. In real-robot experiments, GR-MG is able to perform 47 different tasks and improves the success rate from 62.5% to 75.0% and 42.4% to 57.6% in simple and generalization settings, respectively. Code and checkpoints will be available at the project page: https://gr-mg.github.io/.

9 pages, 7 figures, letter

cs.RO

SWE-bench-java: A GitHub Issue Resolving Benchmark for Java

http://arxiv.org/abs/2408.14354v1

http://arxiv.org/abs/2408.14354v1

http://arxiv.org/pdf/2408.14354v1

2024-08-26

2024-08-26

GitHub issue resolving is a critical task in software engineering, recently gaining significant attention in both industry and academia. Within this task, SWE-bench has been released to evaluate issue resolving capabilities of large language models (LLMs), but has so far only focused on Python version. However, supporting more programming languages is also important, as there is a strong demand in industry. As a first step toward multilingual support, we have developed a Java version of SWE-bench, called SWE-bench-java. We have publicly released the dataset, along with the corresponding Docker-based evaluation environment and leaderboard, which will be continuously maintained and updated in the coming months. To verify the reliability of SWE-bench-java, we implement a classic method SWE-agent and test several powerful LLMs on it. As is well known, developing a high-quality multi-lingual benchmark is time-consuming and labor-intensive, so we welcome contributions through pull requests or collaboration to accelerate its iteration and refinement, paving the way for fully automated programming.

This work is in progress

cs.SE

Assessing Contamination in Large Language Models: Introducing the LogProber method

http://arxiv.org/abs/2408.14352v1

http://arxiv.org/abs/2408.14352v1

http://arxiv.org/pdf/2408.14352v1

2024-08-26

2024-08-26

In machine learning, contamination refers to situations where testing data leak into the training set. The issue is particularly relevant for the evaluation of the performance of Large Language Models (LLMs), which are generally trained on gargantuan, and generally opaque, corpora of text scraped from the world wide web. Developing tools to detect contamination is therefore crucial to be able to fairly and properly track the evolution of the performance of LLMs. Most recent works in the field are not tailored to quantify contamination on short sequences of text like we find in psychology questionnaires. In the present paper we introduce LogProber, a novel, efficient, algorithm that we show able to detect contamination using token probability in given sentences. In the second part we investigate the limitations of the method and discuss how different training methods can contaminate models without leaving traces in the token probabilities.

cs.CL

Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses

http://arxiv.org/abs/2408.14527v1

http://arxiv.org/abs/2408.14527v1

http://arxiv.org/pdf/2408.14527v1

2024-08-26

2024-08-26

Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy simplifications, both on the environment and the agents, which make the resulting algorithms impractical for real-life scenarios. In this paper, we consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations. This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories fulfilling these tasks. To solve this MAPF problem, we propose an extension of the standard Prioritized Planning algorithm to deal with the inter-dependent tasks (Interleaved Prioritized Planning) and a novel Via-Point Star (VP*) algorithm to compute an optimal dynamics-compliant robot trajectory to visit a sequence of goal locations while avoiding moving obstacles. We prove the completeness of our approach and evaluate it in simulation as well as in a real warehouse.

Accepted to ECAI-2024. For related videos, see https://europe.naverlabs.com/research/publications/MAPF_IPP

cs.RO

Foundation Models for Music: A Survey

http://arxiv.org/abs/2408.14340v2

http://arxiv.org/abs/2408.14340v2

http://arxiv.org/pdf/2408.14340v2

2024-08-26

2024-08-27

In recent years, foundation models (FMs) such as large language models (LLMs) and latent diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This comprehensive review examines state-of-the-art (SOTA) pre-trained models and foundation models in music, spanning from representation learning, generative learning and multimodal learning. We first contextualise the significance of music in various industries and trace the evolution of AI in music. By delineating the modalities targeted by foundation models, we discover many of the music representations are underexplored in FM development. Then, emphasis is placed on the lack of versatility of previous methods on diverse music applications, along with the potential of FMs in music understanding, generation and medical application. By comprehensively exploring the details of the model pre-training paradigm, architectural choices, tokenisation, finetuning methodologies and controllability, we emphasise the important topics that should have been well explored, like instruction tuning and in-context learning, scaling law and emergent ability, as well as long-sequence modelling etc. A dedicated section presents insights into music agents, accompanied by a thorough analysis of datasets and evaluations essential for pre-training and downstream tasks. Finally, by underscoring the vital importance of ethical considerations, we advocate that following research on FM for music should focus more on such issues as interpretability, transparency, human responsibility, and copyright issues. The paper offers insights into future challenges and trends on FMs for music, aiming to shape the trajectory of human-AI collaboration in the music realm.

cs.SD

Machine Learning for Quantifier Selection in cvc5

http://arxiv.org/abs/2408.14338v1

http://arxiv.org/abs/2408.14338v1

http://arxiv.org/pdf/2408.14338v1

2024-08-26

2024-08-26

In this work we considerably improve the state-of-the-art SMT solving on first-order quantified problems by efficient machine learning guidance of quantifier selection. Quantifiers represent a significant challenge for SMT and are technically a source of undecidability. In our approach, we train an efficient machine learning model that informs the solver which quantifiers should be instantiated and which not. Each quantifier may be instantiated multiple times and the set of the active quantifiers changes as the solving progresses. Therefore, we invoke the ML predictor many times, during the whole run of the solver. To make this efficient, we use fast ML models based on gradient boosting decision trees. We integrate our approach into the state-of-the-art cvc5 SMT solver and show a considerable increase of the system's holdout-set performance after training it on a large set of first-order problems collected from the Mizar Mathematical Library.

cs.AI

Equivariant Reinforcement Learning under Partial Observability

http://arxiv.org/abs/2408.14336v1

http://arxiv.org/abs/2408.14336v1

http://arxiv.org/pdf/2408.14336v1

2024-08-26

2024-08-26

Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable domains where symmetries can be a useful inductive bias for efficient learning. Specifically, by encoding the equivariance regarding specific group symmetries into the neural networks, our actor-critic reinforcement learning agents can reuse solutions in the past for related scenarios. Consequently, our equivariant agents outperform non-equivariant approaches significantly in terms of sample efficiency and final performance, demonstrated through experiments on a range of robotic tasks in simulation and real hardware.

Conference on Robot Learning, 2023

cs.RO

PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset

http://arxiv.org/abs/2408.14329v1

http://arxiv.org/abs/2408.14329v1

http://arxiv.org/pdf/2408.14329v1

2024-08-26

2024-08-26

PHEVA, a Privacy-preserving Human-centric Ethical Video Anomaly detection dataset. By removing pixel information and providing only de-identified human annotations, PHEVA safeguards personally identifiable information. The dataset includes seven indoor/outdoor scenes, featuring one novel, context-specific camera, and offers over 5x the pose-annotated frames compared to the largest previous dataset. This study benchmarks state-of-the-art methods on PHEVA using a comprehensive set of metrics, including the 10% Error Rate (10ER), a metric used for anomaly detection for the first time providing insights relevant to real-world deployment. As the first of its kind, PHEVA bridges the gap between conventional training and real-world deployment by introducing continual learning benchmarks, with models outperforming traditional methods in 82.14% of cases. The dataset is publicly available at https://github.com/TeCSAR-UNCC/PHEVA.git.

cs.CV

Streamline tractography of the fetal brain in utero with machine learning

http://arxiv.org/abs/2408.14326v1

http://arxiv.org/abs/2408.14326v1

http://arxiv.org/pdf/2408.14326v1

2024-08-26

2024-08-26

Diffusion-weighted magnetic resonance imaging (dMRI) is the only non-invasive tool for studying white matter tracts and structural connectivity of the brain. These assessments rely heavily on tractography techniques, which reconstruct virtual streamlines representing white matter fibers. Much effort has been devoted to improving tractography methodology for adult brains, while tractography of the fetal brain has been largely neglected. Fetal tractography faces unique difficulties due to low dMRI signal quality, immature and rapidly developing brain structures, and paucity of reference data. This work presents the first machine learning model for fetal tractography. The model input consists of five sources of information: (1) Fiber orientation, inferred from a diffusion tensor fit to the dMRI signal; (2) Directions of recent propagation steps; (3) Global spatial information, encoded as distances to keypoints in the brain cortex; (4) Tissue segmentation information; and (5) Prior information about the expected local fiber orientations supplied with an atlas. In order to mitigate the local tensor estimation error, a large spatial context around the current point in the diffusion tensor image is encoded using convolutional and attention neural network modules. Moreover, the diffusion tensor information at a hypothetical next point is included in the model input. Filtering rules based on anatomically constrained tractography are applied to prune implausible streamlines. We trained the model on manually-refined whole-brain fetal tractograms and validated the trained model on an independent set of 11 test scans with gestational ages between 23 and 36 weeks. Results show that our proposed method achieves superior performance across all evaluated tracts. The new method can significantly advance the capabilities of dMRI for studying normal and abnormal brain development in utero.

cs.CV

Claim Verification in the Age of Large Language Models: A Survey

http://arxiv.org/abs/2408.14317v1

http://arxiv.org/abs/2408.14317v1

http://arxiv.org/pdf/2408.14317v1

2024-08-26

2024-08-26

The large and ever-increasing amount of data available on the Internet coupled with the laborious task of manual claim and fact verification has sparked the interest in the development of automated claim verification systems. Several deep learning and transformer-based models have been proposed for this task over the years. With the introduction of Large Language Models (LLMs) and their superior performance in several NLP tasks, we have seen a surge of LLM-based approaches to claim verification along with the use of novel methods such as Retrieval Augmented Generation (RAG). In this survey, we present a comprehensive account of recent claim verification frameworks using LLMs. We describe the different components of the claim verification pipeline used in these frameworks in detail including common approaches to retrieval, prompting, and fine-tuning. Finally, we describe publicly available English datasets created for this task.

cs.CL

Logic interpretations of ANN partition cells

http://arxiv.org/abs/2408.14314v1

http://arxiv.org/abs/2408.14314v1

http://arxiv.org/pdf/2408.14314v1

2024-08-26

2024-08-26

Consider a binary classification problem solved using a feed-forward artificial neural network (ANN). Let the ANN be composed of a ReLU layer and several linear layers (convolution, sum-pooling, or fully connected). We assume the network was trained with high accuracy. Despite numerous suggested approaches, interpreting an artificial neural network remains challenging for humans. For a new method of interpretation, we construct a bridge between a simple ANN and logic. As a result, we can analyze and manipulate the semantics of an ANN using the powerful tool set of logic. To achieve this, we decompose the input space of the ANN into several network partition cells. Each network partition cell represents a linear combination that maps input values to a classifying output value. For interpreting the linear map of a partition cell using logic expressions, we suggest minterm values as the input of a simple ANN. We derive logic expressions representing interaction patterns for separating objects classified as 1 from those classified as 0. To facilitate an interpretation of logic expressions, we present them as binary logic trees.

cs.LO

LLM-3D Print: Large Language Models To Monitor and Control 3D Printing

http://arxiv.org/abs/2408.14307v1

http://arxiv.org/abs/2408.14307v1

http://arxiv.org/pdf/2408.14307v1

2024-08-26

2024-08-26

Industry 4.0 has revolutionized manufacturing by driving digitalization and shifting the paradigm toward additive manufacturing (AM). Fused Deposition Modeling (FDM), a key AM technology, enables the creation of highly customized, cost-effective products with minimal material waste through layer-by-layer extrusion, posing a significant challenge to traditional subtractive methods. However, the susceptibility of material extrusion techniques to errors often requires expert intervention to detect and mitigate defects that can severely compromise product quality. While automated error detection and machine learning models exist, their generalizability across diverse 3D printer setups, firmware, and sensors is limited, and deep learning methods require extensive labeled datasets, hindering scalability and adaptability. To address these challenges, we present a process monitoring and control framework that leverages pre-trained Large Language Models (LLMs) alongside 3D printers to detect and address printing defects. The LLM evaluates print quality by analyzing images captured after each layer or print segment, identifying failure modes and querying the printer for relevant parameters. It then generates and executes a corrective action plan. We validated the effectiveness of the proposed framework in identifying defects by comparing it against a control group of engineers with diverse AM expertise. Our evaluation demonstrated that LLM-based agents not only accurately identify common 3D printing errors, such as inconsistent extrusion, stringing, warping, and layer adhesion, but also effectively determine the parameters causing these failures and autonomously correct them without any need for human intervention.

cs.CL

May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels

http://arxiv.org/abs/2408.14284v1

http://arxiv.org/abs/2408.14284v1

http://arxiv.org/pdf/2408.14284v1

2024-08-26

2024-08-26

Forgetting presents a significant challenge during incremental training, making it particularly demanding for contemporary AI systems to assimilate new knowledge in streaming data environments. To address this issue, most approaches in Continual Learning (CL) rely on the replay of a restricted buffer of past data. However, the presence of noise in real-world scenarios, where human annotation is constrained by time limitations or where data is automatically gathered from the web, frequently renders these strategies vulnerable. In this study, we address the problem of CL under Noisy Labels (CLN) by introducing Alternate Experience Replay (AER), which takes advantage of forgetting to maintain a clear distinction between clean, complex, and noisy samples in the memory buffer. The idea is that complex or mislabeled examples, which hardly fit the previously learned data distribution, are most likely to be forgotten. To grasp the benefits of such a separation, we equip AER with Asymmetric Balanced Sampling (ABS): a new sample selection strategy that prioritizes purity on the current task while retaining relevant samples from the past. Through extensive computational comparisons, we demonstrate the effectiveness of our approach in terms of both accuracy and purity of the obtained buffer, resulting in a remarkable average gain of 4.71% points in accuracy with respect to existing loss-based purification strategies. Code is available at https://github.com/aimagelab/mammoth.

25 pages, 5 figures. Accepted at the The 35th British Machine Vision Conference 2024 (BMVC 2024), Glasgow, UK

cs.LG

Uncertainties of Latent Representations in Computer Vision

http://arxiv.org/abs/2408.14281v1

http://arxiv.org/abs/2408.14281v1

http://arxiv.org/pdf/2408.14281v1

2024-08-26

2024-08-26

Uncertainty quantification is a key pillar of trustworthy machine learning. It enables safe reactions under unsafe inputs, like predicting only when the machine learning model detects sufficient evidence, discarding anomalous data, or emitting warnings when an error is likely to be inbound. This is particularly crucial in safety-critical areas like medical image classification or self-driving cars. Despite the plethora of proposed uncertainty quantification methods achieving increasingly higher scores on performance benchmarks, uncertainty estimates are often shied away from in practice. Many machine learning projects start from pretrained latent representations that come without uncertainty estimates. Uncertainties would need to be trained by practitioners on their own, which is notoriously difficult and resource-intense. This thesis makes uncertainty estimates easily accessible by adding them to the latent representation vectors of pretrained computer vision models. Besides proposing approaches rooted in probability and decision theory, such as Monte-Carlo InfoNCE (MCInfoNCE) and loss prediction, we delve into both theoretical and empirical questions. We show that these unobservable uncertainties about unobservable latent representations are indeed provably correct. We also provide an uncertainty-aware representation learning (URL) benchmark to compare these unobservables against observable ground-truths. Finally, we compile our findings to pretrain lightweight representation uncertainties on large-scale computer vision models that transfer to unseen datasets in a zero-shot manner. Our findings do not only advance the current theoretical understanding of uncertainties over latent variables, but also facilitate the access to uncertainty quantification for future researchers inside and outside the field, enabling straightforward but trustworthy machine learning.

Doctoral thesis

10.15496/publikation-98103

cs.LG

Estimating Uncertainty with Implicit Quantile Network

http://arxiv.org/abs/2408.14525v1

http://arxiv.org/abs/2408.14525v1

http://arxiv.org/pdf/2408.14525v1

2024-08-26

2024-08-26

Uncertainty quantification is an important part of many performance critical applications. This paper provides a simple alternative to existing approaches such as ensemble learning and bayesian neural networks. By directly modeling the loss distribution with an Implicit Quantile Network, we get an estimate of how uncertain the model is of its predictions. For experiments with MNIST and CIFAR datasets, the mean of the estimated loss distribution is 2x higher for incorrect predictions. When data with high estimated uncertainty is removed from the test dataset, the accuracy of the model goes up as much as 10%. This method is simple to implement while offering important information to applications where the user has to know when the model could be wrong (e.g. deep learning for healthcare).

This method is simple to implement and offers important information for performance critical applications

cs.LG

Text3DAug -- Prompted Instance Augmentation for LiDAR Perception

http://arxiv.org/abs/2408.14253v2

http://arxiv.org/abs/2408.14253v2

http://arxiv.org/pdf/2408.14253v2

2024-08-26

2024-08-27

LiDAR data of urban scenarios poses unique challenges, such as heterogeneous characteristics and inherent class imbalance. Therefore, large-scale datasets are necessary to apply deep learning methods. Instance augmentation has emerged as an efficient method to increase dataset diversity. However, current methods require the time-consuming curation of 3D models or costly manual data annotation. To overcome these limitations, we propose Text3DAug, a novel approach leveraging generative models for instance augmentation. Text3DAug does not depend on labeled data and is the first of its kind to generate instances and annotations from text. This allows for a fully automated pipeline, eliminating the need for manual effort in practical applications. Additionally, Text3DAug is sensor agnostic and can be applied regardless of the LiDAR sensor used. Comprehensive experimental analysis on LiDAR segmentation, detection and novel class discovery demonstrates that Text3DAug is effective in supplementing existing methods or as a standalone method, performing on par or better than established methods, however while overcoming their specific drawbacks. The code is publicly available.

Accepted at the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

cs.CV

Beyond Few-shot Object Detection: A Detailed Survey

http://arxiv.org/abs/2408.14249v1

http://arxiv.org/abs/2408.14249v1

http://arxiv.org/pdf/2408.14249v1

2024-08-26

2024-08-26

Object detection is a critical field in computer vision focusing on accurately identifying and locating specific objects in images or videos. Traditional methods for object detection rely on large labeled training datasets for each object category, which can be time-consuming and expensive to collect and annotate. To address this issue, researchers have introduced few-shot object detection (FSOD) approaches that merge few-shot learning and object detection principles. These approaches allow models to quickly adapt to new object categories with only a few annotated samples. While traditional FSOD methods have been studied before, this survey paper comprehensively reviews FSOD research with a specific focus on covering different FSOD settings such as standard FSOD, generalized FSOD, incremental FSOD, open-set FSOD, and domain adaptive FSOD. These approaches play a vital role in reducing the reliance on extensive labeled datasets, particularly as the need for efficient machine learning models continues to rise. This survey paper aims to provide a comprehensive understanding of the above-mentioned few-shot settings and explore the methodologies for each FSOD task. It thoroughly compares state-of-the-art methods across different FSOD settings, analyzing them in detail based on their evaluation protocols. Additionally, it offers insights into their applications, challenges, and potential future directions in the evolving field of object detection with limited data.

43 pages, 8 figures

cs.CV

Celtibero: Robust Layered Aggregation for Federated Learning

http://arxiv.org/abs/2408.14240v1

http://arxiv.org/abs/2408.14240v1

http://arxiv.org/pdf/2408.14240v1

2024-08-26

2024-08-26

Federated Learning (FL) is an innovative approach to distributed machine learning. While FL offers significant privacy advantages, it also faces security challenges, particularly from poisoning attacks where adversaries deliberately manipulate local model updates to degrade model performance or introduce hidden backdoors. Existing defenses against these attacks have been shown to be effective when the data on the nodes is identically and independently distributed (i.i.d.), but they often fail under less restrictive, non-i.i.d data conditions. To overcome these limitations, we introduce Celtibero, a novel defense mechanism that integrates layered aggregation to enhance robustness against adversarial manipulation. Through extensive experiments on the MNIST and IMDB datasets, we demonstrate that Celtibero consistently achieves high main task accuracy (MTA) while maintaining minimal attack success rates (ASR) across a range of untargeted and targeted poisoning attacks. Our results highlight the superiority of Celtibero over existing defenses such as FL-Defender, LFighter, and FLAME, establishing it as a highly effective solution for securing federated learning systems against sophisticated poisoning attacks.

cs.CR

DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for type classification

http://arxiv.org/abs/2408.14236v1

http://arxiv.org/abs/2408.14236v1

http://arxiv.org/pdf/2408.14236v1

2024-08-26

2024-08-26

We introduce semantic towers, an extrinsic knowledge representation method, and compare it to intrinsic knowledge in large language models for ontology learning. Our experiments show a trade-off between performance and semantic grounding for extrinsic knowledge compared to a fine-tuned model intrinsic knowledge. We report our findings on the Large Language Models for Ontology Learning (LLMs4OL) 2024 challenge.

8 pages, 4 figures, accepted for the LLMs4OL challenge at the International Semantic Web Conference (ISWC) 2024

cs.CL

Gallery-Aware Uncertainty Estimation For Open-Set Face Recognition

http://arxiv.org/abs/2408.14229v1

http://arxiv.org/abs/2408.14229v1

http://arxiv.org/pdf/2408.14229v1

2024-08-26

2024-08-26

Accurately estimating image quality and model robustness improvement are critical challenges in unconstrained face recognition, which can be addressed through uncertainty estimation via probabilistic face embeddings. Previous research mainly focused on uncertainty estimation in face verification, leaving the open-set face recognition task underexplored. In open-set face recognition, one seeks to classify an image, which could also be unknown. Here, the low variance of probabilistic embedding does not imply a low error probability: an image embedding could be close to several classes in a gallery, thus yielding high uncertainty. We propose a method aware of two sources of ambiguity in the open-set recognition system: (1) the gallery uncertainty caused by overlapping classes and (2) the uncertainty of the face embeddings. To detect both types, we use a Bayesian probabilistic model of embedding distribution, which provides a principled uncertainty estimate. Challenging open-set face recognition datasets, such as IJB-C, serve as a testbed for our method. We also propose a new open-set recognition protocol for whale and dolphin identification. The proposed approach better identifies recognition errors than uncertainty estimation methods based solely on image quality.

cs.CV

Fact Probability Vector Based Goal Recognition

http://arxiv.org/abs/2408.14224v1

http://arxiv.org/abs/2408.14224v1

http://arxiv.org/pdf/2408.14224v1

2024-08-26

2024-08-26

We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities. These probabilities depend on a specified goal g and initial state s0. Our method maps these probabilities and observed facts into a real vector space to compute heuristic values for potential goals. These values estimate the likelihood of a given goal being the true objective of the observed agent. As obtaining exact expected probabilities for observed facts in an observation sequence is often practically infeasible, we propose and empirically validate a method for approximating these probabilities. Our empirical results show that the proposed approach offers improved goal recognition precision compared to state-of-the-art techniques while reducing computational complexity.

Will be presented at ECAI 2024

cs.AI

MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement

http://arxiv.org/abs/2408.14211v1

http://arxiv.org/abs/2408.14211v1

http://arxiv.org/pdf/2408.14211v1

2024-08-26

2024-08-26

Existing works in single-image human reconstruction suffer from weak generalizability due to insufficient training data or 3D inconsistencies for a lack of comprehensive multi-view knowledge. In this paper, we introduce MagicMan, a human-specific multi-view diffusion model designed to generate high-quality novel view images from a single reference image. As its core, we leverage a pre-trained 2D diffusion model as the generative prior for generalizability, with the parametric SMPL-X model as the 3D body prior to promote 3D awareness. To tackle the critical challenge of maintaining consistency while achieving dense multi-view generation for improved 3D human reconstruction, we first introduce hybrid multi-view attention to facilitate both efficient and thorough information interchange across different views. Additionally, we present a geometry-aware dual branch to perform concurrent generation in both RGB and normal domains, further enhancing consistency via geometry cues. Last but not least, to address ill-shaped issues arising from inaccurate SMPL-X estimation that conflicts with the reference image, we propose a novel iterative refinement strategy, which progressively optimizes SMPL-X accuracy while enhancing the quality and consistency of the generated multi-views. Extensive experimental results demonstrate that our method significantly outperforms existing approaches in both novel view synthesis and subsequent 3D human reconstruction tasks.

Project Page: https://thuhcsi.github.io/MagicMan

cs.CV

Representative Arm Identification: A fixed confidence approach to identify cluster representatives

http://arxiv.org/abs/2408.14195v1

http://arxiv.org/abs/2408.14195v1

http://arxiv.org/pdf/2408.14195v1

2024-08-26

2024-08-26

We study the representative arm identification (RAI) problem in the multi-armed bandits (MAB) framework, wherein we have a collection of arms, each associated with an unknown reward distribution. An underlying instance is defined by a partitioning of the arms into clusters of predefined sizes, such that for any $j > i$, all arms in cluster $i$ have a larger mean reward than those in cluster $j$. The goal in RAI is to reliably identify a certain prespecified number of arms from each cluster, while using as few arm pulls as possible. The RAI problem covers as special cases several well-studied MAB problems such as identifying the best arm or any $M$ out of the top $K$, as well as both full and coarse ranking. We start by providing an instance-dependent lower bound on the sample complexity of any feasible algorithm for this setting. We then propose two algorithms, based on the idea of confidence intervals, and provide high probability upper bounds on their sample complexity, which orderwise match the lower bound. Finally, we do an empirical comparison of both algorithms along with an LUCB-type alternative on both synthetic and real-world datasets, and demonstrate the superior performance of our proposed schemes in most cases.

We analyse a clustered multi-armed bandit formulation, where the learning objective is to identify representative arms from each cluster, in a fixed confidence setting

cs.LG

DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models

http://arxiv.org/abs/2408.14185v1

http://arxiv.org/abs/2408.14185v1

http://arxiv.org/pdf/2408.14185v1

2024-08-26

2024-08-26

Real-time dynamic path planning in complex traffic environments presents challenges, such as varying traffic volumes and signal wait times. Traditional static routing algorithms like Dijkstra and A* compute shortest paths but often fail under dynamic conditions. Recent Reinforcement Learning (RL) approaches offer improvements but tend to focus on local optima, risking dead-ends or boundary issues. This paper proposes a novel approach based on causal inference for real-time dynamic path planning, balancing global and local optimality. We first use the static Dijkstra algorithm to compute a globally optimal baseline path. A distributed control strategy then guides vehicles along this path. At intersections, DynamicRouteGPT performs real-time decision-making for local path selection, considering real-time traffic, driving preferences, and unexpected events. DynamicRouteGPT integrates Markov chains, Bayesian inference, and large-scale pretrained language models like Llama3 8B to provide an efficient path planning solution. It dynamically adjusts to traffic scenarios and driver preferences and requires no pre-training, offering broad applicability across road networks. A key innovation is the construction of causal graphs for counterfactual reasoning, optimizing path decisions. Experimental results show that our method achieves state-of-the-art performance in real-time dynamic path planning for multiple vehicles while providing explainable path selections, offering a novel and efficient solution for complex traffic environments.

This paper is 12 pages long and represents the initial draft, version 1

cs.AI

Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning

http://arxiv.org/abs/2408.14183v1

http://arxiv.org/abs/2408.14183v1

http://arxiv.org/pdf/2408.14183v1

2024-08-26

2024-08-26

Efficient navigation in dynamic environments is crucial for autonomous robots interacting with various environmental entities, including both moving agents and static obstacles. In this study, we present a novel methodology that enhances the robot's interaction with different types of agents and obstacles based on specific safety requirements. This approach uses information about the entity types, improving collision avoidance and ensuring safer navigation. We introduce a new reward function that penalizes the robot for collisions with different entities such as adults, bicyclists, children, and static obstacles, and additionally encourages the robot's proximity to the goal. It also penalizes the robot for being close to entities, and the safe distance also depends on the entity type. Additionally, we propose an optimized algorithm for training and testing, which significantly accelerates train, validation, and test steps and enables training in complex environments. Comprehensive experiments conducted using simulation demonstrate that our approach consistently outperforms conventional navigation and collision avoidance methods, including state-of-the-art techniques. To sum up, this work contributes to enhancing the safety and efficiency of navigation systems for autonomous robots in dynamic, crowded environments.

14 pages, 5 figures

cs.RO

I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing

http://arxiv.org/abs/2408.14180v1

http://arxiv.org/abs/2408.14180v1

http://arxiv.org/pdf/2408.14180v1

2024-08-26

2024-08-26

Significant progress has been made in the field of Instruction-based Image Editing (IIE). However, evaluating these models poses a significant challenge. A crucial requirement in this field is the establishment of a comprehensive evaluation benchmark for accurately assessing editing results and providing valuable insights for its further development. In response to this need, we propose I2EBench, a comprehensive benchmark designed to automatically evaluate the quality of edited images produced by IIE models from multiple dimensions. I2EBench consists of 2,000+ images for editing, along with 4,000+ corresponding original and diverse instructions. It offers three distinctive characteristics: 1) Comprehensive Evaluation Dimensions: I2EBench comprises 16 evaluation dimensions that cover both high-level and low-level aspects, providing a comprehensive assessment of each IIE model. 2) Human Perception Alignment: To ensure the alignment of our benchmark with human perception, we conducted an extensive user study for each evaluation dimension. 3) Valuable Research Insights: By analyzing the advantages and disadvantages of existing IIE models across the 16 dimensions, we offer valuable research insights to guide future development in the field. We will open-source I2EBench, including all instructions, input images, human annotations, edited images from all evaluated methods, and a simple script for evaluating the results from new IIE models. The code, dataset and generated images from all IIE models are provided in github: https://github.com/cocoshe/I2EBench.

Tech report, 39 pages, 41 figures

cs.CV

SwiftBrush v2: Make Your One-step Diffusion Model Better Than Its Teacher

http://arxiv.org/abs/2408.14176v2

http://arxiv.org/abs/2408.14176v2

http://arxiv.org/pdf/2408.14176v2

2024-08-26

2024-08-27

In this paper, we aim to enhance the performance of SwiftBrush, a prominent one-step text-to-image diffusion model, to be competitive with its multi-step Stable Diffusion counterpart. Initially, we explore the quality-diversity trade-off between SwiftBrush and SD Turbo: the former excels in image diversity, while the latter excels in image quality. This observation motivates our proposed modifications in the training methodology, including better weight initialization and efficient LoRA training. Moreover, our introduction of a novel clamped CLIP loss enhances image-text alignment and results in improved image quality. Remarkably, by combining the weights of models trained with efficient LoRA and full training, we achieve a new state-of-the-art one-step diffusion model, achieving an FID of 8.14 and surpassing all GAN-based and multi-step Stable Diffusion models. The project page is available at https://swiftbrushv2.github.io.

Accepted to ECCV'24

cs.CV

Dynamic Pricing for Electric Vehicle Charging

http://arxiv.org/abs/2408.14169v1

http://arxiv.org/abs/2408.14169v1

http://arxiv.org/pdf/2408.14169v1

2024-08-26

2024-08-26

Dynamic pricing is a promising strategy to address the challenges of smart charging, as traditional time-of-use (ToU) rates and stationary pricing (SP) do not dynamically react to changes in operating conditions, reducing revenue for charging station (CS) vendors and affecting grid stability. Previous studies evaluated single objectives or linear combinations of objectives for EV CS pricing solutions, simplifying trade-offs and preferences among objectives. We develop a novel formulation for the dynamic pricing problem by addressing multiple conflicting objectives efficiently instead of solely focusing on one objective or metric, as in earlier works. We find optimal trade-offs or Pareto solutions efficiently using Non-dominated Sorting Genetic Algorithms (NSGA) II and NSGA III. A dynamic pricing model quantifies the relationship between demand and price while simultaneously solving multiple conflicting objectives, such as revenue, quality of service (QoS), and peak-to-average ratios (PAR). A single method can only address some of the above aspects of dynamic pricing comprehensively. We present a three-part dynamic pricing approach using a Bayesian model, multi-objective optimization, and multi-criteria decision-making (MCDM) using pseudo-weight vectors. To address the research gap in CS pricing, our method selects solutions using revenue, QoS, and PAR metrics simultaneously. Two California charging sites' real-world data validates our approach.

12 pages

cs.DC

Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning

http://arxiv.org/abs/2408.14158v1

http://arxiv.org/abs/2408.14158v1

http://arxiv.org/pdf/2408.14158v1

2024-08-26

2024-08-26

The rapid progress in Deep Learning (DL) and Large Language Models (LLMs) has exponentially increased demands of computational power and bandwidth. This, combined with the high costs of faster computing chips and interconnects, has significantly inflated High Performance Computing (HPC) construction costs. To address these challenges, we introduce the Fire-Flyer AI-HPC architecture, a synergistic hardware-software co-design framework and its best practices. For DL training, we deployed the Fire-Flyer 2 with 10,000 PCIe A100 GPUs, achieved performance approximating the DGX-A100 while reducing costs by half and energy consumption by 40%. We specifically engineered HFReduce to accelerate allreduce communication and implemented numerous measures to keep our Computation-Storage Integrated Network congestion-free. Through our software stack, including HaiScale, 3FS, and HAI-Platform, we achieved substantial scalability by overlapping computation and communication. Our system-oriented experience from DL training provides valuable insights to drive future advancements in AI-HPC.

This is the preprint version of the paper accepted for presentation at the 2024 International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'24). \c{opyright} 2024 IEEE. Personal use of this material is permitted. For other uses, permission from IEEE must be obtained. Please refer to IEEE Xplore for the final published version

cs.DC

Explaining Vision-Language Similarities in Dual Encoders with Feature-Pair Attributions

http://arxiv.org/abs/2408.14153v1

http://arxiv.org/abs/2408.14153v1

http://arxiv.org/pdf/2408.14153v1

2024-08-26

2024-08-26

Dual encoder architectures like CLIP models map two types of inputs into a shared embedding space and learn similarities between them. However, it is not understood how such models compare two inputs. Here, we address this research gap with two contributions. First, we derive a method to attribute predictions of any differentiable dual encoder onto feature-pair interactions between its inputs. Second, we apply our method to CLIP-type models and show that they learn fine-grained correspondences between parts of captions and regions in images. They match objects across input modes and also account for mismatches. However, this visual-linguistic grounding ability heavily varies between object classes, depends on the training data distribution, and largely improves after in-domain training. Using our method we can identify knowledge gaps about specific object classes in individual models and can monitor their improvement upon fine-tuning.

cs.CV

Exploring the Potential of Large Language Models for Heterophilic Graphs

http://arxiv.org/abs/2408.14134v1

http://arxiv.org/abs/2408.14134v1

http://arxiv.org/pdf/2408.14134v1

2024-08-26

2024-08-26

Graph Neural Networks (GNNs) are essential for various graph-based learning tasks. Notably, classical GNN architectures operate under the assumption of homophily, which posits that connected nodes are likely to share similar features. However, this assumption limits the effectiveness of GNNs in handling heterophilic graphs where connected nodes often exhibit dissimilar characteristics. Existing approaches for homophily graphs such as non-local neighbor extension and architectural refinement overlook the rich textual data associated with nodes, which could unlock deeper insights into these heterophilic contexts. With advancements in Large Language Models (LLMs), there is significant promise to enhance GNNs by leveraging the extensive open-world knowledge within LLMs to more effectively interpret and utilize textual data for characterizing heterophilic graphs. In this work, we explore the potential of LLMs for modeling heterophilic graphs and propose a novel two-stage framework: LLM-enhanced edge discriminator and LLM-guided edge reweighting. Specifically, in the first stage, we fine-tune the LLM to better identify homophilic and heterophilic edges based on the textual information of their nodes. In the second stage, we adaptively manage message propagation in GNNs for different edge types based on node features, structures, and heterophilic or homophilic characteristics. To cope with the computational demands when deploying LLMs in practical scenarios, we further explore model distillation techniques to fine-tune smaller, more efficient models that maintain competitive performance. Extensive experiments validate the effectiveness of our framework, demonstrating the feasibility of using LLMs to enhance GNNs for node classification on heterophilic graphs.

Under review

cs.LG

Retrieval Augmented Generation for Dynamic Graph Modeling

http://arxiv.org/abs/2408.14523v1

http://arxiv.org/abs/2408.14523v1

http://arxiv.org/pdf/2408.14523v1

2024-08-26

2024-08-26

Dynamic graph modeling is crucial for analyzing evolving patterns in various applications. Existing approaches often integrate graph neural networks with temporal modules or redefine dynamic graph modeling as a generative sequence task. However, these methods typically rely on isolated historical contexts of the target nodes from a narrow perspective, neglecting occurrences of similar patterns or relevant cases associated with other nodes. In this work, we introduce the Retrieval-Augmented Generation for Dynamic Graph Modeling (RAG4DyG) framework, which leverages guidance from contextually and temporally analogous examples to broaden the perspective of each node. This approach presents two critical challenges: (1) How to identify and retrieve high-quality demonstrations that are contextually and temporally analogous to dynamic graph samples? (2) How can these demonstrations be effectively integrated to improve dynamic graph modeling? To address these challenges, we propose RAG4DyG, which enriches the understanding of historical contexts by retrieving and learning from contextually and temporally pertinent demonstrations. Specifically, we employ a time- and context-aware contrastive learning module to identify and retrieve relevant cases for each query sequence. Moreover, we design a graph fusion strategy to integrate the retrieved cases, thereby augmenting the inherent historical contexts for improved prediction. Extensive experiments on real-world datasets across different domains demonstrate the effectiveness of RAG4DyG for dynamic graph modeling.

Under review

cs.LG

Contrastive Learning Subspace for Text Clustering

http://arxiv.org/abs/2408.14119v1

http://arxiv.org/abs/2408.14119v1

http://arxiv.org/pdf/2408.14119v1

2024-08-26

2024-08-26

Contrastive learning has been frequently investigated to learn effective representations for text clustering tasks. While existing contrastive learning-based text clustering methods only focus on modeling instance-wise semantic similarity relationships, they ignore contextual information and underlying relationships among all instances that needs to be clustered. In this paper, we propose a novel text clustering approach called Subspace Contrastive Learning (SCL) which models cluster-wise relationships among instances. Specifically, the proposed SCL consists of two main modules: (1) a self-expressive module that constructs virtual positive samples and (2) a contrastive learning module that further learns a discriminative subspace to capture task-specific cluster-wise relationships among texts. Experimental results show that the proposed SCL method not only has achieved superior results on multiple task clustering datasets but also has less complexity in positive sample construction.

cs.CL

Estimating Causal Effects from Learned Causal Networks

http://arxiv.org/abs/2408.14101v2

http://arxiv.org/abs/2408.14101v2

http://arxiv.org/pdf/2408.14101v2

2024-08-26

2024-08-27

The standard approach to answering an identifiable causal-effect query (e.g., $P(Y|do(X)$) when given a causal diagram and observational data is to first generate an estimand, or probabilistic expression over the observable variables, which is then evaluated using the observational data. In this paper, we propose an alternative paradigm for answering causal-effect queries over discrete observable variables. We propose to instead learn the causal Bayesian network and its confounding latent variables directly from the observational data. Then, efficient probabilistic graphical model (PGM) algorithms can be applied to the learned model to answer queries. Perhaps surprisingly, we show that this \emph{model completion} learning approach can be more effective than estimand approaches, particularly for larger models in which the estimand expressions become computationally difficult. We illustrate our method's potential using a benchmark collection of Bayesian networks and synthetically generated causal models.

cs.AI

Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects

http://arxiv.org/abs/2408.14090v1

http://arxiv.org/abs/2408.14090v1

http://arxiv.org/pdf/2408.14090v1

2024-08-26

2024-08-26

Multi-GPU nodes are increasingly common in the rapidly evolving landscape of exascale supercomputers. On these systems, GPUs on the same node are connected through dedicated networks, with bandwidths up to a few terabits per second. However, gauging performance expectations and maximizing system efficiency is challenging due to different technologies, design options, and software layers. This paper comprehensively characterizes three supercomputers - Alps, Leonardo, and LUMI - each with a unique architecture and design. We focus on performance evaluation of intra-node and inter-node interconnects on up to 4096 GPUs, using a mix of intra-node and inter-node benchmarks. By analyzing its limitations and opportunities, we aim to offer practical guidance to researchers, system architects, and software developers dealing with multi-GPU supercomputing. Our results show that there is untapped bandwidth, and there are still many opportunities for optimization, ranging from network to software optimization.

Published in Proceedings of The International Conference for High Performance Computing Networking, Storage, and Analysis (SC '24) (2024)

cs.DC

SONICS: Synthetic Or Not -- Identifying Counterfeit Songs

http://arxiv.org/abs/2408.14080v2

http://arxiv.org/abs/2408.14080v2

http://arxiv.org/pdf/2408.14080v2

2024-08-26

2024-08-27

The recent surge in AI-generated songs presents exciting possibilities and challenges. While these tools democratize music creation, they also necessitate the ability to distinguish between human-composed and AI-generated songs for safeguarding artistic integrity and content curation. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, this approach is inadequate for contemporary end-to-end AI-generated songs where all components (vocals, lyrics, music, and style) could be AI-generated. Additionally, existing datasets lack lyrics-music diversity, long-duration songs, and open fake songs. To address these gaps, we introduce SONICS, a novel dataset for end-to-end Synthetic Song Detection (SSD), comprising over 97k songs with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long-range temporal dependencies in songs for effective authenticity detection, an aspect overlooked in existing methods. To capture these patterns, we propose a novel model, SpecTTTra, that is up to 3 times faster and 6 times more memory efficient compared to popular CNN and Transformer-based models while maintaining competitive performance. Finally, we offer both AI-based and Human evaluation benchmarks, addressing another deficiency in current research.

cs.SD

Revisiting Vacuous Reduct Semantics for Abstract Argumentation (Extended Version)

http://arxiv.org/abs/2408.14069v1

http://arxiv.org/abs/2408.14069v1

http://arxiv.org/pdf/2408.14069v1

2024-08-26

2024-08-26

We consider the notion of a vacuous reduct semantics for abstract argumentation frameworks, which, given two abstract argumentation semantics {\sigma} and {\tau}, refines {\sigma} (base condition) by accepting only those {\sigma}-extensions that have no non-empty {\tau}-extension in their reduct (vacuity condition). We give a systematic overview on vacuous reduct semantics resulting from combining different admissibility-based and conflict-free semantics and present a principle-based analysis of vacuous reduct semantics in general. We provide criteria for the inheritance of principle satisfaction by a vacuous reduct semantics from its base and vacuity condition for established as well as recently introduced principles in the context of weak argumentation semantics. We also conduct a principle-based analysis for the special case of undisputed semantics.

The paper has been accepted at ECAI 2024, this is an extended version including proofs of technical results

cs.AI

HAPM -- Hardware Aware Pruning Method for CNN hardware accelerators in resource constrained devices

http://arxiv.org/abs/2408.14055v1

http://arxiv.org/abs/2408.14055v1

http://arxiv.org/pdf/2408.14055v1

2024-08-26

2024-08-26

During the last years, algorithms known as Convolutional Neural Networks (CNNs) had become increasingly popular, expanding its application range to several areas. In particular, the image processing field has experienced a remarkable advance thanks to this algorithms. In IoT, a wide research field aims to develop hardware capable of execute them at the lowest possible energy cost, but keeping acceptable image inference time. One can get around this apparently conflicting objectives by applying design and training techniques. The present work proposes a generic hardware architecture ready to be implemented on FPGA devices, supporting a wide range of configurations which allows the system to run different neural network architectures, dynamically exploiting the sparsity caused by pruning techniques in the mathematical operations present in this kind of algorithms. The inference speed of the design is evaluated over different resource constrained FPGA devices. Finally, the standard pruning algorithm is compared against a custom pruning technique specifically designed to exploit the scheduling properties of this hardware accelerator. We demonstrate that our hardware-aware pruning algorithm achieves a remarkable improvement of a 45 % in inference time compared to a network pruned using the standard algorithm.

8 pages, 7 figure, thesis for the title of Electronic Engineer attained in 2021 at the Universidad Tecnologica Nacional (UTN), Argentina

cs.AR

Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks

http://arxiv.org/abs/2408.14045v1

http://arxiv.org/abs/2408.14045v1

http://arxiv.org/pdf/2408.14045v1

2024-08-26

2024-08-26

In recent years, numerous large-scale cyberattacks have exploited Internet of Things (IoT) devices, a phenomenon that is expected to escalate with the continuing proliferation of IoT technology. Despite considerable efforts in attack detection, intrusion detection systems remain mostly reactive, responding to specific patterns or observed anomalies. This work proposes a proactive approach to anticipate and mitigate malicious activities before they cause damage. This paper proposes a novel network intrusion prediction framework that combines Large Language Models (LLMs) with Long Short Term Memory (LSTM) networks. The framework incorporates two LLMs in a feedback loop: a fine-tuned Generative Pre-trained Transformer (GPT) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) for evaluating the predicted traffic. The LSTM classifier model then identifies malicious packets among these predictions. Our framework, evaluated on the CICIoT2023 IoT attack dataset, demonstrates a significant improvement in predictive capabilities, achieving an overall accuracy of 98%, offering a robust solution to IoT cybersecurity challenges.

cs.CR

PAGE: Parametric Generative Explainer for Graph Neural Network

http://arxiv.org/abs/2408.14042v1

http://arxiv.org/abs/2408.14042v1

http://arxiv.org/pdf/2408.14042v1

2024-08-26

2024-08-26

This article introduces PAGE, a parameterized generative interpretive framework. PAGE is capable of providing faithful explanations for any graph neural network without necessitating prior knowledge or internal details. Specifically, we train the auto-encoder to generate explanatory substructures by designing appropriate training strategy. Due to the dimensionality reduction of features in the latent space of the auto-encoder, it becomes easier to extract causal features leading to the model's output, which can be easily employed to generate explanations. To accomplish this, we introduce an additional discriminator to capture the causality between latent causal features and the model's output. By designing appropriate optimization objectives, the well-trained discriminator can be employed to constrain the encoder in generating enhanced causal features. Finally, these features are mapped to substructures of the input graph through the decoder to serve as explanations. Compared to existing methods, PAGE operates at the sample scale rather than nodes or edges, eliminating the need for perturbation or encoding processes as seen in previous methods. Experimental results on both artificially synthesized and real-world datasets demonstrate that our approach not only exhibits the highest faithfulness and accuracy but also significantly outperforms baseline models in terms of efficiency.

cs.LG

Towards Graph Prompt Learning: A Survey and Beyond

http://arxiv.org/abs/2408.14520v1

http://arxiv.org/abs/2408.14520v1

http://arxiv.org/pdf/2408.14520v1

2024-08-26

2024-08-26

Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, image recognition, and multimodal retrieval. This approach fully leverages the potential of large-scale pre-trained models, reducing downstream data requirements and computational costs while enhancing model applicability across various tasks. Graphs, as versatile data structures that capture relationships between entities, play pivotal roles in fields such as social network analysis, recommender systems, and biological graphs. Despite the success of pre-train and prompt learning paradigms in Natural Language Processing (NLP) and Computer Vision (CV), their application in graph domains remains nascent. In graph-structured data, not only do the node and edge features often have disparate distributions, but the topological structures also differ significantly. This diversity in graph data can lead to incompatible patterns or gaps between pre-training and fine-tuning on downstream graphs. We aim to bridge this gap by summarizing methods for alleviating these disparities. This includes exploring prompt design methodologies, comparing related techniques, assessing application scenarios and datasets, and identifying unresolved problems and challenges. This survey categorizes over 100 relevant works in this field, summarizing general design principles and the latest applications, including text-attributed graphs, molecules, proteins, and recommendation systems. Through this extensive review, we provide a foundational understanding of graph prompt learning, aiming to impact not only the graph mining community but also the broader Artificial General Intelligence (AGI) community.

19 pages, 2 figures

cs.LG

MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agents

http://arxiv.org/abs/2408.14033v1

http://arxiv.org/abs/2408.14033v1

http://arxiv.org/pdf/2408.14033v1

2024-08-26

2024-08-26

Machine learning research, crucial for technological advancements and innovation, often faces significant challenges due to its inherent complexity, slow pace of experimentation, and the necessity for specialized expertise. Motivated by this, we present a new systematic framework, autonomous Machine Learning Research with large language models (MLR-Copilot), designed to enhance machine learning research productivity through the automatic generation and implementation of research ideas using Large Language Model (LLM) agents. The framework consists of three phases: research idea generation, experiment implementation, and implementation execution. First, existing research papers are used to generate hypotheses and experimental plans vis IdeaAgent powered by LLMs. Next, the implementation generation phase translates these plans into executables with ExperimentAgent. This phase leverages retrieved prototype code and optionally retrieves candidate models and data. Finally, the execution phase, also managed by ExperimentAgent, involves running experiments with mechanisms for human feedback and iterative debugging to enhance the likelihood of achieving executable research outcomes. We evaluate our framework on five machine learning research tasks and the experimental results show the framework's potential to facilitate the research progress and innovations.

cs.AI

SurGen: Text-Guided Diffusion Model for Surgical Video Generation

http://arxiv.org/abs/2408.14028v2

http://arxiv.org/abs/2408.14028v2

http://arxiv.org/pdf/2408.14028v2

2024-08-26

2024-08-28

Diffusion-based video generation models have made significant strides, producing outputs with improved visual fidelity, temporal coherence, and user control. These advancements hold great promise for improving surgical education by enabling more realistic, diverse, and interactive simulation environments. In this study, we introduce SurGen, a text-guided diffusion model tailored for surgical video synthesis, producing the highest resolution and longest duration videos among existing surgical video generation models. We validate the visual and temporal quality of the outputs using standard image and video generation metrics. Additionally, we assess their alignment to the corresponding text prompts through a deep learning classifier trained on surgical data. Our results demonstrate the potential of diffusion models to serve as valuable educational tools for surgical trainees.

cs.CV

Video-CCAM: Enhancing Video-Language Understanding with Causal Cross-Attention Masks for Short and Long Videos

http://arxiv.org/abs/2408.14023v1

http://arxiv.org/abs/2408.14023v1

http://arxiv.org/pdf/2408.14023v1

2024-08-26

2024-08-26

Multi-modal large language models (MLLMs) have demonstrated considerable potential across various downstream tasks that require cross-domain knowledge. MLLMs capable of processing videos, known as Video-MLLMs, have attracted broad interest in video-language understanding. However, videos, especially long videos, contain more visual tokens than images, making them difficult for LLMs to process. Existing works either downsample visual features or extend the LLM context size, risking the loss of high-resolution information or slowing down inference speed. To address these limitations, we apply cross-attention layers in the intermediate projector between the visual encoder and the large language model (LLM). As the naive cross-attention mechanism is insensitive to temporal order, we further introduce causal cross-attention masks (CCAMs) within the cross-attention layers. This Video-MLLM, named Video-CCAM, is trained in a straightforward two-stage fashion: feature alignment and visual instruction tuning. We develop several Video-CCAM models based on LLMs of different sizes (4B, 9B, and 14B). Video-CCAM proves to be a robust Video-MLLM and shows outstanding performance from short videos to long ones. Among standard video benchmarks like MVBench and VideoChatGPT-QA, Video-CCAM shows outstanding performances (1st/2nd/3rd in MVBench and TGIF-QA, 2nd/3rd/4th in MSVD-QA, MSRVTT-QA, and ActivityNet-QA). In benchmarks encompassing long videos, Video-CCAM models can be directly adapted to long video understanding and still achieve exceptional scores despite being trained solely with images and 16-frame videos. Using 96 frames (6$\times$ the training number of frames), Video-CCAM models rank 1st/2nd/3rd in VideoVista and 1st/2nd/4th in MLVU among all open-source Video-MLLMs, respectively. The code is publicly available in \url{https://github.com/QQ-MM/Video-CCAM}.

10 pages, 5 figures

cs.CV

Pixel-Aligned Multi-View Generation with Depth Guided Decoder

http://arxiv.org/abs/2408.14016v1

http://arxiv.org/abs/2408.14016v1

http://arxiv.org/pdf/2408.14016v1

2024-08-26

2024-08-26

The task of image-to-multi-view generation refers to generating novel views of an instance from a single image. Recent methods achieve this by extending text-to-image latent diffusion models to multi-view version, which contains an VAE image encoder and a U-Net diffusion model. Specifically, these generation methods usually fix VAE and finetune the U-Net only. However, the significant downscaling of the latent vectors computed from the input images and independent decoding leads to notable pixel-level misalignment across multiple views. To address this, we propose a novel method for pixel-level image-to-multi-view generation. Unlike prior work, we incorporate attention layers across multi-view images in the VAE decoder of a latent video diffusion model. Specifically, we introduce a depth-truncated epipolar attention, enabling the model to focus on spatially adjacent regions while remaining memory efficient. Applying depth-truncated attn is challenging during inference as the ground-truth depth is usually difficult to obtain and pre-trained depth estimation models is hard to provide accurate depth. Thus, to enhance the generalization to inaccurate depth when ground truth depth is missing, we perturb depth inputs during training. During inference, we employ a rapid multi-view to 3D reconstruction approach, NeuS, to obtain coarse depth for the depth-truncated epipolar attention. Our model enables better pixel alignment across multi-view images. Moreover, we demonstrate the efficacy of our approach in improving downstream multi-view to 3D reconstruction tasks.

cs.CV

Optimizing TD3 for 7-DOF Robotic Arm Grasping: Overcoming Suboptimality with Exploration-Enhanced Contrastive Learning

http://arxiv.org/abs/2408.14009v1

http://arxiv.org/abs/2408.14009v1

http://arxiv.org/pdf/2408.14009v1

2024-08-26

2024-08-26

In actor-critic-based reinforcement learning algorithms such as Twin Delayed Deep Deterministic policy gradient (TD3), insufficient exploration of the spatial space can result in suboptimal policies when controlling 7-DOF robotic arms. To address this issue, we propose a novel Exploration-Enhanced Contrastive Learning (EECL) module that improves exploration by providing additional rewards for encountering novel states. Our module stores previously explored states in a buffer and identifies new states by comparing them with historical data using Euclidean distance within a K-dimensional tree (KDTree) framework. When the agent explores new states, exploration rewards are assigned. These rewards are then integrated into the TD3 algorithm, ensuring that the Q-learning process incorporates these signals, promoting more effective strategy optimization. We evaluate our method on the robosuite panda lift task, demonstrating that it significantly outperforms the baseline TD3 in terms of both efficiency and convergence speed in the tested environment.

4 pages, 2 figures, IEEE-ICKII-2024

cs.RO

LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models

http://arxiv.org/abs/2408.14008v1

http://arxiv.org/abs/2408.14008v1

http://arxiv.org/pdf/2408.14008v1

2024-08-26

2024-08-26

The explosive growth of videos on streaming media platforms has underscored the urgent need for effective video quality assessment (VQA) algorithms to monitor and perceptually optimize the quality of streaming videos. However, VQA remains an extremely challenging task due to the diverse video content and the complex spatial and temporal distortions, thus necessitating more advanced methods to address these issues. Nowadays, large multimodal models (LMMs), such as GPT-4V, have exhibited strong capabilities for various visual understanding tasks, motivating us to leverage the powerful multimodal representation ability of LMMs to solve the VQA task. Therefore, we propose the first Large Multi-Modal Video Quality Assessment (LMM-VQA) model, which introduces a novel spatiotemporal visual modeling strategy for quality-aware feature extraction. Specifically, we first reformulate the quality regression problem into a question and answering (Q&A) task and construct Q&A prompts for VQA instruction tuning. Then, we design a spatiotemporal vision encoder to extract spatial and temporal features to represent the quality characteristics of videos, which are subsequently mapped into the language space by the spatiotemporal projector for modality alignment. Finally, the aligned visual tokens and the quality-inquired text tokens are aggregated as inputs for the large language model (LLM) to generate the quality score and level. Extensive experiments demonstrate that LMM-VQA achieves state-of-the-art performance across five VQA benchmarks, exhibiting an average improvement of $5\%$ in generalization ability over existing methods. Furthermore, due to the advanced design of the spatiotemporal encoder and projector, LMM-VQA also performs exceptionally well on general video understanding tasks, further validating its effectiveness. Our code will be released at https://github.com/Sueqk/LMM-VQA.

cs.CV

Dual-CBA: Improving Online Continual Learning via Dual Continual Bias Adaptors from a Bi-level Optimization Perspective

http://arxiv.org/abs/2408.13991v1

http://arxiv.org/abs/2408.13991v1

http://arxiv.org/pdf/2408.13991v1

2024-08-26

2024-08-26

In online continual learning (CL), models trained on changing distributions easily forget previously learned knowledge and bias toward newly received tasks. To address this issue, we present Continual Bias Adaptor (CBA), a bi-level framework that augments the classification network to adapt to catastrophic distribution shifts during training, enabling the network to achieve a stable consolidation of all seen tasks. However, the CBA module adjusts distribution shifts in a class-specific manner, exacerbating the stability gap issue and, to some extent, fails to meet the need for continual testing in online CL. To mitigate this challenge, we further propose a novel class-agnostic CBA module that separately aggregates the posterior probabilities of classes from new and old tasks, and applies a stable adjustment to the resulting posterior probabilities. We combine the two kinds of CBA modules into a unified Dual-CBA module, which thus is capable of adapting to catastrophic distribution shifts and simultaneously meets the real-time testing requirements of online CL. Besides, we propose Incremental Batch Normalization (IBN), a tailored BN module to re-estimate its population statistics for alleviating the feature bias arising from the inner loop optimization problem of our bi-level framework. To validate the effectiveness of the proposed method, we theoretically provide some insights into how it mitigates catastrophic distribution shifts, and empirically demonstrate its superiority through extensive experiments based on four rehearsal-based baselines and three public continual learning benchmarks.

cs.LG

Automatic Medical Report Generation: Methods and Applications

http://arxiv.org/abs/2408.13988v1

http://arxiv.org/abs/2408.13988v1

http://arxiv.org/pdf/2408.13988v1

2024-08-26

2024-08-26

The increasing demand for medical imaging has surpassed the capacity of available radiologists, leading to diagnostic delays and potential misdiagnoses. Artificial intelligence (AI) techniques, particularly in automatic medical report generation (AMRG), offer a promising solution to this dilemma. This review comprehensively examines AMRG methods from 2021 to 2024. It (i) presents solutions to primary challenges in this field, (ii) explores AMRG applications across various imaging modalities, (iii) introduces publicly available datasets, (iv) outlines evaluation metrics, (v) identifies techniques that significantly enhance model performance, and (vi) discusses unresolved issues and potential future research directions. This paper aims to provide a comprehensive understanding of the existing literature and inspire valuable future research.

42 pages and 9 figures

cs.CV

Focused Large Language Models are Stable Many-Shot Learners

http://arxiv.org/abs/2408.13987v1

http://arxiv.org/abs/2408.13987v1

http://arxiv.org/pdf/2408.13987v1

2024-08-26

2024-08-26

In-Context Learning (ICL) enables large language models (LLMs) to achieve rapid task adaptation by learning from demonstrations. With the increase in available context length of LLMs, recent experiments have shown that the performance of ICL does not necessarily scale well in many-shot (demonstration) settings. We theoretically and experimentally confirm that the reason lies in more demonstrations dispersing the model attention from the query, hindering its understanding of key content. Inspired by how humans learn from examples, we propose a training-free method FocusICL, which conducts triviality filtering to avoid attention being diverted by unimportant contents at token-level and operates hierarchical attention to further ensure sufficient attention towards current query at demonstration-level. We also design an efficient hyperparameter searching strategy for FocusICL based on model perplexity of demonstrations. Comprehensive experiments validate that FocusICL achieves an average performance improvement of 5.2% over vanilla ICL and scales well with many-shot demonstrations.

15 pages

cs.CL

AgentMove: Predicting Human Mobility Anywhere Using Large Language Model based Agentic Framework

http://arxiv.org/abs/2408.13986v1

http://arxiv.org/abs/2408.13986v1

http://arxiv.org/pdf/2408.13986v1

2024-08-26

2024-08-26

Human mobility prediction plays a crucial role in various real-world applications. Although deep learning based models have shown promising results over the past decade, their reliance on extensive private mobility data for training and their inability to perform zero-shot predictions, have hindered further advancements. Recently, attempts have been made to apply large language models (LLMs) to mobility prediction task. However, their performance has been constrained by the absence of a systematic design of workflow. They directly generate the final output using LLMs, which limits the potential of LLMs to uncover complex mobility patterns and underestimates their extensive reserve of global geospatial knowledge. In this paper, we introduce AgentMove, a systematic agentic prediction framework to achieve generalized mobility prediction for any cities worldwide. In AgentMove, we first decompose the mobility prediction task into three sub-tasks and then design corresponding modules to complete these subtasks, including spatial-temporal memory for individual mobility pattern mining, world knowledge generator for modeling the effects of urban structure and collective knowledge extractor for capturing the shared patterns among population. Finally, we combine the results of three modules and conduct a reasoning step to generate the final predictions. Extensive experiments on mobility data from two sources in 12 cities demonstrate that AgentMove outperforms the best baseline more than 8% in various metrics and it shows robust predictions with various LLMs as base and also less geographical bias across cities. Codes and data can be found in https://github.com/tsinghua-fib-lab/AgentMove.

13 pages

cs.LG

Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models

http://arxiv.org/abs/2408.13979v1

http://arxiv.org/abs/2408.13979v1

http://arxiv.org/pdf/2408.13979v1

2024-08-26

2024-08-26

With the prevalence of large-scale pretrained vision-language models (VLMs), such as CLIP, soft-prompt tuning has become a popular method for adapting these models to various downstream tasks. However, few works delve into the inherent properties of learnable soft-prompt vectors, specifically the impact of their norms to the performance of VLMs. This motivates us to pose an unexplored research question: ``Do we need to normalize the soft prompts in VLMs?'' To fill this research gap, we first uncover a phenomenon, called the \textbf{Low-Norm Effect} by performing extensive corruption experiments, suggesting that reducing the norms of certain learned prompts occasionally enhances the performance of VLMs, while increasing them often degrades it. To harness this effect, we propose a novel method named \textbf{N}ormalizing th\textbf{e} soft-pro\textbf{m}pt v\textbf{e}ctors of vi\textbf{si}on-language model\textbf{s} (\textbf{Nemesis}) to normalize soft-prompt vectors in VLMs. To the best of our knowledge, our work is the first to systematically investigate the role of norms of soft-prompt vector in VLMs, offering valuable insights for future research in soft-prompt tuning. The code is available at \texttt{\href{https://github.com/ShyFoo/Nemesis}{https://github.com/ShyFoo/Nemesis}}.

Accepted at ICLR 2024 (Spotlight)

cs.CV

Time Series Analysis for Education: Methods, Applications, and Future Directions

http://arxiv.org/abs/2408.13960v2

http://arxiv.org/abs/2408.13960v2

http://arxiv.org/pdf/2408.13960v2

2024-08-25

2024-08-27

Recent advancements in the collection and analysis of sequential educational data have brought time series analysis to a pivotal position in educational research, highlighting its essential role in facilitating data-driven decision-making. However, there is a lack of comprehensive summaries that consolidate these advancements. To the best of our knowledge, this paper is the first to provide a comprehensive review of time series analysis techniques specifically within the educational context. We begin by exploring the landscape of educational data analytics, categorizing various data sources and types relevant to education. We then review four prominent time series methods-forecasting, classification, clustering, and anomaly detection-illustrating their specific application points in educational settings. Subsequently, we present a range of educational scenarios and applications, focusing on how these methods are employed to address diverse educational tasks, which highlights the practical integration of multiple time series methods to solve complex educational problems. Finally, we conclude with a discussion on future directions, including personalized learning analytics, multimodal data fusion, and the role of large language models (LLMs) in educational time series. The contributions of this paper include a detailed taxonomy of educational data, a synthesis of time series techniques with specific educational applications, and a forward-looking perspective on emerging trends and future research opportunities in educational analysis. The related papers and resources are available and regularly updated at the project page.

24 pages, 3 figures, 6 tables, project page: see https://github.com/ai-for-edu/time-series-analysis-for-education

cs.LG

Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems

http://arxiv.org/abs/2408.13950v1

http://arxiv.org/abs/2408.13950v1

http://arxiv.org/pdf/2408.13950v1

2024-08-25

2024-08-25

Deep Neural Networks (DNNs) for Autonomous Driving Systems (ADS) are typically trained on real-world images and tested using synthetic simulator images. This approach results in training and test datasets with dissimilar distributions, which can potentially lead to erroneously decreased test accuracy. To address this issue, the literature suggests applying domain-to-domain translators to test datasets to bring them closer to the training datasets. However, translating images used for testing may unpredictably affect the reliability, effectiveness and efficiency of the testing process. Hence, this paper investigates the following questions in the context of ADS: Could translators reduce the effectiveness of images used for ADS-DNN testing and their ability to reveal faults in ADS-DNNs? Can translators result in excessive time overhead during simulation-based testing? To address these questions, we consider three domain-to-domain translators: CycleGAN and neural style transfer, from the literature, and SAEVAE, our proposed translator. Our results for two critical ADS tasks -- lane keeping and object detection -- indicate that translators significantly narrow the gap in ADS test accuracy caused by distribution dissimilarities between training and test data, with SAEVAE outperforming the other two translators. We show that, based on the recent diversity, coverage, and fault-revealing ability metrics for testing deep-learning systems, translators do not compromise the diversity and the coverage of test data, nor do they lead to revealing fewer faults in ADS-DNNs. Further, among the translators considered, SAEVAE incurs a negligible overhead in simulation time and can be efficiently integrated into simulation-based testing. Finally, we show that translators increase the correlation between offline and simulation-based testing results, which can help reduce the cost of simulation-based testing.

Accepted for publication by the International Conference on Automated Software Engineering (ASE 2024)

cs.SE

Learning to Move Like Professional Counter-Strike Players

http://arxiv.org/abs/2408.13934v1

http://arxiv.org/abs/2408.13934v1

http://arxiv.org/pdf/2408.13934v1

2024-08-25

2024-08-25

In multiplayer, first-person shooter games like Counter-Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high-level strategic play. However, the complexity of team coordination and the variety of conditions present in popular game maps make it impractical to author hand-crafted movement policies for every scenario. We show that it is possible to take a data-driven approach to creating human-like movement controllers for CS:GO. We curate a team movement dataset comprising 123 hours of professional game play traces, and use this dataset to train a transformer-based movement model that generates human-like team movement for all players in a "Retakes" round of the game. Importantly, the movement prediction model is efficient. Performing inference for all players takes less than 0.5 ms per game step (amortized cost) on a single CPU core, making it plausible for use in commercial games today. Human evaluators assess that our model behaves more like humans than both commercially-available bots and procedural movement controllers scripted by experts (16% to 59% higher by TrueSkill rating of "human-like"). Using experiments involving in-game bot vs. bot self-play, we demonstrate that our model performs simple forms of teamwork, makes fewer common movement mistakes, and yields movement distributions, player lifetimes, and kill locations similar to those observed in professional CS:GO match play.

The project website is at https://davidbdurst.com/mlmove/

ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA), August 21-23, 2024, Montreal, Canada

cs.LG