Datasets:

mteb
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LitSearchRetrieval

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d252715594	We present Phenaki, a model capable of realistic video synthesis, given a sequence of textual prompts. Generating videos from text is particularly challenging due to the computational cost, limited quantities of high quality text-video data and variable length of videos. To address these issues, we introduce a new model for learning video representation which compresses the video to a small representation of discrete tokens. This tokenizer uses causal attention in time, which allows it to work with variable-length videos. To generate video tokens from text we are using a bidirectional masked transformer conditioned on pre-computed text tokens. The generated video tokens are subsequently de-tokenized to create the actual video. To address data issues, we demonstrate how joint training on a large corpus of image-text pairs as well as a smaller number of video-text examples can result in generalization beyond what is available in the video datasets. Compared to the previous video generation methods, Phenaki can generate arbitrary long videos conditioned on a sequence of prompts (i.e. time variable text or a story) in open domain. To the best of our knowledge, this is the first time a paper studies generating videos from time variable prompts. In addition, compared to the perframe baselines, the proposed video encoder-decoder computes fewer tokens per video but results in better spatio-temporal consistency. ‡ Equal contribution.	PHENAKI: VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS
d13002849	Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem. * Authors contributed equally.	MODE REGULARIZED GENERATIVE ADVERSARIAL NETWORKS
d239998253	Federated learning data is drawn from a distribution of distributions: clients are drawn from a meta-distribution, and their data are drawn from local data distributions. Thus generalization studies in federated learning should separate performance gaps from unseen client data (out-of-sample gap) from performance gaps from unseen client distributions (participation gap). In this work, we propose a framework for disentangling these performance gaps. Using this framework, we observe and explain differences in behavior across natural and synthetic federated datasets, indicating that dataset synthesis strategy can be important for realistic simulations of generalization in federated learning. We propose a semantic synthesis strategy that enables realistic simulation without naturally-partitioned data. Informed by our findings, we call out community suggestions for future federated learning works.	What Do We Mean by Generalization in Federated Learning?
d62841605	Discretizing multi-dimensional data distributions is a fundamental step of modern indexing methods. State-of-the-art techniques learn parameters of quantizers on training data for optimal performance, thus adapting quantizers to the data. In this work, we propose to reverse this paradigm and adapt the data to the quantizer: we train a neural net which last layer forms a fixed parameter-free quantizer, such as pre-defined points of a hyper-sphere. As a proxy objective, we design and train a neural network that favors uniformity in the spherical latent space, while preserving the neighborhood structure after the mapping. We propose a new regularizer derived from the Kozachenko-Leonenko differential entropy estimator to enforce uniformity and combine it with a locality-aware triplet loss. Experiments show that our end-to-end approach outperforms most learned quantization methods, and is competitive with the state of the art on widely adopted benchmarks. Furthermore, we show that training without the quantization step results in almost no difference in accuracy, but yields a generic catalyzer that can be applied with any subsequent quantizer. The code is available online 1 .	SPREADING VECTORS FOR SIMILARITY SEARCH
d253237531	Federated clustering (FC) is an unsupervised learning problem that arises in a number of practical applications, including personalized recommender and healthcare systems. With the adoption of recent laws ensuring the "right to be forgotten", the problem of machine unlearning for FC methods has become of significant importance. We introduce, for the first time, the problem of machine unlearning for FC, and propose an efficient unlearning mechanism for a customized secure FC framework. Our FC framework utilizes special initialization procedures that we show are well-suited for unlearning. To protect client data privacy, we develop the secure compressed multiset aggregation (SCMA) framework that addresses sparse secure federated learning (FL) problems encountered during clustering as well as more general problems. To simultaneously facilitate low communication complexity and secret sharing protocols, we integrate Reed-Solomon encoding with special evaluation points into our SCMA pipeline, and prove that the client communication cost is logarithmic in the vector dimension. Additionally, to demonstrate the benefits of our unlearning mechanism over complete retraining, we provide a theoretical analysis for the unlearning performance of our approach. Simulation results show that the new FC framework exhibits superior clustering performance compared to previously reported FC baselines when the cluster sizes are highly imbalanced. Compared to completely retraining K-means++ locally and globally for each removal request, our unlearning procedure offers an average speed-up of roughly 84x across seven datasets. Our implementation for the proposed method is available at https://github.com/thupchnsky/mufc. * Equal contribution. arXiv:2210.16424v2 [cs.LG] 1 Jul 2023 Published as a conference paper at ICLR 2023 Nir Ailon, Ragesh Jaiswal, and Claire Monteleoni. Streaming k-means approximation. Advances in neural information processing systems, 22, 2009.Constance Beguier, Mathieu Andreux, and Eric W Tramel. Efficient sparse secure aggregation for federated learning. arXiv preprint arXiv:	MACHINE UNLEARNING OF FEDERATED CLUSTERS
d222291443	We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values that are directly represented via human-understandable properties of expected futures. This is realized via the embedded self-prediction (ESP) model, which learns said properties in terms of human provided features. Action preferences can then be explained by contrasting the future properties predicted for each action. To address cases where there are a large number of features, we develop a novel method for computing minimal sufficient explanations from an ESP. Our case studies in three domains, including a complex strategy game, show that ESP models can be effectively learned and support insightful explanations.	CONTRASTIVE EXPLANATIONS FOR REINFORCEMENT LEARNING VIA EMBEDDED SELF PREDICTIONS
d223956716	We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation r of the training data, and then fitting a simple (e.g., linear) classifier g to the labels. Specifically, we show that (under the assumptions described below) the generalization gap of such classifiers tends to zero if C(g) n, where C(g) is an appropriately-defined measure of the simple classifier g's complexity, and n is the number of training samples. We stress that our bound is independent of the complexity of the representation r. We do not make any structural or conditional-independence assumptions on the representation-learning task, which can use the same training dataset that is later used for classification. Rather, we assume that the training procedure satisfies certain natural noise-robustness (adding small amount of label noise causes small degradation in performance) and rationality (getting the wrong label is not better than getting no label at all) conditions that widely hold across many standard architectures. We show that our bound is non-vacuous for many popular representation-learning based classifiers on CIFAR-10 and ImageNet, including SimCLR, AMDIM and BigBiGAN. * Equal contribution.	FOR SELF-SUPERVISED LEARNING, RATIONALITY IMPLIES GENERALIZATION, PROVABLY
d263605472	Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks.However, the cross-task correlations that are learned in the unstructured feature space can be extremely noisy and susceptible to overfitting, consequently hurting performance.We propose to address this problem by introducing a structured 3D-aware regularizer which interfaces multiple tasks through the projection of features extracted from an image encoder to a shared 3D feature space and decodes them into their task output space through differentiable rendering.We show that the proposed method is architecture agnostic and can be plugged into various prior multi-task backbones to improve their performance; as we evidence using standard benchmarks NYUv2 and PASCAL-Context.	MULTI-TASK LEARNING WITH 3D-AWARE REGULARIZATION
d212996548	Transformer has become ubiquitous in natural language processing (e.g., machine translation, question answering); however, it requires enormous amount of computations to achieve high performance, which makes it not suitable for mobile applications that are tightly constrained by the hardware resources and battery. In this paper, we present an efficient mobile NLP architecture, Lite Transformer to facilitate deploying mobile NLP applications on edge devices. The key primitive is the Long-Short Range Attention (LSRA), where one group of heads specializes in the local context modeling (by convolution) while another group specializes in the long-distance relationship modeling (by attention). Such specialization brings consistent improvement over the vanilla transformer on three well-established language tasks: machine translation, abstractive summarization, and language modeling. Under constrained resources (500M/100M MACs), Lite Transformer outperforms transformer on WMT'14 English-French by 1.2/1.7 BLEU, respectively. Lite Transformer reduces the computation of transformer base model by 2.5× with 0.3 BLEU score degradation. Combining with pruning and quantization, we further compressed the model size of Lite Transformer by 18.2×. For language modeling, Lite Transformer achieves 1.8 lower perplexity than the transformer at around 500M MACs. Notably, Lite Transformer outperforms the AutoML-based Evolved Transformer by 0.5 higher BLEU for the mobile NLP setting without the costly architecture search that requires more than 250 GPU years. Code has been made available at https://github.com/mit-han-lab/lite-transformer. * indicates equal contributions.	LITE TRANSFORMER WITH LONG-SHORT RANGE ATTENTION
d202719276	Adversarial training has been demonstrated as one of the most effective methods for training robust models so as to defend against adversarial examples. However, adversarial training often lacks adversarially robust generalization on unseen data. Recent works show that adversarially trained models may be more biased towards global structure features. Instead, in this work, we would like to investigate the relationship between the generalization of adversarial training and the robust local features, as the local features generalize well for unseen shape variation. To learn the robust local features, we develop a Random Block Shuffle (RBS) transformation to break up the global structure features on normal adversarial examples. We continue to propose a new approach called Robust Local Features for Adversarial Training (RLFAT), which first learns the robust local features by adversarial training on the RBS-transformed adversarial examples, and then transfers the robust local features into the training of normal adversarial examples. Finally, we implement RLFAT in two currently state-of-the-art adversarial training frameworks. Extensive experiments on STL-10, CIFAR-10, CIFAR-100 datasets show that RL-FAT improves the adversarially robust generalization as well as the standard generalization of adversarial training. Additionally, we demonstrate that our method captures more local features of the object, aligning better with human perception.	ROBUST LOCAL FEATURES FOR IMPROVING THE GENERALIZATION OF ADVERSARIAL TRAINING
d220665539	The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives. The AD techniques underlying these tools were designed to compute exact gradients to numerical precision, but modern machine learning models are almost always trained with stochastic gradient descent. Why spend computation and memory on exact (minibatch) gradients only to use them for stochastic optimization? We develop a general framework and approach for randomized automatic differentiation (RAD), which allows unbiased gradient estimates to be computed with reduced memory in return for variance. We examine limitations of the general approach, and argue that we must leverage problem specific structure to realize benefits. We develop RAD techniques for a variety of simple neural network architectures, and show that for a fixed memory budget, RAD converges in fewer iterations than using a small batch size for feedforward networks, and in a similar number for recurrent networks. We also show that RAD can be applied to scientific computing, and use it to develop a low-memory stochastic gradient method for optimizing the control parameters of a linear reaction-diffusion PDE representing a fission reactor.	Randomized Automatic Differentiation
d263152628	High-fidelity 3D scene reconstruction has been substantially advanced by recent progress in neural fields. However, most existing methods train a separate network from scratch for each individual scene. This is not scalable, inefficient, and unable to yield good results given limited views. While learning-based multi-view stereo methods alleviate this issue to some extent, their multi-view setting makes it less flexible to scale up and to broad applications. Instead, we introduce training generalizable Neural Fields incorporating scene Priors (NFPs). The NFP network maps any single-view RGB-D image into signed distance and radiance values. A complete scene can be reconstructed by merging individual frames in the volumetric space WITHOUT a fusion module, which provides better flexibility. The scene priors can be trained on large-scale datasets, allowing for fast adaptation to the reconstruction of a new scene with fewer views. NFP not only demonstrates SOTA scene reconstruction performance and efficiency, but it also supports single-image novel-view synthesis, which is underexplored in neural fields. More qualitative results are available at: https://oasisyang.github.io/neural-prior.	3D RECONSTRUCTION WITH GENERALIZABLE NEURAL FIELDS USING SCENE PRIORS
d264802502	Offline reinforcement learning (RL) can in principle synthesize more optimal behavior from a dataset consisting only of suboptimal trials.One way that this can happen is by "stitching" together the best parts of otherwise suboptimal trajectories that overlap on similar states, to create new behaviors where each individual state is in-distribution, but the overall returns are higher.However, in many interesting and complex applications, such as autonomous navigation and dialogue systems, the state is partially observed.Even worse, the state representation is unknown or not easy to define.In such cases, policies and value functions are often conditioned on observation histories instead of states.In these cases, it is not clear if the same kind of "stitching" is feasible at the level of observation histories, since two different trajectories would always have different histories, and thus "similar states" that might lead to effective stitching cannot be leveraged.Theoretically, we show that standard offline RL algorithms conditioned on observation histories suffer from poor sample complexity, in accordance with the above intuition.We then identify sufficient conditions under which offline RL can still be efficient -intuitively, it needs to learn a compact representation of history comprising only features relevant for action selection.We introduce a bisimulation loss that captures the extent to which this happens, and propose that offline RL can explicitly optimize this loss to aid worst-case sample complexity.Empirically, we show that across a variety of tasks either our proposed loss improves performance, or the value of this loss is already minimized as a consequence of standard offline RL, indicating that it correlates well with good performance.	OFFLINE RL WITH OBSERVATION HISTORIES: ANALYZING AND IMPROVING SAMPLE COMPLEXITY
d227068701	While energy-based models (EBMs) exhibit a number of desirable properties, training and sampling on high-dimensional datasets remains challenging. Inspired by recent progress on diffusion probabilistic models, we present a diffusion recovery likelihood method to tractably learn and sample from a sequence of EBMs trained on increasingly noisy versions of a dataset. Each EBM is trained by maximizing the recovery likelihood: the conditional probability of the data at a certain noise level given their noisy versions at a higher noise level. The recovery likelihood objective is more tractable than the marginal likelihood objective, since it only requires MCMC sampling from a relatively concentrated conditional distribution. Moreover, we show that this estimation method is theoretically consistent: it learns the correct conditional and marginal distributions at each noise level, given sufficient data. After training, synthesized images can be generated efficiently by a sampling process that initializes from a spherical Gaussian distribution and progressively samples the conditional distributions at decreasingly lower noise levels. Our method generates high fidelity samples on various image datasets. On unconditional CIFAR-10 our method achieves FID 9.60 and inception score 8.58, superior to the majority of GANs. Moreover, we demonstrate that unlike previous work on EBMs, our long-run MCMC samples from the conditional distributions do not diverge and still represent realistic images, allowing us to accurately estimate the normalized density of data even for high-dimensional datasets.arXiv:2012.08125v1 [cs.LG] 15 Dec 2020Preprint. Work in progress. Figure 1: Generated samples on LSUN 128 2 church outdoor (left), LSUN 128 2 bedroom (center) and CelebA 64 2 (right).Gaussian distributions at increasing scales, and then learns to reverse this perturbation process by training a sequence of models to reverse each step of the noise corruption. After training such a sequence of models, we can obtain samples from Gaussian white noise by sampling from each model sequentially with decreasing noise scales. These methods have demonstrated great success in applications such as image generation (Ho et al., 2020;Song & Ermon, 2020)and audio synthesis(Chen et al., 2020;Kong et al., 2020).Inspired bySohl-Dickstein et al. (2015)and Ho et al. (2020), we propose to train EBMs with diffusion recovery likelihood, a better method than training them directly on a dataset with the standard likelihood. Specifically, we perturb the dataset with a sequence of noise distributions, and learn a sequence of EBMs to model the marginal distributions of the perturbation process. The sequence of EBMs are learned by maximizing recovery likelihoods, which are the densities of conditional distributions that reverse each step of the perturbation process. Compared to standard maximum likelihood estimation (MLE) of EBMs, learning marginal EBMs with recovery likelihoods only require sampling from conditional distributions, which is arguably much easier than sampling from marginal distributions(Bengio et al., 2014). After learning all marginal EBMs, we can generate image samples by starting from the Gaussian white noise, and then produce samples from each conditional distribution in the descending order of noise scales.Unlike Ho et al. (2020) where the reverse conditional models are parameterized with normal distributions, in our case the conditional models are derived from the marginal EBMs, which are much more flexible. Our method has similarities toBengio et al. (2013)where the same recovery likelihood objective is used but with a single noise level and without EBMs, leading to different theoretical properties. Importantly, the model inBengio et al. (2013)does not directly estimate a marginal distribution, while we learn a the sequence of EBMs to model the marginal distributions of the perturbation process.Rhodes et al. (2020)also propose to train EBMs based on a series of intermediate distributions, but their training approach is a variant of noise contrastive estimation-not a likelihood-based approach like ours.We demonstrate the efficacy of diffusion recovery likelihood on CIFAR-10, CelebA and LSUN datasets. The generated samples are of high fidelity and comparable to GAN-based methods. On CIFAR-10, we achieve FID 9.60 and inception score 8.58, exceeding existing methods of learning explicit EBMs to a large extent. We also demonstrate that diffusion recovery likelihood outperforms denoising score matching from diffusion data if we naively take the gradients of explicit energy functions as the score functions. More interestingly, by using a thousand diffusion time steps, we demonstrate that even very long MCMC chains from the sequence of conditional distributions produce samples that represent realistic images. With the faithful long-run MCMC samples from the	LEARNING ENERGY-BASED MODELS BY DIFFUSION RECOVERY LIKELIHOOD
d251732759	Motivated by the fact that forward and backward passes of a deep network naturally form symmetric mappings between input and output representations, we introduce a simple yet effective self-supervised vision model pretraining framework inspired by energy-based models (EBMs). In the proposed framework, we model energy estimation and data restoration as the forward and backward passes of a single network without any auxiliary components, e.g., an extra decoder. For the forward pass, we fit a network to an energy function that assigns low energy scores to samples that belong to an unlabeled dataset, and high energy otherwise. For the backward pass, we restore data from corrupted versions iteratively using gradientbased optimization along the direction of energy minimization. In this way, we naturally fold the encoder-decoder architecture widely used in masked image modeling into the forward and backward passes of a single vision model. Thus, our framework now accepts a wide range of pretext tasks with different data corruption methods, and permits models to be pretrained from masked image modeling, patch sorting, and image restoration, including super-resolution, denoising, and colorization. We support our findings with extensive experiments, and show the proposed method delivers comparable and even better performance with remarkably fewer epochs of training compared to the state-of-the-art self-supervised vision model pretraining methods. Our findings shed light on further exploring self-supervised vision model pretraining and pretext tasks beyond masked image modeling. , et al. Language models are few-shot learners. arXiv preprint arXiv:	ENERGY-INSPIRED SELF-SUPERVISED PRETRAINING FOR VISION MODELS
d253523474	Under mild conditions on the network initialization we derive a power series expansion for the Neural Tangent Kernel (NTK) of arbitrarily deep feedforward networks in the infinite width limit. We provide expressions for the coefficients of this power series which depend on both the Hermite coefficients of the activation function as well as the depth of the network. We observe faster decay of the Hermite coefficients leads to faster decay in the NTK coefficients and explore the role of depth. Using this series, first we relate the effective rank of the NTK to the effective rank of the inputdata Gram. Second, for data drawn uniformly on the sphere we study the eigenvalues of the NTK, analyzing the impact of the choice of activation function. Finally, for generic data and activation functions with sufficiently fast Hermite coefficient decay, we derive an asymptotic upper bound on the spectrum of the NTK.	CHARACTERIZING THE SPECTRUM OF THE NTK VIA A POWER SERIES EXPANSION
d162184036	Storage assignment, the act of choosing what goods are placed in what locations in a warehouse, is a central problem of supply chain logistics. Past literature has shown that the optimal method to assign pallets is to arrange them in increasing duration of stay (DoS) in the warehouse (the DoS method), but the methodology requires perfect prior knowledge of DoS for each pallet, which is unknown and uncertain under realistic conditions. Attempts to predict DoS have largely been unfruitful due to the multi-valuedness nature (every shipment contains identical pallets with different DoS) and data sparsity induced by lack of matching historical conditions. In this paper, we introduce a new framework for storage assignment that provides a solution to the DoS prediction problem through a distributional reformulation and a novel neural network, ParallelNet. Through collaboration with a world-leading cold storage company, we show that the system is able to predict DoS with a MAPE of 29%, a decrease of ∼30% compared to a CNN-LSTM model, and suffers less performance decay into the future. The framework is then integrated into a first-of-its-kind Storage Assignment system, which is being deployed in warehouses across United States, with initial results showing up to 21% in labor savings. We also release the first publicly available set of warehousing records to facilitate research into this central problem.	DURATION-OF-STAY STORAGE ASSIGNMENT UNDER UNCERTAINTY
d264555396	Exploration bonuses in reinforcement learning guide long-horizon exploration by defining custom intrinsic objectives.Count-based methods use the frequency of state visits to derive an exploration bonus.In this paper, we identify that any intrinsic reward function derived from count-based methods is non-stationary and hence induces a difficult objective to optimize for the agent.The key contribution of our work lies in transforming the original non-stationary rewards into stationary rewards through an augmented state representation.For this purpose, we introduce the Stationary Objectives For Exploration (SOFE) framework.SOFE requires identifying sufficient statistics for different exploration bonuses and finding an efficient encoding of these statistics to use as input to a deep network.SOFE is based on proposing state augmentations that expand the state space but hold the promise of simplifying the optimization of the agent's objective.Our experiments show that SOFE improves the agents' performance in challenging exploration problems, including sparse-reward tasks, pixel-based observations, 3D navigation, and procedurally generated environments.	IMPROVING INTRINSIC EXPLORATION BY CREATING STATIONARY OBJECTIVES
d251341969	Early stopping based on the validation set performance is a popular approach to find the right balance between under-and overfitting in the context of supervised learning. However, in reinforcement learning, even for supervised sub-problems such as world model learning, early stopping is not applicable as the dataset is continually evolving. As a solution, we propose a new general method that dynamically adjusts the update to data (UTD) ratio during training based on underand overfitting detection on a small subset of the continuously collected experience not used for training. We apply our method to DreamerV2, a state-of-the-art model-based reinforcement learning algorithm, and evaluate it on the DeepMind Control Suite and the Atari 100k benchmark. The results demonstrate that one can better balance under-and overestimation by adjusting the UTD ratio with our approach compared to the default setting in DreamerV2 and that it is competitive with an extensive hyperparameter search which is not feasible for many applications. Our method eliminates the need to set the UTD hyperparameter by hand and even leads to a higher robustness with regard to other learning-related hyperparameters further reducing the amount of necessary tuning.Published as a conference paper at ICLR 2023 UTD ratio is more prone to overfit the data and a lower one to underfit it. State-of-the-art methods set the UTD ratio at the beginning of the training and do not base the selection on a dynamic performance metric. Unfortunately, tuning this parameter is very costly as the complete training process has to be traversed several times. Furthermore, a fixed UTD ratio is often sub-optimal because different values for this parameter might be preferable at different stages of the training process. Environment Training Data World Model Train every 1 UTD ratio Evaluate update Policy Validation Data	DYNAMIC UPDATE-TO-DATA RATIO: MINIMIZING WORLD MODEL OVERFITTING
d255340742	Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is used to measure the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and nonlong-tailed natural and medical datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias. In addition, we look ahead to future challenges.	Delving into Semantic Scale Imbalance
d53467348	We study the phenomenon of bias amplification in classifiers, wherein a machine learning model learns to predict classes with a greater disparity than the underlying ground truth. We demonstrate that bias amplification can arise via an inductive bias in gradient descent methods that results in the overestimation of the importance of moderately-predictive "weak" features if insufficient training data is available. This overestimation gives rise to feature-wise bias amplificationa previously unreported form of bias that can be traced back to the features of a trained model. Through analysis and experiments, we show that while some bias cannot be mitigated without sacrificing accuracy, feature-wise bias amplification can be mitigated through targeted feature selection. We present two new feature selection algorithms for mitigating bias amplification in linear models, and show how they can be adapted to convolutional neural networks efficiently. Our experiments on synthetic and real data demonstrate that these algorithms consistently lead to reduced bias without harming accuracy, in some cases eliminating predictive bias altogether while providing modest gains in accuracy.	FEATURE-WISE BIAS AMPLIFICATION
d253801963	One of the grand challenges of reinforcement learning is the ability to generalize to new tasks.However, general agents require a set of rich, diverse tasks to train on.Designing a 'foundation environment' for such tasks is tricky -the ideal environment would support a range of emergent phenomena, an expressive task space, and fast runtime.To take a step towards addressing this research bottleneck, this work presents Powderworld, a lightweight yet expressive simulation environment running directly on the GPU.Within Powderworld, two motivating challenges distributions are presented, one for world-modelling and one for reinforcement learning.Each contains hand-designed test tasks to examine generalization.Experiments indicate that increasing the environment's complexity improves generalization for world models and certain reinforcement learning agents, yet may inhibit learning in high-variance environments.Powderworld aims to support the study of generalization by providing a source of diverse tasks arising from the same core rules.Try an interactable demo at kvfrans.com/static/powder	POWDERWORLD: A PLATFORM FOR UNDERSTANDING GENERALIZATION VIA RICH TASK DISTRIBUTIONS
d249888901	In this paper, we investigate the power of regularization, a common technique in reinforcement learning and optimization, in solving extensive-form games (EFGs). We propose a series of new algorithms based on regularizing the payoff functions of the game, and establish a set of convergence results that strictly improve over the existing ones, with either weaker assumptions or stronger convergence guarantees. In particular, we first show that dilated optimistic mirror descent (DOMD), an efficient variant of OMD for solving EFGs, with adaptive regularization can achieve a fast O(1/T ) last-iterate convergence in terms of duality gap and distance to the set of Nash equilibrium (NE) without uniqueness assumption of the NE. Second, we show that regularized counterfactual regret minimization (Reg-CFR), with a variant of optimistic mirror descent algorithm as regret-minimizer, can achieve O(1/T 1/4 ) best-iterate, and O(1/T 3/4 ) averageiterate convergence rate for finding NE in EFGs. Finally, we show that Reg-CFR can achieve asymptotic last-iterate convergence, and optimal O(1/T ) averageiterate convergence rate, for finding the NE of perturbed EFGs, which is useful for finding approximate extensive-form perfect equilibria (EFPE). To the best of our knowledge, they constitute the first last-iterate convergence results for CFRtype algorithms, while matching the state-of-the-art average-iterate convergence rate in finding NE for non-perturbed EFGs. We also provide numerical results to corroborate the advantages of our algorithms.	THE POWER OF REGULARIZATION IN SOLVING EXTENSIVE-FORM GAMES
d231632937	We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks. Unlike current hierarchical RL approaches that tend to formulate goal-reaching low-level tasks or pre-define ad hoc lowerlevel policies, HIDIO encourages lower-level option learning that is independent of the task at hand, requiring few assumptions or little knowledge about the task structure. These options are learned through an intrinsic entropy minimization objective conditioned on the option sub-trajectories. The learned options are diverse and task-agnostic. In experiments on sparse-reward robotic manipulation and navigation tasks, HIDIO achieves higher success rates with greater sample efficiency than regular RL baselines and two state-of-the-art hierarchical RL methods. Code available at https://www.github.com/jesbu1/hidio. * Denotes equal contribution.	HIERARCHICAL REINFORCEMENT LEARNING BY DISCOVERING INTRINSIC OPTIONS
d246904522	Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields. However, no existing work has delved deeper to further investigate the main cause of this phenomenon. In this work, we make the attempt to analyze the over-smoothing problem from the perspective of graph, where such problem was first discovered and explored. Intuitively, the self-attention matrix can be seen as a normalized adjacent matrix of a corresponding graph. Based on the above connection, we provide some theoretical analysis and find that layer normalization plays a key role in the over-smoothing issue of Transformer-based models. Specifically, if the standard deviation of layer normalization is sufficiently large, the output of Transformer stacks will converge to a specific low-rank subspace and result in over-smoothing. To alleviate the over-smoothing problem, we consider hierarchical fusion strategies, which combine the representations from different layers adaptively to make the output more diverse. Extensive experiment results on various data sets illustrate the effect of our fusion method. * Equal contribution.	REVISITING OVER-SMOOTHING IN BERT FROM THE PERSPECTIVE OF GRAPH
d252846609	In a backdoor attack, an attacker injects corrupted examples into the training set. The goal of the attacker is to cause the final trained model to predict the attacker's desired target label when a predefined trigger is added to test inputs. Central to these attacks is the trade-off between the success rate of the attack and the number of corrupted training examples injected. We pose this attack as a novel bilevel optimization problem: construct strong poison examples that maximize the attack success rate of the trained model. We use neural tangent kernels to approximate the training dynamics of the model being attacked and automatically learn strong poison examples. We experiment on subclasses of CIFAR-10 and ImageNet with WideResNet-34 and ConvNeXt architectures on periodic and patch trigger attacks and show that NTBA-designed poisoned examples achieve, for example, an attack success rate of 90% with ten times smaller number of poison examples injected compared to the baseline. We provided an interpretation of the NTBA-designed attacks using the analysis of kernel linear regression. We further demonstrate a vulnerability in overparametrized deep neural networks, which is revealed by the shape of the neural tangent kernel.	Few-shot Backdoor Attacks via Neural Tangent Kernels
d257834209	We introduce semi-parametric inducing point networks (SPIN), a general-purpose architecture that can query the training set at inference time in a compute-efficient manner. Semi-parametric architectures are typically more compact than parametric models, but their computational complexity is often quadratic. In contrast, SPIN attains linear complexity via a cross-attention mechanism between datapoints inspired by inducing point methods. Querying large training sets can be particularly useful in meta-learning, as it unlocks additional training signal, but often exceeds the scaling limits of existing models. We use SPIN as the basis of the Inducing Point Neural Process, a probabilistic model which supports large contexts in metalearning and achieves high accuracy where existing models fail. In our experiments, SPIN reduces memory requirements, improves accuracy across a range of metalearning tasks, and improves state-of-the-art performance on an important practical problem, genotype imputation.	SEMI-PARAMETRIC INDUCING POINT NETWORKS AND NEURAL PROCESSES
d52980218	Data augmentation is commonly used to encode invariances in learning methods. However, this process is often performed in an inefficient manner, as artificial examples are created by applying a number of transformations to all points in the training set. The resulting explosion of the dataset size can be an issue in terms of storage and training costs, as well as in selecting and tuning the optimal set of transformations to apply. In this work, we demonstrate that it is possible to significantly reduce the number of data points included in data augmentation while realizing the same accuracy and invariance benefits of augmenting the entire dataset. We propose a novel set of subsampling policies, based on model influence and loss, that can achieve a 90% reduction in augmentation set size while maintaining the accuracy gains of standard data augmentation.	EFFICIENT AUGMENTATION VIA DATA SUBSAMPLING
d232257804		IMPLICIT NORMALIZING FLOWS
d263831863	Dataset distillation methods have achieved remarkable success in distilling a large dataset into a small set of representative samples.However, they are not designed to produce a distilled dataset that can be effectively used for facilitating selfsupervised pre-training.To this end, we propose a novel problem of distilling an unlabeled dataset into a set of small synthetic samples for efficient self-supervised learning (SSL).We first prove that a gradient of synthetic samples with respect to a SSL objective in naive bilevel optimization is biased due to the randomness originating from data augmentations or masking.To address this issue, we propose to minimize the mean squared error (MSE) between a model's representations of the synthetic examples and their corresponding learnable target feature representations for the inner objective, which does not introduce any randomness.Our primary motivation is that the model obtained by the proposed inner optimization can mimic the self-supervised target model.To achieve this, we also introduce the MSE between representations of the inner model and the self-supervised target model on the original full dataset for outer optimization.Lastly, assuming that a feature extractor is fixed, we only optimize a linear head on top of the feature extractor, which allows us to reduce the computational cost and obtain a closedform solution of the head with kernel ridge regression.We empirically validate the effectiveness of our method on various applications involving transfer learning.	SELF-SUPERVISED DATASET DISTILLATION FOR TRANSFER LEARNING
d5763832	An important field in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. When gradient-based methods are used, models are kept small or rely on finite difference approximations for the Jacobian. This method quickly grows expensive with increasing numbers of parameters, such as found in deep learning. We propose the implementation of a modern physics engine, which can differentiate control parameters. This engine is implemented for both CPU and GPU. Firstly, this paper shows how such an engine speeds up the optimization process, even for small problems. Furthermore, it explains why this is an alternative approach to deep Q-learning, for using deep learning in robotics. Finally, we argue that this is a big step for deep learning in robotics, as it opens up new possibilities to optimize robots, both in hardware and software.	A Differentiable Physics Engine for Deep Learning in Robotics
d261245530	Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce InsertNeRF, a method for INStilling gEneRalizabiliTy into NeRF. By utilizing multiple plug-and-play HyperNet modules, InsertNeRF dynamically tailors NeRF's weights to specific reference scenes, transforming multi-scale sampling-aware features into scene-specific representations. This novel design allows for more accurate and efficient representations of complex appearances and geometries. Experiments show that this method not only achieves superior generalization performance but also provides a flexible pathway for integration with other NeRF-like systems, even in sparse input settings. Code will be available https://github.com/bbbbby-99/InsertNeRF.	INSERTNERF: INSTILLING GENERALIZABILITY INTO NERF WITH HYPERNET MODULES
d21850704	Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. However, for longer documents and summaries, these models often include repetitive and incoherent phrases. We introduce a neural network model with intra-attention and a new training method. This method combines standard supervised word prediction and reinforcement learning (RL). Models trained only with the former often exhibit "exposure bias" -they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, a 5.7 absolute points improvement over previous state-of-the-art models. It also performs well as the first abstractive model on the New York Times corpus. Human evaluation also shows that our model produces higher quality summaries.	A Deep Reinforced Model for Abstractive Summarization
d239009555	Model-free reinforcement learning algorithms can compute policy gradients given sampled environment transitions, but require large amounts of data. In contrast, model-based methods can use the learned model to generate new data, but model errors and bias can render learning unstable or suboptimal. In this paper, we present a novel method that combines real-world data and a learned model in order to get the best of both worlds. The core idea is to exploit the real-world data for onpolicy predictions and use the learned model only to generalize to different actions. Specifically, we use the data as time-dependent on-policy correction terms on top of a learned model, to retain the ability to generate data without accumulating errors over long prediction horizons. We motivate this method theoretically and show that it counteracts an error term for model-based policy improvement. Experiments on MuJoCo-and PyBullet-benchmarks show that our method can drastically improve existing model-based approaches without introducing additional tuning parameters. * Work done partially while at Published as a conference paper at ICLR 2022 combine the two methodologies, in this work we focus on improving the model's predictive state distribution such that it more closely resembles the data distribution of the true environment.ContributionsThe main contribution of this paper is on-policy corrections (OPC), a novel hyperparameter-free methodology that uses on-policy transition data on top of a separately learned model to enable accurate long-term predictions for MBRL. A key strength of our approach is that it does not introduce any new parameters that need to be hand-tuned for specific tasks. We theoretically motivate our approach by means of a policy improvement bound and show that we can recover the true state distribution when generating trajectories on-policy with the model. We illustrate how OPC improves the quality of policy gradient estimates in a simple toy example and evaluate it on various continuous control tasks from the MuJoCo control suite and their PyBullet variants. There, we demonstrate that OPC improves current state-of-the-art MBRL algorithms in terms of data-efficiency, especially for the more difficult PyBullet environments.	ON-POLICY MODEL ERRORS IN REINFORCEMENT LEARNING
d43939886	Deep neural networks generalize remarkably well without explicit regularization even in the strongly over-parametrized regime. This success suggests that some form of implicit regularization must be at work. In this paper we argue that a strong intrinsic bias in the parameter-function map helps explain the success of deep neural networks. We provide evidence that the parameter-function map results in a heavily biased prior over functions, if we assume that the training algorithm samples parameters close to uniformly within the zero-error region. The PAC-Bayes theorem then guarantees good expected generalization for target functions producing high-likelihood training sets. We exploit connections between deep neural networks and Gaussian processes to estimate the marginal likelihood, finding remarkably good agreement between Gaussian processes and neural networks for small input sets. Using approximate marginal likelihood calculations we produce nontrivial generalization PAC-Bayes error bounds which correlate well with the true error on realistic datasets such as MNIST and CIFAR and for architectures including convolutional and fully connected networks. As predicted by recent arguments based on algorithmic information theory, we find that the prior probability drops exponentially with linear increases in several measures of descriptional complexity of the target function. As target functions in many real problems are expected to be highly structured, this simplicity bias offers an insight into why deep networks generalize well on real world problems, but badly on randomized data. information theory, 22(1):75-81, 1976. Qianli Liao and Tomaso Poggio. Theory of deep learning ii: Landscape of the empirical risk in deep learning. arXiv preprint arXiv:1703.09833, 2017. , et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529, 2015. . A pacbayesian approach to spectrally-normalized margin bounds for neural networks. arXiv preprint arXiv:1707.09564, 2017a. wards understanding the role of over-parametrization in generalization of neural networks. arXiv preprint arXiv:1805.12076, 2018. . Exponential expressivity in deep neural networks through transient chaos. In Advances in neural information processing systems, pp. 3360-3368, 2016. Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.	DEEP LEARNING GENERALIZES BECAUSE THE PARAMETER-FUNCTION MAP IS BIASED TOWARDS SIMPLE FUNCTIONS
d7305965	We formulate learning of a binary autoencoder as a biconvex optimization problem which learns from the pairwise correlations between encoded and decoded bits. Among all possible algorithms that use this information, ours finds the autoencoder that reconstructs its inputs with worst-case optimal loss. The optimal decoder is a single layer of artificial neurons, emerging entirely from the minimax loss minimization, and with weights learned by convex optimization. All this is reflected in competitive experimental results, demonstrating that binary autoencoding can be done efficiently by conveying information in pairwise correlations in an optimal fashion.	OPTIMAL BINARY AUTOENCODING WITH PAIRWISE CORRELATIONS
d261697392	Diffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attempts to improve its sampling speed and reduce computational costs through distillation have been unsuccessful in achieving a functional one-step model. In this paper, we explore a recent method called Rectified Flow [1, 2], which, thus far, has only been applied to small datasets. The core of Rectified Flow lies in its reflow procedure, which straightens the trajectories of probability flows, refines the coupling between noises and images, and facilitates the distillation process with student models. We propose a novel text-conditioned pipeline to turn Stable Diffusion (SD) into an ultra-fast one-step model, in which we find reflow plays a critical role in improving the assignment between noise and images. Leveraging our new pipeline, we create, to the best of our knowledge, the first one-step diffusion-based text-to-image generator with SD-level image quality, achieving an FID (Fréchet Inception Distance) of 23.3 on MS COCO 2017-5k, surpassing the previous state-of-the-art technique, progressive distillation [3], by a significant margin (37.2 → 23.3 in FID). By utilizing an expanded network with 1.7B parameters, we further improve the FID to 22.4. We call our one-step models InstaFlow. On MS COCO 2014-30k, InstaFlow yields an FID of 13.1 in just 0.09 second, the best in ≤ 0.	InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
d3536139	Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and functional significance of these spatial representations remain largely mysterious. As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs. Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. All these different functional types of neurons have been observed experimentally. The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits. * equal contribution arXiv:1803.07770v1 [q-bio.NC]	EMERGENCE OF GRID-LIKE REPRESENTATIONS BY TRAINING RECURRENT NEURAL NETWORKS TO PERFORM SPATIAL LOCALIZATION
d220302524	Conducting text retrieval in a dense learned representation space has many intriguing advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires combination with sparse retrieval. In this paper, we identify that the main bottleneck is in the training mechanisms, where the negative instances used in training are not representative of the irrelevant documents in testing. This paper presents Approximate nearest neighbor Negative Contrastive Estimation (ANCE), a training mechanism that constructs negatives from an Approximate Nearest Neighbor (ANN) index of the corpus, which is parallelly updated with the learning process to select more realistic negative training instances. This fundamentally resolves the discrepancy between the data distribution used in the training and testing of DR. In our experiments, ANCE boosts the BERT-Siamese DR model to outperform all competitive dense and sparse retrieval baselines. It nearly matches the accuracy of sparse-retrieval-and-BERT-reranking using dot-product in the ANCE-learned representation space and provides almost 100x speed-up. * Lee and Chenyan contributed equally.Preprint. Under review.	Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
d252596001	Humans can reason compositionally when presented with new tasks. Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artificial compositional generalization tasks such as SCAN.In this work, we identify additional challenges in more realistic semantic parsing tasks with larger vocabulary and refine these prompting techniques to address them. Our best method is based on least-to-most prompting: it decomposes the problem using prompting-based syntactic parsing, then uses this decomposition to select appropriate exemplars and to sequentially generate the semantic parse. This method allows us to set a new state of the art for CFQ while requiring only 1% of the training data used by traditional approaches. Due to the general nature of our approach, we expect similar efforts will lead to new results in other tasks and domains, especially for knowledge-intensive applications. arXiv:2209.15003v2 [cs.CL] 30 Sep 2022 Q: Was N1 N2 that M2 employed A: Was N1 ((N2) that (M2 employed))Q: Who was N0 that wrote N1 and edited N2 A: Who was ((N0) that (wrote N1 and edited N2)) Q: Did N1 marry and divorce N2 whose N3 wrote M3 A: Did N1 marry and divorce ((N2) whose (N3 wrote M3)) Q: Did N1 whose N2 was employed by M2 and was employed by M3 direct and write M1 A: Did ((N1) whose (N2 was employed by M2 and was employed by M3)) direct and write M1 Q: Did M3 found M4 and found N1 whose N2 wrote M1 A: Did M3 found M4 and found ((N1) whose (N2 wrote M1)) Q: What N1 whose N2 was influenced by N3 played M1 A: What ((N1) whose (N2 was influenced by N3)) played M1 Q: Which N1 whose N2 was influenced by M3 and was influenced by N3 directed M2 A: Which ((N1) whose (N2 was influenced by M3 and was influenced by N3)) directed M2 Q: Which N1 was influenced by N2 whose N3 edited M2 and employed by M3 A: Which N1 was influenced by ((N2) whose (N3 edited M2)) and employed by M3 Q: Was N1 whose N2 executive produced , edited , and wrote M0 M1 A: Was ((N1) whose (N2 executive produced , edited , and wrote M0)) M1 Q: Was M1 N2 whose N3 directed , produced , and wrote N4 A: Was M1 ((N2) whose (N3 directed , produced , and wrote N4)) Q: Was N1 that N2 directed N3 A: Was ((N1) that (N2 directed)) N3 Q: Was N1 whose N2 executive produced M1 and edited M0 M3 A: Was ((N1) whose (N2 executive produced M1 and edited M0)) M3 Q: What N1 was N2 whose N3 wrote N4 A: What N1 was ((N2) whose (N3 wrote N4)) Q: Which N1 that N2 were written by and were produced by was N3 A: Which ((N1) that (N2 were written by and were produced by)) was N3 Q: Was M2 N2 whose N3 was employed by and founded M1 A: Was M2 ((N2) whose (N3 was employed by and founded M1)) Q: Which N1 was N2 whose N3 produced and executive produced M2 A: Which N1 was ((N2) whose (N3 produced and executive produced M2)) Q: Was N0 that N1 were executive produced by , were edited by , were written by , and starred M0 A: Was ((N0) that (N1 were executive produced by , were edited by , were written by , and starred M0)) Q: Which N0 that M2 employed executive produced M1 A: Which ((N0) that (M2 employed)) executive produced M1 Q: Who was N0 that M2 was influenced by and married A: Who was ((N0) that (M2 was influenced by and married)) A: Which ((N0) that (M1 was edited by)) did M2 influence Q: Were N0 directed by and produced by N1 that executive produced M1 A: Were N0 directed by and produced by ((N1) that (executive produced M1)) Q: What N0 that M1 was executive produced by and written by did N1 marry A: What ((N0) that (M1 was executive produced by and written by)) did N1 marry Q: Was N0 that M1 married N1 that directed and edited N2 A: Was ((N0) that (M1 married)) ((N1) that (directed and edited N2)) Q: What N0 influenced by M1 and influenced by N1 that founded N2 edited M2 A: What N0 influenced by M1 and influenced by ((N1) that (founded N2)) edited M2 Q: Was N0 that N1 were edited , art directed , and produced by M0 A: Was ((N0) that (N1 were edited , art directed , and produced by)) M0A: whose (N1) (was employed by and founded) (M1) Q: that M1 influenced A: that (M1) (influenced) Q: that directed and executive produced N1 A: that (directed and executive produced) (N1) Q: that M2 was influenced by and married A: that (M2) (was influenced by and married) Q: that N1 were influenced by , M3 was edited by , and M4 married A: that ((N1 were influenced by) , (M3 was edited by) , and (M4 married)) Q: that N1 were written by A: that (N1) (were written by) Q: that was founded by and employed N2 A: that (was founded by and employed) (N2) Q: that was influenced by M2 and was employed by M3 and M4 A: that ((was influenced by M2) and (was employed by M3 and M4)) Q: that N1 married A: that (N1) (married) Q: that edited N1 and produced M2 A: that ((edited N1) and (produced M2)) Q: whose N1 edited M0 and executive produced N2 A: whose (N1) ((edited M0) and (executive produced N2)) Q: whose N1 edited N2 A: whose (N1) (edited) (N2) Q: that M1 influenced and employed A: that (M1) (influenced and employed) Q: that wrote N1 and edited N2 A: that ((wrote N1) and (edited N2)) Q: that edited and wrote N1 A: that (edited and wrote) (N1) Q: whose N1 was employed by and founded M1 A: whose (N1) (was employed by and founded) (M1) Q: whose N1 married M2 and married N2 A: whose (N1) ((married M2) and (married N2)) Q: that played M2 , played M3 , and played M4 A: that ((played M2) , (played M3) , and (played M4)) Q: that wrote , edited , executive produced , and directed N1 A: that (wrote , edited , executive produced , and directed) (N1) Q: that N3 were written by and art directed by A: that (N3) (were written by and art directed by) Q: Was N1 N2	COMPOSITIONAL SEMANTIC PARSING WITH LARGE LANGUAGE MODELS
d259095643	As LLMs become commonplace, machine-generated text has the potential to flood the internet with spam, social media bots, and valueless content. Watermarking is a simple and effective strategy for mitigating such harms by enabling the detection and documentation of LLM-generated text. Yet a crucial question remains: How reliable is watermarking in realistic settings in the wild? There, watermarked text may be modified to suit a user's needs, or entirely rewritten to avoid detection. We study the robustness of watermarked text after it is re-written by humans, paraphrased by a non-watermarked LLM, or mixed into a longer hand-written document. We find that watermarks remain detectable even after human and machine paraphrasing. While these attacks dilute the strength of the watermark, paraphrases are statistically likely to leak n-grams or even longer fragments of the original text, resulting in high-confidence detections when enough tokens are observed. For example, after strong human paraphrasing the watermark is detectable after observing 800 tokens on average, when setting a 1e−5 false positive rate. We also consider a range of new detection schemes that are sensitive to short spans of watermarked text embedded inside a large document, and we compare the robustness of watermarking to other kinds of detectors.	On the Reliability of Watermarks for Large Language Models
d247595088	Recent works in deep learning have shown that integrating differentiable physics simulators into the training process can greatly improve the quality of results. Although this combination represents a more complex optimization task than supervised neural network training, the same gradient-based optimizers are typically employed to minimize the loss function. However, the integrated physics solvers have a profound effect on the gradient flow as manipulating scales in magnitude and direction is an inherent property of many physical processes. Consequently, the gradient flow is often highly unbalanced and creates an environment in which existing gradient-based optimizers perform poorly. In this work, we analyze the characteristics of both physical and neural network optimizations to derive a new method that does not suffer from this phenomenon. Our method is based on a halfinversion of the Jacobian and combines principles of both classical network and physics optimizers to solve the combined optimization task. Compared to state-ofthe-art neural network optimizers, our method converges more quickly and yields better solutions, which we demonstrate on three complex learning problems involving nonlinear oscillators, the Schrödinger equation and the Poisson problem.Published as a conference paper at ICLR 2022 lenging loss landscapes are addressed using gradient-based optimizers with data-based normalizing schemes, such as Adam(Kingma & Ba, 2015), whereas in physics, the optimizers of choice are higher-order techniques, such as Newton's method(Gill & Murray, 1978), which inherently make use of inversion processes. However,Holl et al. (2021)found that these approaches can not effectively handle the joint optimization of network and physics. Gradient-descent-based optimizers suffer from vanishing or exploding gradients, preventing effective convergence, while higher-order methods do not generally scale to the high-dimensional parameter spaces required by deep learning(Goodfellow et al., 2016).Inspired by the insight that inversion is crucial for physics problems in learning from Holl et al.(2021), we focus on an inversion-based approach but propose a new method for joint physics and network optimization which we refer to as half-inverse gradients. At its core lies a partial matrix inversion, which we derive from the interaction between network and physics both formally and geometrically. An important property of our method is that its runtime scales linearly with the number of network parameters. To demonstrate the wide-ranging and practical applicability of our method, we show that it yields significant improvements in terms of convergence speed and final loss values over existing methods. These improvements are measured both in terms of absolute accuracy as well as wall-clock time. We evaluate a diverse set of physical systems, such as the Schrödinger equation, a nonlinear chain system and the Poisson problem.	HALF-INVERSE GRADIENTS FOR PHYSICAL DEEP LEARNING
d259342096	Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost. Instruction tuning is a technique for training LLMs to follow instructions. We advocate combining these two approaches, as we find that MoE models benefit more from instruction tuning than dense models. In particular, we conduct empirical studies across three experimental setups: (i) Direct finetuning on individual downstream tasks devoid of instruction tuning; (ii) Instruction tuning followed by in-context few-shot or zero-shot generalization on downstream tasks; and (iii) Instruction tuning supplemented by further finetuning on individual downstream tasks. In the first scenario, MoE models overall underperform dense models of identical computational capacity. This narrative, however, dramatically changes with the introduction of instruction tuning (second and third scenario), used independently or in conjunction with task-specific finetuning. Our most powerful model, FLAN-MOE 32B , surpasses the performance of FLAN-PALM 62B on four benchmark tasks, while using only a third of the FLOPs. The advancements embodied by FLAN-MOE inspire a reevaluation of the design principles of large-scale, high-performance language models in the framework of task-agnostic learning. * Work done at Google Preprint. Under review. arXiv:2305.14705v2 [cs.CL] 5 Jul 2023 2.2 Instruction Fine-tuning RecipeWe fine-tune FLAN-MOE using the prefix language model objective on the FLAN collective dataset[4,28]. Each FLAN-MOE will inherit the auxiliary loss setting during pre-training. All the model parameters will be updated. We adapt the sequence length of each FLAN-MOE to 2, 048 for input and 512 for output based on the relative position embedding. The dropout rate is 0.05 and the expert dropout rate is 0.2. The learning rate is 1e −4 . The optimizer setting follows [4].ExperimentWe study FLAN-MOE in the context of instruction-tuning. We first perform a controlled comparison of FLAN-MOE to an equivalent "standard" dense encoder-decoder Transformer (T5), across a range of model sizes in Section 3.2. We subsequently demonstrate in Section 3.3 that scaling up our model, referred to as FLAN-MOE, can attain remarkable performance levels. Our most extensive model, FLAN-ST 32B , surpasses the performance of FLAN-PALM 62B while utilizing less than 30% of FLOPs per token. We further ablate the various design decisions in the next Section. 3.1 Settings Traning Data. By default, all models are trained on the 1,836 finetuning tasks by combining four mixtures from prior work: Muffin, T0-SF, NIV2, and CoT, as in [4]. Specifically, Muffin comprises 80 tasks from [52] and 26 dialog/program synthesis tasks; T0-SF comprises 193 tasks from [44]; NIV2 comprises 1554 tasks from [51]; CoT comprises 9 reasoning tasks.Evaluations. We conduct both zero-shot and few-shot evaluations on held-out tasks as in [4] which were not included as part of the finetuning data. We use MMLU [16] that includes exam questions from 57 tasks such as mathematics, history, law, and medicine; BBH includes 23 challenging	Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models
d21196492	Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a mixed objective that combines cross entropy loss with self-critical policy learning. The objective uses rewards derived from word overlap to solve the misalignment between evaluation metric and optimization objective. In addition to the mixed objective, we improve dynamic coattention networks (DCN) with a deep residual coattention encoder that is inspired by recent work in deep self-attention and residual networks. Our proposals improve model performance across question types and input lengths, especially for long questions that requires the ability to capture long-term dependencies. On the Stanford Question Answering Dataset, our model achieves state-of-the-art results with 75.1% exact match accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy and 86.0% F1.	DCN+: MIXED OBJECTIVE AND DEEP RESIDUAL COATTENTION FOR QUESTION ANSWERING
d247446857	Selecting an appropriate optimizer for a given problem is of major interest for researchers and practitioners. Many analytical optimizers have been proposed using a variety of theoretical and empirical approaches; however, none can offer a universal advantage over other competitive optimizers. We are thus motivated to study a new problem named Optimizer Amalgamation: how can we best combine a pool of "teacher" optimizers into a single "student" optimizer that can have stronger problem-specific performance? In this paper, we draw inspiration from the field of "learning to optimize" to use a learnable amalgamation target. First, we define three differentiable amalgamation mechanisms to amalgamate a pool of analytical optimizers by gradient descent. Then, in order to reduce variance of the amalgamation process, we also explore methods to stabilize the amalgamation process by perturbing the amalgamation target. Finally, we present experiments showing the superiority of our amalgamated optimizer compared to its amalgamated components and learning to optimize baselines, and the efficacy of our variance reducing perturbations. Our code and pre-trained models are publicly available at	OPTIMIZER AMALGAMATION
d254926490	Language models have recently achieved strong performance across a wide range of NLP benchmarks. However, unlike benchmarks, real world tasks are often poorly specified, and agents must deduce the user's intended behavior from a combination of context, instructions, and examples. We investigate how both humans and models behave in the face of such task ambiguity by proposing AmbiBench, a new benchmark of six ambiguously-specified classification tasks. We evaluate humans and models on AmbiBench by seeing how well they identify the intended task using 1) instructions with varying degrees of ambiguity, and 2) different numbers of labeled examples. We find that the combination of model scaling (to 175B parameters) and training with human feedback data enables models to approach or exceed the accuracy of human participants across tasks, but that either one alone is not sufficient. In addition, we show how to dramatically improve the accuracy of language models trained without large-scale human feedback training by finetuning on a small number of ambiguous in-context examples, providing a promising direction for teaching models to generalize well in the face of ambiguity.	TASK AMBIGUITY IN HUMANS AND LANGUAGE MODELS
d52912260	The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task.Yet, even with such meta-learning, the low-data problem in the novel classification task still remains.In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data.TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner.We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.	LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING
d3535369	Gradient-based optimization is the foundation of deep learning and reinforcement learning. Even when the mechanism being optimized is unknown or not differentiable, optimization using high-variance or biased gradient estimates is still often the best strategy. We introduce a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables. Our method uses gradients of a neural network trained jointly with model parameters or policies, and is applicable in both discrete and continuous settings. We demonstrate this framework for training discrete latent-variable models. We also give an unbiased, action-conditional extension of the advantage actor-critic reinforcement learning algorithm.	BACKPROPAGATION THROUGH THE VOID: OPTIMIZING CONTROL VARIATES FOR BLACK-BOX GRADIENT ESTIMATION
d263834989	Current privacy research on large language models (LLMs) primarily focuses on the issue of extracting memorized training data. At the same time, models' inference capabilities have increased drastically. This raises the key question of whether current LLMs could violate individuals' privacy by inferring personal attributes from text given at inference time. In this work, we present the first comprehensive study on the capabilities of pretrained LLMs to infer personal attributes from text. We construct a dataset consisting of real Reddit profiles, and show that current LLMs can infer a wide range of personal attributes (e.g., location, income, sex), achieving up to 85% top-1 and 95.8% top-3 accuracy at a fraction of the cost (100×) and time (240×) required by humans. As people increasingly interact with LLM-powered chatbots across all aspects of life, we also explore the emerging threat of privacy-invasive chatbots trying to extract personal information through seemingly benign questions. Finally, we show that common mitigations, i.e., text anonymization and model alignment, are currently ineffective at protecting user privacy against LLM inference. Our findings highlight that current LLMs can infer personal data at a previously unattainable scale. In the absence of working defenses, we advocate for a broader discussion around LLM privacy implications beyond memorization, striving for a wider privacy protection.	BEYOND MEMORIZATION: VIOLATING PRIVACY VIA INFERENCE WITH LARGE LANGUAGE MODELS
d247595243	To generalize well, classifiers must learn to be invariant to nuisance transformations that do not alter an input's class. Many problems have "class-agnostic" nuisance transformations that apply similarly to all classes, such as lighting and background changes for image classification. Neural networks can learn these invariances given sufficient data, but many real-world datasets are heavily class imbalanced and contain only a few examples for most of the classes. We therefore pose the question: how well do neural networks transfer class-agnostic invariances learned from the large classes to the small ones? Through careful experimentation, we observe that invariance to class-agnostic transformations is still heavily dependent on class size, with the networks being much less invariant on smaller classes. This result holds even when using data balancing techniques, and suggests poor invariance transfer across classes. Our results provide one explanation for why classifiers generalize poorly on unbalanced and long-tailed distributions. Based on this analysis, we show how a generative approach for learning the nuisance transformations can help transfer invariances across classes and improve performance on a set of imbalanced image classification benchmarks. Source code for our experiments is available at https://github.com/AllanYangZhou/ generative-invariance-transfer. * First two authors contributed equally.	DO DEEP NETWORKS TRANSFER INVARIANCES ACROSS CLASSES?
d252683543	Recent large-scale neural autoregressive sequence models have shown impressive performances on a variety of natural language generation tasks. However, their generated sequences often exhibit degenerate properties such as non-termination, undesirable repetition, and premature termination, when generated with decoding algorithms such as greedy search, beam search, top-k sampling, and nucleus sampling. In this paper, we focus on the problem of non-terminating sequences resulting from an incomplete decoding algorithm. We first define an incomplete probable decoding algorithm which includes greedy search, top-k sampling, and nucleus sampling, beyond the incomplete decoding algorithm originally put forward by Welleck et al. (2020). We then propose a non-monotonic self-terminating language model, which significantly relaxes the constraint of monotonically increasing termination probability in the originally proposed self-terminating language model by Welleck et al. (2020), to address the issue of non-terminating sequences when using incomplete probable decoding algorithms. We prove that our proposed model prevents non-terminating sequences when using not only incomplete probable decoding algorithms but also beam search. We empirically validate our model on sequence completion tasks with various architectures. † New York University ‡ Prescient Design, Genentech § CIFAR Fellow Published as a conference paper at ICLR 2023 example, suppose there are two sequences in our dataset: "I am a boy" vs. "I am a boy, and you are a girl.". Our language model trained on this dataset may or may not terminate after the former. Once our model decides not to end, it should dramatically reduce the termination probability to continue. The ST language model, which monotonically increase the termination probability, cannot capture such a case, where one sequence is a prefix of another. We thus propose a non-monotonic self-terminating (NMST) language model which guarantees the consistency with respect to greedy search, beam search, top-k sampling, and nucleus sampling without monotonically increasing termination probability.	A NON-MONOTONIC SELF-TERMINATING LANGUAGE MODEL
d246240237	Robust subspace recovery (RSR) is a fundamental problem in robust representation learning. Here we focus on a recently proposed RSR method termed Dual Principal Component Pursuit (DPCP) approach, which aims to recover a basis of the orthogonal complement of the subspace and is amenable to handling subspaces of high relative dimension. Prior work has shown that DPCP can provably recover the correct subspace in the presence of outliers, as long as the true dimension of the subspace is known. We show that DPCP can provably solve RSR problems in the unknown subspace dimension regime, as long as orthogonality constraints -adopted in previous DPCP formulations-are relaxed and random initialization is used instead of spectral one. Namely, we propose a very simple algorithm based on running multiple instances of a projected sub-gradient descent method (PSGM), with each problem instance seeking to find one vector in the null space of the subspace. We theoretically prove that under mild conditions this approach will succeed with high probability. In particular, we show that 1) all of the problem instances will converge to a vector in the nullspace of the subspace and 2) the ensemble of problem instance solutions will be sufficiently diverse to fully span the nullspace of the subspace thus also revealing its true unknown codimension. We provide empirical results that corroborate our theoretical results and showcase the remarkable implicit rank regularization behavior of PSGM algorithm that allows us to perform RSR without being aware of the subspace dimension.	IMPLICIT BIAS OF PROJECTED SUBGRADIENT METHOD GIVES PROVABLE ROBUST RECOVERY OF SUBSPACES OF UNKNOWN CODIMENSION
d263671656	Matching cross-modality features between images and point clouds is a fundamental problem for image-to-point cloud registration.However, due to the modality difference between images and points, it is difficult to learn robust and discriminative cross-modality features by existing metric learning methods for feature matching.Instead of applying metric learning on cross-modality data, we propose to unify the modality between images and point clouds by pretrained large-scale models first, and then establish robust correspondence within the same modality.We show that the intermediate features, called diffusion features, extracted by depth-to-image diffusion models are semantically consistent between images and point clouds, which enables the building of coarse but robust crossmodality correspondences.We further extract geometric features on depth maps produced by the monocular depth estimator.By matching such geometric features, we significantly improve the accuracy of the coarse correspondences produced by diffusion features.Extensive experiments demonstrate that without any task-specific training, direct utilization of both features produces accurate imageto-point cloud registration.On three public indoor and outdoor benchmarks, the proposed method averagely achieves a 20.6% improvement in Inlier Ratio, a 3.0× higher Inlier Number, and a 48.6% improvement in Registration Recall than existing state-of-the-arts.The codes and additional results are available at https://whu-usi3dv.github.io/FreeReg/.	FREEREG: IMAGE-TO-POINT CLOUD REGISTRATION LEVERAGING PRETRAINED DIFFUSION MODELS AND MONOCULAR DEPTH ESTIMATORS
d202660778	Improving the sample efficiency in reinforcement learning has been a longstanding research problem. In this work, we aim to reduce the sample complexity of existing policy gradient methods. We propose a novel policy gradient algorithm called SRVR-PG, which only requires O(1/ 3/2 ) 1 episodes to find anapproximate stationary point of the nonconcave performance function J(θ) (i.e.	SAMPLE EFFICIENT POLICY GRADIENT METHODS WITH RECURSIVE VARIANCE REDUCTION
d235293695	A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more representative or informative than others. This unequal importance among the data points may have a large impact in rehearsal-based continual learning, where we store a subset of the training examples (coreset) to be replayed later to alleviate catastrophic forgetting. In continual learning, the quality of the samples stored in the coreset directly affects the model's effectiveness and efficiency. The coreset selection problem becomes even more important under realistic settings, such as imbalanced continual learning or noisy data scenarios. To tackle this problem, we propose Online Coreset Selection (OCS), a simple yet effective method that selects the most representative and informative coreset at each iteration and trains them in an online manner. Our proposed method maximizes the model's adaptation to a current dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgetting. We validate the effectiveness of our coreset selection mechanism over various standard, imbalanced, and noisy datasets against strong continual learning baselines, demonstrating that it improves task adaptation and prevents catastrophic forgetting in a sample-efficient manner.	ONLINE CORESET SELECTION FOR REHEARSAL-BASED CONTINUAL LEARNING
d233378598	Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models. However, in certain situations, this technique is more of a curse than a blessing. For instance, KD poses a potential risk of exposing intellectual properties (IPs): even if a trained machine learning model is released in "black boxes" (e.g., as executable software or APIs without open-sourcing code), it can still be replicated by KD through imitating input-output behaviors. To prevent this unwanted effect of KD, this paper introduces and investigates a concept called Nasty Teacher: a specially trained teacher network that yields nearly the same performance as a normal one, but would significantly degrade the performance of student models learned by imitating it. We propose a simple yet effective algorithm to build the nasty teacher, called self-undermining knowledge distillation. Specifically, we aim to maximize the difference between the output of the nasty teacher and a normal pretrained network. Extensive experiments on several datasets demonstrate that our method is effective on both standard KD and data-free KD, providing the desirable KD-immunity to model owners for the first time. We hope our preliminary study can draw more awareness and interest in this new practical problem of both social and legal importance. Our codes and pre-trained models can be found at https://github.com/VITA-Group/Nasty-Teacher.	UNDISTILLABLE: MAKING A NASTY TEACHER THAT CANNOT TEACH STUDENTS
d238582772	In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with varying time intervals between successive readouts and different subsets of variables (sensors) observed at different time points. Here, we introduce RAINDROP, a graph neural network that embeds irregularly sampled and multivariate time series while also learning the dynamics of sensors purely from observational data. RAINDROP represents every sample as a separate sensor graph and models time-varying dependencies between sensors with a novel message passing operator. It estimates the latent sensor graph structure and leverages the structure together with nearby observations to predict misaligned readouts. This model can be interpreted as a graph neural network that sends messages over graphs that are optimized for capturing time-varying dependencies among sensors. We use RAINDROP to classify time series and interpret temporal dynamics on three healthcare and human activity datasets. RAINDROP outperforms state-of-the-art methods by up to 11.4% (absolute F1-score points), including techniques that deal with irregular sampling using fixed discretization and set functions. RAINDROP shows superiority in diverse setups, including challenging leave-sensor-out settings.	GRAPH-GUIDED NETWORK FOR IRREGULARLY SAMPLED MULTIVARIATE TIME SERIES
d253116642	We present new benchmarks for evaluating code generation models: MBXP, Multilingual HumanEval, and MathQA-X. These datasets encompass over 10 programming languages and are generated using a scalable conversion framework that transpiles prompts and test cases from the original Python datasets into the corresponding data in the target language. With these benchmarks, we can assess the performance of code generation models in a multilingual context, uncovering the generalization ability of language models on out-of-domain languages, the advantages of multilingual models over monolingual ones, the potential of few-shot prompting to teach models new languages, and zero-shot translation capabilities, even in monolingual settings. Additionally, we utilize our code generation model for large-scale bootstrapping to obtain synthetic canonical solutions in various languages, which can be employed for other code-related evaluations, such as code insertion, robustness, or summarization tasks. Overall, our benchmarks represent a significant step towards a deeper understanding of language models' code generation abilities. We publicly release our code and datasets at	MULTI-LINGUAL EVALUATION OF CODE GENERATION MODELS
d252668582	Regularized optimal transport (OT) is now increasingly used as a loss or as a matching layer in neural networks. Entropy-regularized OT can be computed using the Sinkhorn algorithm but it leads to fully-dense transportation plans, meaning that all sources are (fractionally) matched with all targets. To address this issue, several works have investigated quadratic regularization instead. This regularization preserves sparsity and leads to unconstrained and smooth (semi) dual objectives, that can be solved with off-the-shelf gradient methods. Unfortunately, quadratic regularization does not give direct control over the cardinality (number of nonzeros) of the transportation plan. We propose in this paper a new approach for OT with explicit cardinality constraints on the transportation plan. Our work is motivated by an application to sparse mixture of experts, where OT can be used to match input tokens such as image patches with expert models such as neural networks. Cardinality constraints ensure that at most k tokens are matched with an expert, which is crucial for computational performance reasons. Despite the nonconvexity of cardinality constraints, we show that the corresponding (semi) dual problems are tractable and can be solved with first-order gradient methods. Our method can be thought as a middle ground between unregularized OT (recovered when k is small enough) and quadratically-regularized OT (recovered when k is large enough). The smoothness of the objectives increases as k increases, giving rise to a trade-off between convergence speed and sparsity of the optimal plan.	SPARSITY-CONSTRAINED OPTIMAL TRANSPORT
d220302148	Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, generalize poorly, or treat subgroups unfairly. While many methods aim to address these problems individually, in this work, we explore them through a unified framework-tilted empirical risk minimization (TERM). In particular, we show that it is possible to flexibly tune the impact of individual losses through a straightforward extension to ERM using a hyperparameter called the tilt. We provide several interpretations of the resulting framework: We show that TERM can increase or decrease the influence of outliers, respectively, to enable fairness or robustness; has variance-reduction properties that can benefit generalization; and can be viewed as a smooth approximation to a superquantile method. We develop batch and stochastic first-order optimization methods for solving TERM, and show that the problem can be efficiently solved relative to common alternatives. Finally, we demonstrate that TERM can be used for a multitude of applications, such as enforcing fairness between subgroups, mitigating the effect of outliers, and handling class imbalance. TERM is not only competitive with existing solutions tailored to these individual problems, but can also enable entirely new applications, such as simultaneously addressing outliers and promoting fairness. * Equal contribution.	Tilted Empirical Risk Minimization
d245828046	Identifying the status of individual network units is critical for understanding the mechanism of convolutional neural networks (CNNs). However, it is still challenging to reliably give a general indication of unit status, especially for units in different network models. To this end, we propose a novel method for quantitatively clarifying the status of single unit in CNN using algebraic topological tools. Unit status is indicated via the calculation of a defined topological-based entropy, called feature entropy, which measures the degree of chaos of the global spatial pattern hidden in the unit for a category. In this way, feature entropy could provide an accurate indication of status for units in different networks with diverse situations like weight-rescaling operation. Further, we show that feature entropy decreases as the layer goes deeper and shares almost simultaneous trend with loss during training. We show that by investigating the feature entropy of units on only training data, it could give discrimination between networks with different generalization ability from the view of the effectiveness of feature representations.	QUANTITATIVE PERFORMANCE ASSESSMENT OF CNN UNITS VIA TOPOLOGICAL ENTROPY CALCULATION
d211132990	Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks. However, an ensemble's cost for both training and testing increases linearly with the number of networks, which quickly becomes untenable. In this paper, we propose BatchEnsemble 1 , an ensemble method whose computational and memory costs are significantly lower than typical ensembles. BatchEnsemble achieves this by defining each weight matrix to be the Hadamard product of a shared weight among all ensemble members and a rank-one matrix per member. Unlike ensembles, BatchEnsemble is not only parallelizable across devices, where one device trains one member, but also parallelizable within a device, where multiple ensemble members are updated simultaneously for a given mini-batch. Across CIFAR-10, CIFAR-100, WMT14 EN-DE/EN-FR translation, and out-of-distribution tasks, BatchEnsemble yields competitive accuracy and uncertainties as typical ensembles; the speedup at test time is 3X and memory reduction is 3X at an ensemble of size 4. We also apply BatchEnsemble to lifelong learning, where on Split-CIFAR-100, BatchEnsemble yields comparable performance to progressive neural networks while having a much lower computational and memory costs. We further show that BatchEnsemble can easily scale up to lifelong learning on Split-ImageNet which involves 100 sequential learning tasks. * Partial work done as part of the Google Student Researcher Program.	BATCHENSEMBLE: AN ALTERNATIVE APPROACH TO EFFICIENT ENSEMBLE AND LIFELONG LEARNING
d213938729	These days deep neural networks are ubiquitously used in a wide range of tasks, from image classification and machine translation to face identification and selfdriving cars. In many applications, a single model error can lead to devastating financial, reputational and even life-threatening consequences. Therefore, it is crucially important to correct model mistakes quickly as they appear. In this work, we investigate the problem of neural network editing -how one can efficiently patch a mistake of the model on a particular sample, without influencing the model behavior on other samples. Namely, we propose Editable Training, a model-agnostic training technique that encourages fast editing of the trained model. We empirically demonstrate the effectiveness of this method on large-scale image classification and machine translation tasks. * Equal contribution arXiv:2004.00345v1 [cs.LG] 1 Apr 2020Published as a conference paper at ICLR 2020 that model's mistakes can be corrected without harming its overall performance. With thorough experimental evaluation, we demonstrate that our method works on both small academical datasets and industry-scale machine learning tasks. We summarize the contributions of this study as follows:	EDITABLE NEURAL NETWORKS
d259952484	Many real-world applications of reinforcement learning (RL) require making decisions in continuous action environments. In particular, determining the optimal dose level plays a vital role in developing medical treatment regimes. One challenge in adapting existing RL algorithms to medical applications, however, is that the popular infinite support stochastic policies, e.g., Gaussian policy, may assign riskily high dosages and harm patients seriously. Hence, it is important to induce a policy class whose support only contains near-optimal actions, and shrink the action-searching area for effectiveness and reliability. To achieve this, we develop a novel quasi-optimal learning algorithm, which can be easily optimized in off-policy settings with guaranteed convergence under general function approximations. Theoretically, we analyze the consistency, sample complexity, adaptability, and convergence of the proposed algorithm. We evaluate our algorithm with comprehensive simulated experiments and a dose suggestion real application to Ohio Type 1 diabetes dataset.	Quasi-optimal Reinforcement Learning with Continuous Actions
d259375870	Large language models like GPT-4 exhibit emergent capabilities across generalpurpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks are not explicitly encoded by the unsupervised, next-token prediction objective. This study investigates how small transformers, trained from random initialization, can efficiently learn arithmetic operations such as addition, multiplication, and elementary functions like square root, using the nexttoken prediction objective. We first demonstrate that conventional training data is not the most effective for arithmetic learning, and simple formatting changes can significantly improve accuracy. This leads to sharp phase transitions as a function of training data scale, which, in some cases, can be explained through connections to low-rank matrix completion. Building on prior work, we then train on chain-of-thought style data that includes intermediate step results. Even in the complete absence of pretraining, this approach significantly and simultaneously improves accuracy, sample complexity, and convergence speed. We also study the interplay between arithmetic and text data during training and examine the effects of few-shot prompting, pretraining, and model scale. Additionally, we discuss length generalization challenges. Our work highlights the importance of high-quality, instructive data that considers the particular characteristics of the next-word prediction objective for rapidly eliciting arithmetic capabilities. 2 * Authors contributed equally to this paper. 2 Our code is available at https://github.com/lee-ny/teaching_arithmetic Preprint. Under review.	Teaching Arithmetic to Small Transformers
d222141728	We study the implicit bias of gradient flow (i.e., gradient descent with infinitesimal step size) on linear neural network training. We propose a tensor formulation of neural networks that includes fully-connected, diagonal, and convolutional networks as special cases, and investigate the linear version of the formulation called linear tensor networks. With this formulation, we can characterize the convergence direction of the network parameters as singular vectors of a tensor defined by the network. For L-layer linear tensor networks that are orthogonally decomposable, we show that gradient flow on separable classification finds a stationary point of the 2/L max-margin problem in a "transformed" input space defined by the network. For underdetermined regression, we prove that gradient flow finds a global minimum which minimizes a norm-like function that interpolates between weighted 1 and 2 norms in the transformed input space. Our theorems subsume existing results in the literature while removing standard convergence assumptions. We also provide experiments that corroborate our analysis. * Based on work performed during internship at Google Research arXiv:2010.02501v3 [cs.LG] 10 Sep 2021Published as a conference paper at ICLR 2021Following previous results (e.g., Lyu & Li (2020); Ji & Telgarsky (2020)), we use the exponential loss (ŷ, y) = exp(−ŷy) for classification problems. For regression, we use the squared error loss (ŷ, y) = 1 2 (ŷ−y) 2 . On the algorithm side, we minimize L using gradient flow, which can be viewed as GD with infinitesimal step size. The gradient flow dynamics is defined as d dt Θ = −∇ Θ L(Θ).RELATED WORKSGradient flow/descent in separable classification. For linear models f (x; z) = x T z with separable data, Soudry et al.(2018)show that the GD run on L drives z to ∞, but z converges in direction to the 2 max-margin classifier. The limit direction of z is aligned with the solution ofwhere the norm in the cost is the 2 norm. Nacson et al. (2019b;c); Gunasekar et al. (2018a); Ji & Telgarsky (2019b;c) extend these results to other (stochastic) algorithms and non-separable settings. Gunasekar et al. (2018b) study the same problem on linear neural networks and show that GD exhibits different implicit biases depending on the architecture. The authors show that the linear coefficients of the network converges in direction to the solution of (1) with different norms: 2 norm for linear fully-connected networks, 2/L (quasi-)norm for diagonal networks, and DFT-domain 2/L (quasi-)norm for convolutional networks with full-length filters. Here, L denotes the depth. We note that Gunasekar et al. (2018b) assume that GD globally minimizes the loss, and the network parameters and the gradient with respect to the linear coefficients converge in direction. Subsequent results (Ji & Telgarsky, 2019a; 2020) remove such assumptions for linear fully-connected networks. A recent line of results (Nacson et al., 2019a; Lyu & Li, 2020; Ji & Telgarsky, 2020) studies general homogeneous models and show divergence of parameters to infinity, monotone increase of smoothed	A UNIFYING VIEW ON IMPLICIT BIAS IN TRAINING LINEAR NEURAL NETWORKS
d252668746	With the increasing need for handling large state and action spaces, general function approximation has become a key technique in reinforcement learning (RL). In this paper, we propose a general framework that unifies model-based and model-free RL, and an Admissible Bellman Characterization (ABC) class that subsumes nearly all Markov Decision Process (MDP) models in the literature for tractable RL. We propose a novel estimation function with decomposable structural properties for optimization-based exploration and the functional eluder dimension as a complexity measure of the ABC class. Under our framework, a new sample-efficient algorithm namely OPtimization-based ExploRation with Approximation (OPERA) is proposed, achieving regret bounds that match or improve over the best-known results for a variety of MDP models. In particular, for MDPs with low Witness rank, under a slightly stronger assumption, OPERA improves the state-of-the-art sample complexity results by a factor of dH. Our framework provides a generic interface to design and analyze new RL models and algorithms. arXiv:2209.15634v1 [cs.LG] 30 Sep 2022	A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
d247476364	Current image harmonization methods consider the entire background as the guidance for harmonization. However, this may limit the capability for user to choose any specific object/person in the background to guide the harmonization. To enable flexible interaction between user and harmonization, we introduce interactive harmonization, a new setting where the harmonization is performed with respect to a selected region in the reference image instead of the entire background. A new flexible framework that allows users to pick certain regions of the background image and use it to guide the harmonization is proposed. Inspired by professional portrait harmonization users, we also introduce a new luminance matching loss to optimally match the color/luminance conditions between the composite foreground and select reference region. This framework provides more control to the image harmonization pipeline achieving visually pleasing portrait edits. Furthermore, we also introduce a new dataset carefully curated for validating portrait harmonization. Extensive experiments on both synthetic and real-world datasets show that the proposed approach is efficient and robust compared to previous harmonization baselines, especially for portraits. Project Webpage at https://jeya-maria-jose.github.io/IPH-web/	Interactive Portrait Harmonization
d253080406	Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning but cannot understand visual information, while vision models such as DALL-E can generate photorealistic photos but fail to understand complex language descriptions. In this work, we propose a unified framework for composing ensembles of different pre-trained models -combining the strengths of each individual model to solve various multimodal problems in a zero-shot manner. We use pre-trained models as "generators" or "scorers" and compose them via closed-loop iterative consensus optimization. The generator constructs proposals and the scorers iteratively provide feedback to refine the generated result. Such closed-loop communication enables models to correct errors caused by other models, significantly boosting performance on downstream tasks, e.g. improving accuracy on grade school math problems by 7.5%, without requiring any model finetuning. We demonstrate that consensus achieved by an ensemble of scorers outperforms the feedback of a single scorer, by leveraging the strengths of each expert model. Results show that the proposed method can be used as a general purpose framework for a wide range of zero-shot multimodal tasks, such as image generation, video question answering, mathematical reasoning, and robotic manipulation. Project page: https://energy-basedmodel.github.io/composing-pretrained-models. * Correspondence to: Shuang Li <[email protected]>. † indicates equal contribution. Shuang Li did all the experiments on image generation, video question answering, and mathematical reasoning. Yilun Du did all the experiments on robot manipulation.	COMPOSING ENSEMBLES OF PRE-TRAINED MODELS VIA ITERATIVE CONSENSUS
d259833441	Real-world classifiers can benefit from the option of abstaining from predicting on samples where they have low confidence. Such abstention is particularly useful on samples which are close to the learned decision boundary, or which are outliers with respect to the training sample. These settings have been the subject of extensive but disjoint study in the selective classification (SC) and out-of-distribution (OOD) detection literature. Recent work on selective classification with OOD detection (SCOD) has argued for the unified study of these problems; however, the formal underpinnings of this problem are still nascent, and existing techniques are heuristic in nature. In this paper, we propose new plugin estimators for SCOD that are theoretically grounded, effective, and generalise existing approaches from the SC and OOD detection literature. In the course of our analysis, we formally explicate how naïve use of existing SC and OOD detection baselines may be inadequate for SCOD. We empirically demonstrate that our approaches yields competitive SC and OOD detection performance compared to baselines from both literatures.Black-box SCODLoss-based SCOD Training data ID data only ID + OOD data SC score ssc Any off-the-shelf technique, e.g., maximum softmax probability [7]Minimise (10) or(11), obtain max y∈ [L] fy(x)OOD score s ood Any off-the-shelf technique, e.g., gradient norm [23]Minimise(10)or(11), obtain s(x)Rejection ruleCombine ssc, s ood via(8)Combine ssc, s ood via(8)or(15)detection(UD)in Kim et al. [27], and selective classification with OOD detection (SCOD) in Xia and Bouganis [53]; we adopt the latter in the sequel. One may view SCOD as a unification of OOD detection and the classical selective classification (SC) paradigm[7,1,10,41,47,39,6]. Both OOD detection and SC have well-established formal underpinnings, with accompanying principled techniques [3, 10, 41]; however, by contrast, the understanding of SCOD is still nascent. In particular, existing SCOD approaches either employ OOD detection baselines [27], or heuristic design choices [53]. It remains unclear if there are conditions where such approaches may fail, and whether there are effective, theoretically grounded alternatives.In this paper, we provide a statistical formulation for the SCOD problem, and design two novel plug-in estimators for SCOD that operate under different assumptions on available data during training(Table 1). The first estimator addresses the challenging setting where one has access to only ID data, and leverages existing techniques for SC and OOD detection in a black-box manner. The second estimator addresses the setting where one additionally access to a "wild" sample comprising a mixture of both ID and OOD data [26], and involves the design of novel loss functions with consistency guarantees. Both estimators generalise existing approaches from the SC and OOD detection literature, and thus offer a unified means of reasoning about both problems. In sum, our contributions are: (i) We provide a statistical formulation for SCOD that unifies both the SC and OOD detection problems ( §3), and derive the corresponding Bayes-optimal solution (Lemma 3.1). Intriguingly this solution is a variant of the popular maximum softmax probability baseline for SC and OOD detection [7, 18], using a sample-dependent rather than constant threshold.(ii) Based on the form of the Bayes-optimal solution, we propose two new plug-in approaches for SCOD ( §4). These operate in settings with access to only ID data ( §4.1), and access to a mixture of ID and OOD data ( §4.2) respectively, and generalise existing SC and OOD detection techniques.(iii) Experiments on benchmark image classification datasets ( §5) show that our plug-in approaches yield competitive classification and OOD detection performance at any desired abstention rate, compared to a range of both SC and OOD detection baselines.2	Plugin estimators for selective classification with out-of-distribution detection
d238583049	Deep Reinforcement Learning (RL) is mainly studied in a setting where the training and the testing environments are similar. But in many practical applications, these environments may differ. For instance, in control systems, the robot(s) on which a policy is learned might differ from the robot(s) on which a policy will run. It can be caused by different internal factors (e.g., calibration issues, system attrition, defective modules) or also by external changes (e.g., weather conditions). There is a need to develop RL methods that generalize well to variations of the training conditions. In this article, we consider the simplest yet hard to tackle generalization setting where the test environment is unknown at train time, forcing the agent to adapt to the system's new dynamics. This online adaptation process can be computationally expensive (e.g., fine-tuning) and cannot rely on meta-RL techniques since there is just a single train environment. To do so, we propose an approach where we learn a subspace of policies within the parameter space. This subspace contains an infinite number of policies that are trained to solve the training environment while having different parameter values. As a consequence, two policies in that subspace process information differently and exhibit different behaviors when facing variations of the train environment. Our experiments 1 carried out over a large variety of benchmarks compare our approach with baselines, including diversity-based methods. In comparison, our approach is simple to tune, does not need any extra component (e.g., discriminator) and learns policies able to gather a high reward on unseen environments.	LEARNING A SUBSPACE OF POLICIES FOR ONLINE ADAPTATION IN REINFORCEMENT LEARNING
d252439127	The design choices in the Transformer attention mechanism, including weak inductive bias and quadratic computational complexity, have limited its application for modeling long sequences.In this paper, we introduce Mega, a simple, theoretically grounded, single-head gated attention mechanism equipped with (exponential) moving average to incorporate inductive bias of position-aware local dependencies into the position-agnostic attention mechanism.We further propose a variant of Mega that offers linear time and space complexity yet yields only minimal quality loss, by efficiently splitting the whole sequence into multiple chunks with fixed length.Extensive experiments on a wide range of sequence modeling benchmarks, including the Long Range Arena, neural machine translation, autoregressive language modeling, and image and speech classification, show that Mega achieves significant improvements over other sequence models, including variants of Transformers and recent state space models. 1	Mega: Moving Average Equipped Gated Attention
d173991084	Backpropagation is driving today's artificial neural networks (ANNs). However, despite extensive research, it remains unclear if the brain implements this algorithm. Among neuroscientists, reinforcement learning (RL) algorithms are often seen as a realistic alternative: neurons can randomly introduce change, and use unspecific feedback signals to observe their effect on the cost and thus approximate their gradient. However, the convergence rate of such learning scales poorly with the number of involved neurons. Here we propose a hybrid learning approach. Each neuron uses an RL-type strategy to learn how to approximate the gradients that backpropagation would provide. We provide proof that our approach converges to the true gradient for certain classes of networks. In both feedforward and convolutional networks, we empirically show that our approach learns to approximate the gradient, and can match or the performance of exact gradient-based learning. Learning feedback weights provides a biologically plausible mechanism of achieving good performance, without the need for precise, pre-specified learning rules.	LEARNING TO SOLVE THE CREDIT ASSIGNMENT PROBLEM
d232075892	Custom voice, a specific text to speech (TTS) service in commercial speech platforms, aims to adapt a source TTS model to synthesize personal voice for a target speaker using few speech from her/him. Custom voice presents two unique challenges for TTS adaptation: 1) to support diverse customers, the adaptation model needs to handle diverse acoustic conditions which could be very different from source speech data, and 2) to support a large number of customers, the adaptation parameters need to be small enough for each target speaker to reduce memory usage while maintaining high voice quality. In this work, we propose AdaSpeech, an adaptive TTS system for high-quality and efficient customization of new voices. We design several techniques in AdaSpeech to address the two challenges in custom voice: 1) To handle different acoustic conditions, we model the acoustic information in both utterance and phoneme level. Specifically, we use one acoustic encoder to extract an utterance-level vector and another one to extract a sequence of phoneme-level vectors from the target speech during pre-training and fine-tuning; in inference, we extract the utterance-level vector from a reference speech and use an acoustic predictor to predict the phonemelevel vectors. 2) To better trade off the adaptation parameters and voice quality, we introduce conditional layer normalization in the mel-spectrogram decoder of AdaSpeech, and fine-tune this part in addition to speaker embedding for adaptation. We pre-train the source TTS model on LibriTTS datasets and fine-tune it on VCTK and LJSpeech datasets (with different acoustic conditions from LibriTTS) with few adaptation data, e.g., 20 sentences, about 1 minute speech. Experiment results show that AdaSpeech achieves much better adaptation quality than baseline methods, with only about 5K specific parameters for each speaker, which demonstrates its effectiveness for custom voice. The audio samples are available at Published as a conference paper at ICLR 2021 on the naturalness and similarity of adapted voice. Furthermore, there are also several distinctive challenges in custom voice: 1) The recordings of the custom users are usually of different acoustic conditions from the source speech data (the data to train the source TTS model). For example, the adaptation data is usually recorded with diverse speaking prosodies, styles, emotions, accents and recording environments. The mismatch in these acoustic conditions makes the source model difficult to generalize and leads to poor adaptation quality. 2) When adapting the source TTS model to a new voice, there is a trade-off between the fine-tuning parameters and voice quality. Generally speaking, more adaptation parameters will usually result in better voice quality, which, as a result, increases the memory storage and serving cost 1 .While previous works in TTS adaptation have well considered the few adaptation data setting in custom voice, they have not fully addressed the above challenges. They fine-tune the whole modelKons et al., 2019)or decoder part(Moss et al., 2020;, achieving good quality but causing too many adaptation parameters. Reducing the amount of adaptation parameters is necessary for the deployment of commercialized custom voice. Otherwise, the memory storage would explode as the increase of users. Some works only fine-tune the speaker embedding , or train a speaker encoder moduleJia et al., 2018;Cooper et al., 2020;Wan et al., 2018)that does not need fine-tuning during adaptation. While these approaches lead a light-weight and efficient adaptation, they result in poor adaptation quality. Moreover, most previous works assume the source speech data and adaptation data are in the same domain and do not consider the setting with different acoustic conditions, which is not practical in custom voice scenarios.	ADASPEECH: ADAPTIVE TEXT TO SPEECH FOR CUSTOM VOICE
d213529244	Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning. In this paper, we show how to make more effective use of the model by exploiting its differentiability. We construct a policy optimization algorithm that uses the pathwise derivative of the learned model and policy across future timesteps. Instabilities of learning across many timesteps are prevented by using a terminal value function, learning the policy in an actor-critic fashion. Furthermore, we present a derivation on the monotonic improvement of our objective in terms of the gradient error in the model and value function. We show that our approach (i) is consistently more sample efficient than existing state-of-the-art model-based algorithms, (ii) matches the asymptotic performance of model-free algorithms, and (iii) scales to long horizons, a regime where typically past model-based approaches have struggled.	MODEL-AUGMENTED ACTOR-CRITIC: BACKPROPAGATING THROUGH PATHS
d231918471	Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not scale well beyond the small tabular setting due to the need for an inner-loop MDP solver, and even non-Bayesian methods that do themselves scale often require extensive interaction with the environment to perform well, being inappropriate for high stakes or costly applications such as healthcare. In this paper we introduce our method, Approximate Variational Reward Imitation Learning (AVRIL), that addresses both of these issues by jointly learning an approximate posterior distribution over the reward that scales to arbitrarily complicated state spaces alongside an appropriate policy in a completely offline manner through a variational approach to said latent reward. Applying our method to real medical data alongside classic control simulations, we demonstrate Bayesian reward inference in environments beyond the scope of current methods, as well as task performance competitive with focused offline imitation learning algorithms.	SCALABLE BAYESIAN INVERSE REINFORCEMENT LEARNING
d7942973	This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7% UAS and 94.1% LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmarkoutperforming Kiperwasser & Goldberg (2016) by 1.8% and 2.2%-and comparable to the highest performing transition-based parser(Kuncoro et al., 2016), which achieves 95.8% UAS and 94.6% LAS. We also show which hyperparameter choices had a significant effect on parsing accuracy, allowing us to achieve large gains over other graph-based approaches.	DEEP BIAFFINE ATTENTION FOR NEURAL DEPENDENCY PARSING
d1684853	Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich function approximators like deep neural networks. Model-based methods where the real-world target domain is approximated using a simulated source domain provide an avenue to tackle the above challenges by augmenting real data with simulated data. However, discrepancies between the simulated source domain and the target domain pose a challenge for simulated training. We introduce the EPOpt algorithm, which uses an ensemble of simulated source domains and a form of adversarial training to learn policies that are robust and generalize to a broad range of possible target domains, including unmodeled effects. Further, the probability distribution over source domains in the ensemble can be adapted using data from target domain and approximate Bayesian methods, to progressively make it a better approximation. Thus, learning on a model ensemble, along with source domain adaptation, provides the benefit of both robustness and learning/adaptation.	EPOPT: LEARNING ROBUST NEURAL NETWORK POLICIES USING MODEL ENSEMBLES
d250451381	Reinforcement Learning (RL) agents are often unable to generalise well to environment variations in the state space that were not observed during training. This issue is especially problematic for image-based RL, where a change in just one variable, such as the background colour, can change many pixels in the image. The changed pixels can lead to drastic changes in the agent's latent representation of the image, causing the learned policy to fail. To learn more robust representations, we introduce TEmporal Disentanglement (TED), a self-supervised auxiliary task that leads to disentangled image representations exploiting the sequential nature of RL observations. We find empirically that RL algorithms utilising TED as an auxiliary task adapt more quickly to changes in environment variables with continued training compared to state-of-the-art representation learning methods. Since TED enforces a disentangled structure of the representation, our experiments also show that policies trained with TED generalise better to unseen values of variables irrelevant to the task (e.g. background colour) as well as unseen values of variables that affect the optimal policy (e.g. goal positions).	TEMPORAL DISENTANGLEMENT OF REPRESENTATIONS FOR IMPROVED GENERALISATION IN REINFORCEMENT LEARNING
d254044220	The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously to similar advances in vision and NLP. However, recent works argue that offline RL methods encounter unique challenges to scaling up model capacity. Drawing on the learnings from these works, we re-examine previous design choices and find that with appropriate choices: ResNets, cross-entropy based distributional backups, and feature normalization, offline Q-learning algorithms exhibit strong performance that scales with model capacity. Using multi-task Atari as a testbed for scaling and generalization, we train a single policy on 40 games with near-human performance using up-to 80 million parameter networks, finding that model performance scales favorably with capacity. In contrast to prior work, we extrapolate beyond dataset performance even when trained entirely on a large (400M transitions) but highly suboptimal dataset (51% human-level performance). Compared to return-conditioned supervised approaches, offline Q-learning scales similarly with model capacity and has better performance, especially when the dataset is suboptimal. Finally, we show that offline Q-learning with a diverse dataset is sufficient to learn powerful representations that facilitate rapid transfer to novel games and fast online learning on new variations of a training game, improving over existing state-of-the-art representation learning approaches. * Co-senior authors	OFFLINE Q-LEARNING ON DIVERSE MULTI-TASK DATA BOTH SCALES AND GENERALIZES
d246996534	The literature has proposed several methods to finetune pretrained GANs on new datasets, which typically results in higher performance compared to training from scratch, especially in the limited-data regime. However, despite the apparent empirical benefits of GAN pretraining, its inner mechanisms were not analyzed indepth, and understanding of its role is not entirely clear. Moreover, the essential practical details, e.g., selecting a proper pretrained GAN checkpoint, currently do not have rigorous grounding and are typically determined by trial and error. This work aims to dissect the process of GAN finetuning. First, we show that initializing the GAN training process by a pretrained checkpoint primarily affects the model's coverage rather than the fidelity of individual samples. Second, we explicitly describe how pretrained generators and discriminators contribute to the finetuning process and explain the previous evidence on the importance of pretraining both of them. Finally, as an immediate practical benefit of our analysis, we describe a simple recipe to choose an appropriate GAN checkpoint that is the most suitable for finetuning to a particular target task. Importantly, for most of the target tasks, Imagenet-pretrained GAN, despite having poor visual quality, appears to be an excellent starting point for finetuning, resembling the typical pretraining scenario of discriminative computer vision models. * Indicates equal contribution arXiv:2202.08937v2 [cs.LG]	WHEN, WHY, AND WHICH PRETRAINED GANS ARE USEFUL?
d53114258	Imitation learning provides an appealing framework for autonomous control: in many tasks, demonstrations of preferred behavior can be readily obtained from human experts, removing the need for costly and potentially dangerous online data collection in the real world. However, policies learned with imitation learning have limited flexibility to accommodate varied goals at test time. Model-based reinforcement learning (MBRL) offers considerably more flexibility, since a predictive model learned from data can be used to achieve various goals at test time. However, MBRL suffers from two shortcomings. First, the predictive model does not help to choose desired or safe outcomes -it reasons only about what is possible, not what is preferred. Second, MBRL typically requires additional online data collection to ensure that the model is accurate in those situations that are actually encountered when attempting to achieve test time goals. Collecting this data with a partially trained model can be dangerous and time-consuming. In this paper, we aim to combine the benefits of imitation learning and MBRL, and propose imitative models: probabilistic predictive models able to plan expert-like trajectories to achieve arbitrary goals. We find this method substantially outperforms both direct imitation and MBRL in a simulated autonomous driving task, and can be learned efficiently from a fixed set of expert demonstrations without additional online data collection. We also show our model can flexibly incorporate user-supplied costs as test-time, can plan to sequences of goals, and can even perform well with imprecise goals, including goals on the wrong side of the road.	DEEP IMITATIVE MODELS FOR FLEXIBLE INFERENCE, PLANNING, AND CONTROL
d247411320	While recent automated data augmentation methods lead to state-of-the-art results, their design spaces and the derived data augmentation strategies still incorporate strong human priors. In this work, instead of fixing a set of hand-picked default augmentations alongside the searched data augmentations, we propose a fully automated approach for data augmentation search named Deep AutoAugment (DeepAA). DeepAA progressively builds a multi-layer data augmentation pipeline from scratch by stacking augmentation layers one at a time until reaching convergence. For each augmentation layer, the policy is optimized to maximize the cosine similarity between the gradients of the original and augmented data along the direction with low variance. Our experiments show that even without default augmentations, we can learn an augmentation policy that achieves strong performance with that of previous works. Extensive ablation studies show that the regularized gradient matching is an effective search method for data augmentation policies. Our code is available at: https://github.com/MSU-MLSys-Lab/DeepAA.	DEEP AUTOAUGMENT
d252682980	Offline reinforcement learning, which aims at optimizing sequential decision-making strategies with historical data, has been extensively applied in real-life applications. State-Of-The-Art algorithms usually leverage powerful function approximators (e.g. neural networks) to alleviate the sample complexity hurdle for better empirical performances. Despite the successes, a more systematic understanding of the statistical complexity for function approximation remains lacking. Towards bridging the gap, we take a step by considering offline reinforcement learning with differentiable function class approximation (DFA). This function class naturally incorporates a wide range of models with nonlinear/nonconvex structures. Most importantly, we show offline RL with differentiable function approximation is provably efficient by analyzing the pessimistic fitted Q-learning (PFQL) algorithm, and our results provide the theoretical basis for understanding a variety of practical heuristics that rely on Fitted Q-Iteration style design. In addition, we further improve our guarantee with a tighter instance-dependent characterization. We hope our work could draw interest in studying reinforcement learning with differentiable function approximation beyond the scope of current research. max 1 n ) which lack the characterizations of individual instance behaviors. However, as mentioned in Zanette and Brunskill [2019], practical reinforcement learning algorithms often perform far better than what these problem-independent bounds would suggest.These observations motivate us to consider function approximation schemes that can help address the existing limitations. In particular, in this work we consider offline reinforcement learning with differentiable function class approximations. Its definition is given in below.	Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient
d235358397	In real-world systems, models are frequently updated as more data becomes available, and in addition to achieving high accuracy, the goal is to also maintain a low difference in predictions compared to the base model (i.e. predictive "churn"). If model retraining results in vastly different behavior, then it could cause negative effects in downstream systems, especially if this churn can be avoided with limited impact on model accuracy. In this paper, we show an equivalence between training with distillation using the base model as the teacher and training with an explicit constraint on the predictive churn. We then show that distillation performs strongly for low churn training against a number of recent baselines on a wide range of datasets and model architectures, including fully-connected networks, convolutional networks, and transformers.	CHURN REDUCTION VIA DISTILLATION
d249210151	We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals.In contrast to common Euclidean costs, i.e., ℓ 1 or ℓ 2 , such functionals provide more flexibility and allow using auxiliary information, such as class labels, to construct the required transport map.Existing methods for general costs are discrete and have limitations in practice, i.e. they do not provide an out-of-sample estimation.We address the challenge of designing a continuous OT approach for general costs that generalizes to new data points in high-dimensional spaces, such as images.Additionally, we provide the theoretical error analysis for our recovered transport plans.As an application, we construct a cost functional to map data distributions while preserving the class-wise structure.	NEURAL OPTIMAL TRANSPORT WITH GENERAL COST FUNCTIONALS
d252668614	Machine learning algorithms typically assume independent and identically distributed samples in training and at test time. Much work has shown that highperforming ML classifiers can degrade significantly and provide overly-confident, wrong classification predictions, particularly for out-of-distribution (OOD) inputs. Conditional language models (CLMs) are predominantly trained to classify the next token in an output sequence, and may suffer even worse degradation on OOD inputs as the prediction is done auto-regressively over many steps. Furthermore, the space of potential low-quality outputs is larger as arbitrary text can be generated and it is important to know when to trust the generated output. We present a highly accurate and lightweight OOD detection method for CLMs, and demonstrate its effectiveness on abstractive summarization and translation. We also show how our method can be used under the common and realistic setting of distribution shift for selective generation (analogous to selective prediction for classification) of high-quality outputs, while automatically abstaining from low-quality ones, enabling safer deployment of generative language models.Published as a conference paper at ICLR 2023 detection. In Section 2.2, we propose a highly-accurate, simple, and lightweight OOD score based on the model's input and output representations (or embeddings) to detect OOD examples, requiring negligible additional compute beyond the model itself.	OUT-OF-DISTRIBUTION DETECTION AND SELECTIVE GENERATION FOR CONDITIONAL LANGUAGE MODELS
d21946795	Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafted using fast single-step methods that maximize a linear approximation of the model's loss. We show that this form of adversarial training converges to a degenerate global minimum, wherein small curvature artifacts near the data points obfuscate a linear approximation of the loss. The model thus learns to generate weak perturbations, rather than defend against strong ones. As a result, we find that adversarial training remains vulnerable to black-box attacks, where we transfer perturbations computed on undefended models, as well as to a powerful novel single-step attack that escapes the non-smooth vicinity of the input data via a small random step. We further introduce Ensemble Adversarial Training, a technique that augments training data with perturbations transferred from other models. On ImageNet, Ensemble Adversarial Training yields models with stronger robustness to blackbox attacks. In particular, our most robust model won the first round of the NIPS 2017 competition on Defenses against Adversarial Attacks (Kurakin et al., 2017c). However, subsequent work found that more elaborate black-box attacks could significantly enhance transferability and reduce the accuracy of our models.	ENSEMBLE ADVERSARIAL TRAINING: ATTACKS AND DEFENSES
d219636462	We propose a general, yet simple patch that can be applied to existing regularizationbased continual learning methods called classifier-projection regularization (CPR). Inspired by both recent results on neural networks with wide local minima and information theory, CPR adds an additional regularization term that maximizes the entropy of a classifier's output probability. We demonstrate that this additional term can be interpreted as a projection of the conditional probability given by a classifier's output to the uniform distribution. By applying the Pythagorean theorem for KL divergence, we then prove that this projection may (in theory) improve the performance of continual learning methods. In our extensive experimental results, we apply CPR to several state-of-the-art regularization-based continual learning methods and benchmark performance on popular image recognition datasets. Our results demonstrate that CPR indeed promotes a wide local minima and significantly improves both accuracy and plasticity while simultaneously mitigating the catastrophic forgetting of baseline continual learning methods.Preprint. Under review.	CPR: Classifier-Projection Regularization for Continual Learning
d222141662	We introduce a notion of usable information contained in the representation learned by a deep network, and use it to study how optimal representations for the task emerge during training, and how they adapt to different tasks. We use this to characterize the transient dynamics of deep neural networks on perceptual decision-making tasks inspired by neuroscience literature. In particular, we show that both the random initialization and the implicit regularization from Stochastic Gradient Descent play an important role in learning minimal sufficient representations for the task. If the network is not randomly initialized, we show that the training may not recover an optimal representation, increasing the chance of overfitting.	USABLE INFORMATION AND EVOLUTION OF OPTIMAL REPRESENTATIONS DURING TRAINING
d252907593	In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and masked data modeling, two major self-supervised learning frameworks, to learn a joint and coordinated audio-visual representation. Our experiments show that the contrastive audio-visual correspondence learning objective not only enables the model to perform audio-visual retrieval tasks, but also helps the model learn a better joint representation. As a result, our fully self-supervised pretrained CAV-MAE achieves a new SOTA accuracy of 65.9% on VGGSound, and is comparable with the previous best supervised pretrained model on AudioSet in the audio-visual event classification task. Code and pretrained models are at https	CONTRASTIVE AUDIO-VISUAL MASKED AUTOENCODER
d203593909	Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's prediction (i.e. the pseudo-parallel data). While self-training has been extensively studied on classification problems, in complex sequence generation tasks (e.g. machine translation) it is still unclear how self-training works due to the compositionality of the target space. In this work, we first empirically show that selftraining is able to decently improve the supervised baseline on neural sequence generation tasks. Through careful examination of the performance gains, we find that the perturbation on the hidden states (i.e. dropout) is critical for self-training to benefit from the pseudo-parallel data, which acts as a regularizer and forces the model to yield close predictions for similar unlabeled inputs. Such effect helps the model correct some incorrect predictions on unlabeled data. To further encourage this mechanism, we propose to inject noise to the input space, resulting in a "noisy" version of self-training. Empirical study on standard machine translation and text summarization benchmarks shows that noisy self-training is able to effectively utilize unlabeled data and improve the performance of the supervised baseline by a large margin.	REVISITING SELF-TRAINING FOR NEURAL SEQUENCE GENERATION
d219708742	The universal approximation property of width-bounded networks has been studied as a dual of classical universal approximation results on depth-bounded networks.However, the critical width enabling the universal approximation has not been exactly characterized in terms of the input dimension d x and the output dimension d y .In this work, we provide the first definitive result in this direction for networks using the ReLU activation functions: The minimum width required for the universal approximation of the L p functions is exactly max{d x + 1, d y }.We also prove that the same conclusion does not hold for the uniform approximation with ReLU, but does hold with an additional threshold activation function.Our proof technique can be also used to derive a tighter upper bound on the minimum width required for the universal approximation using networks with general activation functions.	Minimum Width for Universal Approximation
d220768638	We study the problem of unsupervised physical object discovery. Unlike existing frameworks that aim to learn to decompose scenes into 2D segments purely based on each object's appearance, we explore how physics, especially object interactions, facilitates learning to disentangle and segment instances from raw videos, and to infer the 3D geometry and position of each object, all without supervision. Drawing inspiration from developmental psychology, our Physical Object Discovery Network (POD-Net) uses both multi-scale pixel cues and physical motion cues to accurately segment observable and partially occluded objects of varying sizes, and infer properties of those objects. Our model reliably segments objects on both synthetic and real scenes. The discovered object properties can also be used to reason about physical events.Preprint. Under review.	Unsupervised Discovery of 3D Physical Objects from Video
d84186721	Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using "arrow-pushing" diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data. Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of (a) being easy for chemists to interpret, (b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and (c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants. We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings. Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines. Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so.Recently, there have been a number of machine learning models proposed for directly predicting the products of chemical reactions(Coley et al., 2017;Jin et al., 2017; Schwaller et al., 2018; Segler and Waller, 2017a; Segler et al., 2018; Wei et al., 2016), largely using graph-based or machine translation models. The task of reaction product prediction is shown on the left-hand side ofFigure 1.In this paper we propose a machine learning model to predict the reaction mechanism, as shown on the right-hand side ofFigure 1, for a particularly important subset of organic reactions. We argue that our + reactant 1 reactant 2 reagent target product prediction product 1 product 2 + reactant 1 reactant 2 reagent mechanism prediction target target 1 2 3Figure 1: (Left) The reaction product prediction problem: Given the reactants and reagents, predict the structure of the product. (Right) The reaction mechanism prediction problem: Given the reactants and reagents, predict how the reaction occurred to form the products.model is not only more interpretable than product prediction models, but also allows easier encoding of constraints imposed by chemistry. Proposed approaches to predicting reaction mechanisms have often been based on combining hand-coded heuristics and quantum mechanics(Bergeler et al., 2015;Kim et al., 2018; Nandi et al., 2017; Segler and Waller, 2017b; Rappoport et al., 2014; Simm and Reiher, 2017; Zimmerman, 2013), rather than using machine learning. We call our model ELECTRO, as it directly predicts the path of electrons through molecules (i.e., the reaction mechanism). To train the model we devise a general technique to obtain approximate reaction mechanisms purely from data about the reactants and products. This allows one to train our a model on large, unannotated reaction datasets such as USPTO (Lowe, 2012). We demonstrate that not only does our model achieve impressive results, surprisingly it also learns chemical properties it was not explicitly trained on.	A GENERATIVE MODEL FOR ELECTRON PATHS
d254535921	Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction.To do so, offline RL methods must handle distributional shift between the dataset and the learned policy.The most common approach is to learn conservative, or lower-bound, value functions, which underestimate the return of out-of-distribution (OOD) actions.However, such methods exhibit one notable drawback: policies optimized on such value functions can only behave according to a fixed, possibly suboptimal, degree of conservatism.However, this can be alleviated if we instead are able to learn policies for varying degrees of conservatism at training time and devise a method to dynamically choose one of them during evaluation.To do so, in this work, we propose learning value functions that additionally condition on the degree of conservatism, which we dub confidence-conditioned value functions.We derive a new form of a Bellman backup that simultaneously learns Q-values for any degree of confidence with high probability.By conditioning on confidence, our value functions enable adaptive strategies during online evaluation by controlling for confidence level using the history of observations thus far.This approach can be implemented in practice by conditioning the Q-function from existing conservative algorithms on the confidence.We theoretically show that our learned value functions produce conservative estimates of the true value at any desired confidence.Finally, we empirically show that our algorithm outperforms existing conservative offline RL algorithms on multiple discrete control domains.	CONFIDENCE-CONDITIONED VALUE FUNCTIONS FOR OFFLINE REINFORCEMENT LEARNING
d203591409	Identifiability, or recovery of the true latent representations from which the observed data originates, is a fundamental goal of representation learning. However, most deep generative models do not address the question of identifiability, and cannot recover the true latent sources that generate the observations. Recent work proposed identifiable generative modelling using variational autoencoders (iVAE) with a theory of identifiability. However, due to the intractablity of KL divergence between variational approximate posterior and the true posterior, iVAE has to maximize the evidence lower bound of the marginal likelihood, leading to suboptimal solutions in both theory and practice. In contrast, we propose an identifiable framework for estimating latent representations using a flow-based model (iFlow). Our approach directly maximizes the marginal likelihood, allowing for theoretical guarantees on identifiability, without the need for variational approximations. We derive its learning objective in analytical form, making it possible to train iFlow in an end-to-end manner. Simulations on synthetic data validate the correctness and effectiveness of our proposed method and demonstrate its practical advantages over other existing methods.Recently, Khemakhem et al. (2019) introduced a theory of identifiability for deep generative models, based upon which they proposed an identifiable variant of VAEs called iVAE, to learn the distribu-1 arXiv:1909.12555v1 [cs.LG]	IDENTIFYING THROUGH FLOWS FOR RECOVERING LATENT REPRESENTATIONS
d1880070	Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and systemlevel. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.	Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
d263829563	There is growing evidence that pretraining on high quality, carefully thought-out tokens such as code or mathematics plays an important role in improving the reasoning abilities of large language models.For example, Minerva, a PaLM model finetuned on billions of tokens of mathematical documents from arXiv and the web, reported dramatically improved performance on problems that require quantitative reasoning.However, because all known publicly released web datasets employ preprocessing that does not faithfully preserve mathematical notation, the benefits of large scale training on quantitive web documents are unavailable to the research community.We introduce OpenWebMath, an open dataset inspired by these works containing 14.7B tokens of mathematical webpages from Common Crawl.We describe in detail our method for extracting text and L A T E X content and removing boilerplate from HTML documents, as well as our methods for quality filtering and deduplication.Additionally, we run small-scale experiments by training 1.4B parameter language models on OpenWebMath, showing that models trained on 14.7B tokens of our dataset surpass the performance of models trained on over 20x the amount of general language data.We hope that our dataset, openly released on the Hugging Face Hub, will help spur advances in the reasoning abilities of large language models.* Keiran and Marco created the dataset and Zhangir led model training and evaluation. 1 https://commoncrawl.org/	OPENWEBMATH: AN OPEN DATASET OF HIGH-QUALITY MATHEMATICAL WEB TEXT
d252762187	Neural network weights are typically initialized at random from univariate distributions, controlling just the variance of individual weights even in highlystructured operations like convolutions. Recent ViT-inspired convolutional networks such as ConvMixer and ConvNeXt use large-kernel depthwise convolutions whose learned filters have notable structure; this presents an opportunity to study their empirical covariances. In this work, we first observe that such learned filters have highly-structured covariance matrices, and moreover, we find that covariances calculated from small networks may be used to effectively initialize a variety of larger networks of different depths, widths, patch sizes, and kernel sizes, indicating a degree of model-independence to the covariance structure. Motivated by these findings, we then propose a learning-free multivariate initialization scheme for convolutional filters using a simple, closed-form construction of their covariance. Models using our initialization outperform those using traditional univariate initializations, and typically meet or exceed the performance of those initialized from the covariances of learned filters; in some cases, this improvement can be achieved without training the depthwise convolutional filters at all. arXiv:2210.03651v1 [cs.CV] 7 Oct 2022 Preprint convergence. Models using our initialization often see gains of over 1% accuracy on CIFAR-10 and short-training ImageNet classification; it also leads to small but significant performance gains on full-scale, ≈ 80%-accuracy ImageNet training. Indeed, in some cases our initialization works so well that it outperforms uniform initialization even when the filters aren't trained at all. And our initialization is almost completely free to compute. Saxe et al. (2013) proposed to replace random i.i.d. Gaussian weights with random orthogonal matrices, a constraint in which weights depend on each other and are thus, in some sense, "multivariate"; Xiao et al. (2018) also proposed an orthogonal initialization for convolutions. Similarly to these works, our initialization greatly improves the trainability of deep (depthwise) convolutional networks, but is much simpler and constraint-free, being just a random sample from a multivariate Gaussian distribution. Martens et al. (2021) uses "Gaussian Delta initialization" for convolutions; while largely unrelated to our technique both in form and motivation, this is similar to our initialization as applied in the first layer (i.e., the lowest-variance case). Zhang et al. (2022) suggests that the main purpose of pre-training may be to find a good initialization, and crafts a mimicking initialization based on observed, desirable information transfer patterns. We similarly initialize convolutional filters to be closer to those found in pre-trained models, but do so in a completely random and simpler manner. Romero et al. (2021) proposes an analytic parameterization of variable-size convolutions, based in part on Gaussian filters; while our covariance construction is also analytic and built upon Gaussian filters, we use them to specify the distribution of filters. Our contribution is most advantageous for large-filter convolutions, which have become prevalent in recent work: ConvNeXt (Liu et al., 2022b) uses 7 × 7 convolutions, and ConvMixer (Trockman & Kolter, 2022) uses 9 × 9; taking the trend a step further, Ding et al. (2022) uses 31 × 31, and Liu et al. (2022a) uses 51 × 51 sparse convolutions. Many other works argue for large-filter convolutions (Wang et al., 2022; Chen et al., 2022; Han et al., 2021).Related work	UNDERSTANDING THE COVARIANCE STRUCTURE OF CONVOLUTIONAL FILTERS

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LitSearchRetrieval

An MTEB dataset

Massive Text Embedding Benchmark

    The dataset contains the query set and retrieval corpus for the paper LitSearch: A Retrieval Benchmark for
    Scientific Literature Search. It introduces LitSearch, a retrieval benchmark comprising 597 realistic literature
    search queries about recent ML and NLP papers. LitSearch is constructed using a combination of (1) questions
    generated by GPT-4 based on paragraphs containing inline citations from research papers and (2) questions about
    recently published papers, manually written by their authors. All LitSearch questions were manually examined or
    edited by experts to ensure high quality.


Task category	t2t
Domains	Academic, Non-fiction, Written
Reference	https://github.com/princeton-nlp/LitSearch

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_tasks(["LitSearchRetrieval"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repitory.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.


@article{ajith2024litsearch,
  author = {Ajith, Anirudh and Xia, Mengzhou and Chevalier, Alexis and Goyal, Tanya and Chen, Danqi and Gao, Tianyu},
  title = {LitSearch: A Retrieval Benchmark for Scientific Literature Search},
  year = {2024},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("LitSearchRetrieval")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 64780,
        "number_of_characters": 58371129,
        "num_documents": 64183,
        "min_document_length": 0,
        "average_document_length": 908.135035757132,
        "max_document_length": 18451,
        "unique_documents": 64183,
        "num_queries": 597,
        "min_query_length": 37,
        "average_query_length": 141.20268006700167,
        "max_query_length": 327,
        "unique_queries": 597,
        "none_queries": 0,
        "num_relevant_docs": 639,
        "min_relevant_docs_per_query": 1,
        "average_relevant_docs_per_query": 1.07035175879397,
        "max_relevant_docs_per_query": 5,
        "unique_relevant_docs": 574,
        "num_instructions": null,
        "min_instruction_length": null,
        "average_instruction_length": null,
        "max_instruction_length": null,
        "unique_instructions": null,
        "num_top_ranked": null,
        "min_top_ranked_per_query": null,
        "average_top_ranked_per_query": null,
        "max_top_ranked_per_query": null
    }
}

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