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d244799256 | Recent advances at the intersection of dense large graph limits and mean field games have begun to enable the scalable analysis of a broad class of dynamical sequential games with large numbers of agents. So far, results have been largely limited to graphon mean field systems with continuous-time diffusive or jump dynamics, typically without control and with little focus on computational methods. We propose a novel discrete-time formulation for graphon mean field games as the limit of non-linear dense graph Markov games with weak interaction. On the theoretical side, we give extensive and rigorous existence and approximation properties of the graphon mean field solution in sufficiently large systems. On the practical side, we provide general learning schemes for graphon mean field equilibria by either introducing agent equivalence classes or reformulating the graphon mean field system as a classical mean field system. By repeatedly finding a regularized optimal control solution and its generated mean field, we successfully obtain plausible approximate Nash equilibria in otherwise infeasible large dense graph games with many agents. Empirically, we are able to demonstrate on a number of examples that the finite-agent behavior comes increasingly close to the mean field behavior for our computed equilibria as the graph or system size grows, verifying our theory. More generally, we successfully apply policy gradient reinforcement learning in conjunction with sequential Monte Carlo methods. Lauriere. Finite state graphon games with applications to epidemics. arXiv preprint arXiv:2106.07859, 2021a.Alexander Aurell, Rene Carmona, and Mathieu Lauriere. Stochastic graphon games: Ii. the linearquadratic case. arXiv preprint arXiv:2105.12320, 2021b.Fabio Bagagiolo and Dario Bauso. Mean-field games and dynamic demand management in power grids. . A mean-field-type game approach to computation offloading in mobile edge computing networks. | LEARNING GRAPHON MEAN FIELD GAMES AND APPROXIMATE NASH EQUILIBRIA |
d243847413 | Federated learning is an established method for training machine learning models without sharing training data. However, recent work has shown that it cannot guarantee data privacy as shared gradients can still leak sensitive information. To formalize the problem of gradient leakage, we propose a theoretical framework that enables, for the first time, analysis of the Bayes optimal adversary phrased as an optimization problem. We demonstrate that existing leakage attacks can be seen as approximations of this optimal adversary with different assumptions on the probability distributions of the input data and gradients. Our experiments confirm the effectiveness of the Bayes optimal adversary when it has knowledge of the underlying distribution. Further, our experimental evaluation shows that several existing heuristic defenses are not effective against stronger attacks, especially early in the training process. Thus, our findings indicate that the construction of more effective defenses and their evaluation remains an open problem.Published as a conference paper at ICLR 2022 findings suggest that creation of effective defenses and their evaluation is a challenging problem, and that our insights and contributions can substantially advance future research in the area.Main contributions Our main contributions are:• Formulation of the gradient leakage problem in a Bayesian framework which enables phrasing Bayes optimal adversary as an optimization problem.• Interpretation of several prior attacks as approximations of the Bayes optimal adversary, each using different assumptions for the distributions of inputs and their gradients. | BAYESIAN FRAMEWORK FOR GRADIENT LEAKAGE |
d67770197 | Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning. However, web navigation tasks are difficult for current deep reinforcement learning (RL) models due to the large discrete action space and the varying number of actions between the states. In this work, we introduce DOM-Q-NET, a novel architecture for RL-based web navigation to address both of these problems. It parametrizes Q functions with separate networks for different action categories: clicking a DOM element and typing a string input. Our model utilizes a graph neural network to represent the tree-structured HTML of a standard web page. We demonstrate the capabilities of our model on the MiniWoB environment where we can match or outperform existing work without the use of expert demonstrations. Furthermore, we show 2x improvements in sample efficiency when training in the multi-task setting, allowing our model to transfer learned behaviours across tasks. | DOM-Q-NET: GROUNDED RL ON STRUCTURED LANGUAGE |
d56657849 | High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time.To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space. This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty. We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data. | SOM-VAE: INTERPRETABLE DISCRETE REPRESENTATION LEARNING ON TIME SERIES |
d49876500 | Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code. In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce an output which semantically mixes characteristics from the datapoints. In this paper, we propose a regularization procedure which encourages interpolated outputs to appear more realistic by fooling a critic network which has been trained to recover the mixing coefficient from interpolated data. We then develop a simple benchmark task where we can quantitatively measure the extent to which various autoencoders can interpolate and show that our regularizer dramatically improves interpolation in this setting. We also demonstrate empirically that our regularizer produces latent codes which are more effective on downstream tasks, suggesting a possible link between interpolation abilities and learning useful representations. | Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer |
d264825357 | In this work, we propose a concise neural operator architecture for operator learning.Drawing an analogy with a conventional fully connected neural network, we define the neural operator as follows: the output of the i-th neuron in a nonlinear operator layer is defined by O i (u) = σ j W i j u + B i j .Here, W i j denotes the bounded linear operator connecting j-th input neuron to i-th output neuron, and the bias B i j takes the form of a function rather than a scalar.Given its new universal approximation property, the efficient parameterization of the bounded linear operators between two neurons (Banach spaces) plays a critical role.As a result, we introduce MgNO, utilizing multigrid structures to parameterize these linear operators between neurons.This approach offers both mathematical rigor and practical expressivity.Additionally, MgNO obviates the need for conventional lifting and projecting operators typically required in previous neural operators.Moreover, it seamlessly accommodates diverse boundary conditions.Our empirical observations reveal that MgNO exhibits superior ease of training compared to other CNNbased models, while also displaying a reduced susceptibility to overfitting when contrasted with spectral-type neural operators.We demonstrate the efficiency and accuracy of our method with consistently state-of-the-art performance on different types of partial differential equations (PDEs). | MgNO: Efficient Parameterization of Linear Operators via Multigrid |
d235386376 | Training and using modern neural-network based latent-variable generative models (like Variational Autoencoders) often require simultaneously training a generative direction along with an inferential (encoding) direction, which approximates the posterior distribution over the latent variables. Thus, the question arises: how complex does the inferential model need to be, in order to be able to accurately model the posterior distribution of a given generative model?In this paper, we identify an important property of the generative map impacting the required size of the encoder. We show that if the generative map is "strongly invertible" (in a sense we suitably formalize), the inferential model need not be much more complex. Conversely, we prove that there exist non-invertible generative maps, for which the encoding direction needs to be exponentially larger (under standard assumptions in computational complexity). Importantly, we do not require the generative model to be layerwise invertible, which a lot of the related literature assumes and isn't satisfied by many architectures used in practice (e.g. convolution and pooling based networks). Thus, we provide theoretical support for the empirical wisdom that learning deep generative models is harder when data lies on a low-dimensional manifold. Valuation, pages 129-164. World Scientific, 2005. B. Dai and D. Wipf. Diagnosing and enhancing vae models. arXiv preprint arXiv:1903.05789, 2019 A. S. Dalalyan. Theoretical guarantees for approximate sampling from smooth and log-concave densities.A. S. Dalalyan. Further and stronger analogy between sampling and optimization: Langevin monte carlo and gradient descent. arXiv preprint arXiv:1704.04752, 2017. | The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders |
d52297370 | We consider a problem of learning a reward and policy from expert examples under unknown dynamics in high-dimensional scenarios. Our proposed method builds on the framework of generative adversarial networks and exploits reward shaping to learn near-optimal rewards and policies. Potential-based reward shaping functions are known to guide the learning agent whereas in this paper we bring forward their benefits in learning near-optimal rewards. Our method simultaneously learns a potential-based reward shaping function through variational information maximization along with the reward and policy under the adversarial learning formulation. We evaluate our method on various high-dimensional complex control tasks. We also evaluate our learned rewards in transfer learning problems where training and testing environments are made to be different from each other in terms of dynamics or structure. Our experimentation shows that our proposed method not only learns near-optimal rewards and policies matching expert behavior, but also performs significantly better than state-of-the-art inverse reinforcement learning algorithms. | ADVERSARIAL IMITATION VIA VARIATIONAL INVERSE REINFORCEMENT LEARNING |
d263909446 | Diffusion models suffer from slow sample generation at inference time.Therefore, developing a principled framework for fast deterministic/stochastic sampling for a broader class of diffusion models is a promising direction.We propose two complementary frameworks for accelerating sample generation in pretrained models: Conjugate Integrators and Splitting Integrators.Conjugate integrators generalize DDIM, mapping the reverse diffusion dynamics to a more amenable space for sampling.In contrast, splitting-based integrators, commonly used in molecular dynamics, reduce the numerical simulation error by cleverly alternating between numerical updates involving the data and auxiliary variables.After extensively studying these methods empirically and theoretically, we present a hybrid method that leads to the best-reported performance for diffusion models in augmented spaces.Applied to Phase Space Langevin Diffusion[Pandey & Mandt, 2023]on CIFAR-10, our deterministic and stochastic samplers achieve FID scores of 2.11 and 2.36 in only 100 network function evaluations (NFE) as compared to 2.57 and 2.63 for the best-performing baselines, respectively.Our code and model checkpoints will be made publicly available at https://github.com/mandt-lab/PSLD. | EFFICIENT INTEGRATORS FOR DIFFUSION GENERATIVE MODELS |
d244920632 | 3D point cloud is an important 3D representation for capturing real world 3D objects. However, real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications. Most existing point cloud completion methods use Chamfer Distance (CD) loss for training. The CD loss estimates correspondences between two point clouds by searching nearest neighbors, which does not capture the overall point density distribution on the generated shape, and therefore likely leads to non-uniform point cloud generation. To tackle this problem, we propose a novel Point Diffusion-Refinement (PDR) paradigm for point cloud completion. PDR consists of a Conditional Generation Network (CGNet) and a ReFinement Network (RFNet). The CGNet uses a conditional generative model called the denoising diffusion probabilistic model (DDPM) to generate a coarse completion conditioned on the partial observation. DDPM establishes a one-to-one pointwise mapping between the generated point cloud and the uniform ground truth, and then optimizes the mean squared error loss to realize uniform generation. The RFNet refines the coarse output of the CGNet and further improves quality of the completed point cloud. Furthermore, we develop a novel dual-path architecture for both networks. The architecture can (1) effectively and efficiently extract multi-level features from partially observed point clouds to guide completion, and (2) accurately manipulate spatial locations of 3D points to obtain smooth surfaces and sharp details. Extensive experimental results on various benchmark datasets show that our PDR paradigm outperforms previous state-of-the-art methods for point cloud completion. Remarkably, with the help of the RFNet, we can accelerate the iterative generation process of the DDPM by up to 50 times without much performance drop. * Equal Contribution.Code is released at https://github.com/ZhaoyangLyu/Point_ Diffusion_Refinement. . 3d shape generation and completion through point-voxel diffusion. arXiv preprint arXiv:2104.03670, 2021. | A CONDITIONAL POINT DIFFUSION-REFINEMENT PARADIGM FOR 3D POINT CLOUD COMPLETION |
d264825556 | Diffusion or flow-based models are powerful generative paradigms that are notoriously hard to sample as samples are defined as solutions to high-dimensional Ordinary or Stochastic Differential Equations (ODEs/SDEs) which require a large Number of Function Evaluations (NFE) to approximate well.Existing methods to alleviate the costly sampling process include model distillation and designing dedicated ODE solvers.However, distillation is costly to train and sometimes can deteriorate quality, while dedicated solvers still require relatively large NFE to produce high quality samples.In this paper we introduce "Bespoke solvers", a novel framework for constructing custom ODE solvers tailored to the ODE of a given pre-trained flow model.Our approach optimizes an order consistent and parameter-efficient solver (e.g., with 80 learnable parameters), is trained for roughly 1% of the GPU time required for training the pre-trained model, and significantly improves approximation and generation quality compared to dedicated solvers.For example, a Bespoke solver for a CIFAR10 model produces samples with Fréchet Inception Distance (FID) of 2.73 with 10 NFE, and gets to 1% of the Ground Truth (GT) FID (2.59) for this model with only 20 NFE.On the more challenging ImageNet-64×64, Bespoke samples at 2.2 FID with 10 NFE, and gets within 2% of GT FID (1.71) with 20 NFE. | BESPOKE SOLVERS FOR GENERATIVE FLOW MODELS |
d235358868 | Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label. The large scale of data sets used in deep learning forces most sample selection strategies to employ efficient heuristics. This paper introduces an integer optimization problem for selecting a core set that minimizes the discrete Wasserstein distance from the unlabeled pool. We demonstrate that this problem can be tractably solved with a Generalized Benders Decomposition algorithm. Our strategy uses high-quality latent features that can be obtained by unsupervised learning on the unlabeled pool. Numerical results on several data sets show that our optimization approach is competitive with baselines and particularly outperforms them in the low budget regime where less than one percent of the data set is labeled. | LOW-BUDGET ACTIVE LEARNING VIA WASSERSTEIN DISTANCE: AN INTEGER PROGRAMMING APPROACH |
d256827824 | Modern ML applications increasingly rely on complex deep learning models and large datasets.There has been an exponential growth in the amount of computation needed to train the largest models.Therefore, to scale computation and data, these models are inevitably trained in a distributed manner in clusters of nodes, and their updates are aggregated before being applied to the model.However, a distributed setup is prone to Byzantine failures of individual nodes, components, and software.With data augmentation added to these settings, there is a critical need for robust and efficient aggregation systems.We define the quality of workers as reconstruction ratios ∈ (0, 1], and formulate aggregation as a Maximum Likelihood Estimation procedure using Beta densities.We show that the Regularized form of log-likelihood wrt subspace can be approximately solved using iterative least squares solver, and provide convergence guarantees using recent Convex Optimization landscape results.Our empirical findings demonstrate that our approach significantly enhances the robustness of state-of-the-art Byzantine resilient aggregators.We evaluate our method in a distributed setup with a parameter server, and show simultaneous improvements in communication efficiency and accuracy across various tasks.The code is publicly available at https://github.com/hamidralmasi/FlagAggregatorPreprint.Under review. | Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization |
d259375820 | Recent works have empirically analyzed in-context learning and shown that transformers trained on synthetic linear regression tasks can learn to implement ridge regression, which is the Bayes-optimal predictor, given sufficient capacity [Akyürek et al., 2023], while one-layer transformers with linear selfattention and no MLP layer will learn to implement one step of gradient descent (GD) on a least-squares linear regression objective[von Oswald et al., 2022]. However, the theory behind these observations remains poorly understood. We theoretically study transformers with a single layer of linear self-attention, trained on synthetic noisy linear regression data. First, we mathematically show that when the covariates are drawn from a standard Gaussian distribution, the one-layer transformer which minimizes the pretraining loss will implement a single step of GD on the least-squares linear regression objective. Then, we find that changing the distribution of the covariates and weight vector to a non-isotropic Gaussian distribution has a strong impact on the learned algorithm: the global minimizer of the pre-training loss now implements a single step of pre-conditioned GD. However, if only the distribution of the responses is changed, then this does not have a large effect on the learned algorithm: even when the response comes from a more general family of nonlinear functions, the global minimizer of the pre-training loss still implements a single step of GD on a least-squares linear regression objective.1 In some settings in these works, the noise is set to 0. | One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention |
d246485738 | Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -we justify such a paradigm from a causal perspective. Following this principle, we propose a new time series representation learning framework for long sequence time series forecasting named CoST, which applies contrastive learning methods to learn disentangled seasonal-trend representations. CoST comprises both time domain and frequency domain contrastive losses to learn discriminative trend and seasonal representations, respectively. Extensive experiments on real-world datasets show that CoST consistently outperforms the state-of-the-art methods by a considerable margin, achieving a 21.3% improvement in MSE on multivariate benchmarks. It is also robust to various choices of backbone encoders, as well as downstream regressors. | COST: CONTRASTIVE LEARNING OF DISENTANGLED SEASONAL-TREND REPRESENTATIONS FOR TIME SERIES FORECASTING |
d49411844 | This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. | DARTS: Differentiable Architecture Search |
d263334556 | Implicit neural representations (INRs) have arisen as useful methods for representing signals on Euclidean domains.By parameterizing an image as a multilayer perceptron (MLP) on Euclidean space, INRs effectively represent signals in a way that couples spatial and spectral features of the signal that is not obvious in the usual discrete representation, paving the way for continuous signal processing and machine learning approaches that were not previously possible.Although INRs using sinusoidal activation functions have been studied in terms of Fourier theory, recent works have shown the advantage of using wavelets instead of sinusoids as activation functions, due to their ability to simultaneously localize in both frequency and space.In this work, we approach such INRs and demonstrate how they resolve high-frequency features of signals from coarse approximations done in the first layer of the MLP.This leads to multiple prescriptions for the design of INR architectures, including the use of complex wavelets, decoupling of low and band-pass approximations, and initialization schemes based on the singularities of the desired signal. | Implicit Neural Representations and the Algebra of Complex Wavelets |
d263909387 | Speculative decoding (SD) accelerates large language model inference by employing a faster draft model for generating multiple tokens, which are then verified in parallel by the larger target model, resulting in the text generated according to the target model distribution.However, identifying a compact draft model that is well-aligned with the target model is challenging.To tackle this issue, we propose DistillSpec that uses knowledge distillation to better align the draft model with the target model, before applying SD.Dis-tillSpec makes two key design choices, which we demonstrate via systematic study to be crucial to improve the draft and target alignment: utilizing on-policy data generation from the draft model, and tailoring the divergence function to the task and decoding strategy.Notably, DistillSpec yields impressive 10 − 45% speedups over standard SD on a range of standard benchmarks, using both greedy and non-greedy sampling.Furthermore, we combine DistillSpec with lossy SD to achieve fine-grained control over the latency vs. task performance trade-off.Finally, in practical scenarios with models of varying sizes, first using distillation to boost the performance of the target model and then applying DistillSpec to train a well-aligned draft model can reduce decoding latency by 6 − 10× with minimal performance drop, compared to standard decoding without distillation. | DISTILLSPEC: IMPROVING SPECULATIVE DECODING VIA KNOWLEDGE DISTILLATION |
d53034786 | In general, natural language is governed by a tree structure: smaller units (e.g., phrases) are nested within larger units (e.g., clauses). This is a strict hierarchy: when a larger constituent ends, all of the smaller constituents that are nested within it must also be closed. While the standard LSTM allows different neurons to track information at different time scales, the architecture does not impose a strict hierarchy. This paper proposes to add such a constraint to the system by ordering the neurons; a vector of "master" input and forget gates ensure that when a given unit is updated, all of the units that follow it in the ordering are also updated. To this end, we propose a new RNN unit: ON-LSTM, which achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference. 1 | ORDERED NEURONS: INTEGRATING TREE STRUCTURES INTO RECURRENT NEURAL NETWORKS |
d256827026 | Large-scale vision-language pre-trained models have shown promising transferability to various downstream tasks. As the size of these foundation models and the number of downstream tasks grow, the conventional full fine-tuning paradigm becomes impractical due to heavy computational and storage costs. This paper proposes UniAdapter, which unifies unimodal and multimodal adapters for parameter-efficient cross-modal adaptation on pre-trained vision-language models. Specifically, adapters are distributed to different modalities and their interactions, with the total number of tunable parameters reduced by partial weight sharing. The unified and knowledge-sharing design enables efficient adaptation to various downstream tasks with powerful cross-modal representations, requiring only 1.0%-2.0% tunable parameters of the pre-trained model. Extensive experiments on 6 crossmodal downstream benchmarks (including video-text retrieval, image-text retrieval, VideoQA, and VQA) show that in most cases, UniAdapter not only outperforms the state-of-the-arts, but even surpasses the full fine-tuning strategy. Notably, on the MSRVTT retrieval task, UniAdapter achieves 49.7% recall@1 with only 2.2% tunable model parameters, outperforming the latest competitors by 2.0%. The code and models are available at https | UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling |
d263334567 | Transformers have become the standard in state-of-the-art vision architectures, achieving impressive performance on both image-level and dense pixelwise tasks.However, training vision transformers for high-resolution pixelwise tasks has a prohibitive cost.Typical solutions boil down to hierarchical architectures, fast and approximate attention, or training on low-resolution crops.This latter solution does not constrain architectural choices, but it leads to a clear performance drop when testing at resolutions significantly higher than that used for training, thus requiring ad-hoc and slow post-processing schemes.In this paper, we propose a novel strategy for efficient training and inference of high-resolution vision transformers: the key principle is to mask out most of the high-resolution inputs during training, keeping only N random windows.This allows the model to learn local interactions between tokens inside each window, and global interactions between tokens from different windows.As a result, the model can directly process the high-resolution input at test time without any special trick.We show that this strategy is effective when using relative positional embedding such as rotary embeddings.It is 4 times faster to train than a full-resolution network, and it is straightforward to use at test time compared to existing approaches.We apply this strategy to two dense prediction tasks with high resolution data.First, we show on the task of semantic segmentation that a simple setting with 2 windows performs best, hence the name of our method: Win-Win.To demonstrate the generality of our contribution, we further extend it to the binocular task of optical flow, reaching state-of-the-art performance on the Spring benchmark that contains Full-HD images with an inference time an order of magnitude faster than the best competitor. | WIN-WIN: TRAINING HIGH-RESOLUTION VISION TRANSFORMERS FROM TWO WINDOWS |
d252715598 | This paper presents MOAT, a family of neural networks that build on top of MObile convolution (i.e., inverted residual blocks) and ATtention. Unlike the current works that stack separate mobile convolution and transformer blocks, we effectively merge them into a MOAT block. Starting with a standard Transformer block, we replace its multi-layer perceptron with a mobile convolution block, and further reorder it before the self-attention operation. The mobile convolution block not only enhances the network representation capacity, but also produces better downsampled features. Our conceptually simple MOAT networks are surprisingly effective, achieving 89.1% / 81.5% top-1 accuracy on ImageNet-1K / ImageNet-1K-V2 with ImageNet-22K pretraining. Additionally, MOAT can be seamlessly applied to downstream tasks that require large resolution inputs by simply converting the global attention to window attention. Thanks to the mobile convolution that effectively exchanges local information between pixels (and thus cross-windows), MOAT does not need the extra window-shifting mechanism. As a result, on COCO object detection, MOAT achieves 59.2% AP box with 227M model parameters (single-scale inference, and hard NMS), and on ADE20K semantic segmentation, MOAT attains 57.6% mIoU with 496M model parameters (single-scale inference). Finally, the tiny-MOAT family, obtained by simply reducing the channel sizes, also surprisingly outperforms several mobile-specific transformer-based models on ImageNet. The tiny-MOAT family is also benchmarked on downstream tasks, serving as a baseline for the community. We hope our simple yet effective MOAT will inspire more seamless integration of convolution and self-attention. Code is publicly available. | MOAT: ALTERNATING MOBILE CONVOLUTION AND ATTENTION BRINGS STRONG VISION MODELS |
d53113014 | Computer simulation provides an automatic and safe way for training robotic control policies to achieve complex tasks such as locomotion. However, a policy trained in simulation usually does not transfer directly to the real hardware due to the differences between the two environments. Transfer learning using domain randomization is a promising approach, but it usually assumes that the target environment is close to the distribution of the training environments, thus relying heavily on accurate system identification. In this paper, we present a different approach that leverages domain randomization for transferring control policies to unknown environments. The key idea that, instead of learning a single policy in the simulation, we simultaneously learn a family of policies that exhibit different behaviors. When tested in the target environment, we directly search for the best policy in the family based on the task performance, without the need to identify the dynamic parameters. We evaluate our method on five simulated robotic control problems with different discrepancies in the training and testing environment and demonstrate that our method can overcome larger modeling errors compared to training a robust policy or an adaptive policy. | POLICY TRANSFER WITH STRATEGY OPTIMIZATION |
d237372712 | We present miniF2F, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The miniF2F benchmark currently targets Metamath, Lean, Isabelle (partially) and HOL Light (partially) and consists of 488 problem statements drawn from the AIME, AMC, and the International Mathematical Olympiad (IMO), as well as material from high-school and undergraduate mathematics courses. We report baseline results using GPT-f (Polu & Sutskever, 2020), a neural theorem prover based on GPT-3 (Brown et al., 2020) and provide an analysis of its performance. We intend for miniF2F to be a community-driven effort and hope that our benchmark will help spur advances in neural theorem proving. | MINIF2F: A CROSS-SYSTEM BENCHMARK FOR FORMAL OLYMPIAD-LEVEL MATHEMATICS |
d246294898 | Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of diverse sequences can advance the parameter scale from million-level to billion-level and achieve remarkable improvement. However, those prevailing approaches rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better protein representations. We argue that informative biology knowledge in KGs can enhance protein representation with external knowledge. In this work, we propose OntoProtein, the first general framework that makes use of structure in GO (Gene Ontology) into protein pre-training models. We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph. We propose novel contrastive learning with knowledge-aware negative sampling to jointly optimize the knowledge graph and protein embedding during pre-training. Experimental results show that OntoProtein can surpass state-of-the-art methods with pre-trained protein language models in TAPE benchmark and yield better performance compared with baselines in protein-protein interaction and protein function prediction 1 . * Equal contribution and shared co-first authorship. † Corresponding author. 1 Code and datasets are available in https://github.com/zjunlp/OntoProtein. 2 http://geneontology.org/ arXiv:2201.11147v6 [q-bio.BM] | ONTOPROTEIN: PROTEIN PRETRAINING WITH GENE ONTOLOGY EMBEDDING |
d259187750 | This paper introduces the Fair Fairness Benchmark (FFB), a benchmarking framework for in-processing group fairness methods. Ensuring fairness in machine learning is critical for ethical and legal compliance. However, there exist challenges in comparing and developing of fairness methods due to inconsistencies in experimental settings, lack of accessible algorithmic implementations, and limited extensibility of current fairness packages and tools. To address these issues, we introduce an open-source, standardized benchmark for evaluating in-processing group fairness methods and provide a comprehensive analysis of state-of-the-art methods to ensure different notions of group fairness. This work offers the following key contributions: the provision of flexible, extensible, minimalistic, and research-oriented opensource code; the establishment of unified fairness method benchmarking pipelines; and extensive benchmarking, which yields key insights from 45, 079 experiments. We believe our work will significantly facilitate the growth and development of the fairness research community. The benchmark, including code and running logs, is available at https://github.com/ahxt/fair_fairness_benchmark. * This work was done while the first author was an intern at Meta.2We use utility to represent the performance of the downstream task.Preprint. Under review. | FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods |
d52920337 | This paper establishes risk convergence and asymptotic weight matrix alignment -a form of implicit regularization -of gradient flow and gradient descent when applied to deep linear networks on linearly separable data. In more detail, for gradient flow applied to strictly decreasing loss functions (with similar results for gradient descent with particular decreasing step sizes): (i) the risk converges to 0; (ii) the normalized i th weight matrix asymptotically equals its rank-1 approximation uiv i ; (iii) these rank-1 matrices are aligned across layers, meaning |v i+1 ui| → 1. In the case of the logistic loss (binary cross entropy), more can be said: the linear function induced by the network -the product of its weight matrices -converges to the same direction as the maximum margin solution. This last property was identified in prior work, but only under assumptions on gradient descent which here are implied by the alignment phenomenon. | Gradient descent aligns the layers of deep linear networks |
d247693295 | Models of human behavior for prediction and collaboration tend to fall into two categories: ones that learn from large amounts of data via imitation learning, and ones that assume human behavior to be noisily-optimal for some reward function. The former are very useful, but only when it is possible to gather a lot of human data in the target environment and distribution. The advantage of the latter type, which includes Boltzmann rationality, is the ability to make accurate predictions in new environments without extensive data when humans are actually close to optimal. However, these models fail when humans exhibit systematic suboptimality, i.e. when their deviations from optimal behavior are not independent, but instead consistent over time. Our key insight is that systematic suboptimality can be modeled by predicting policies, which couple action choices over time, instead of trajectories. We introduce the Boltzmann policy distribution (BPD), which serves as a prior over human policies and adapts via Bayesian inference to capture systematic deviations by observing human actions during a single episode. The BPD is difficult to compute and represent because policies lie in a high-dimensional continuous space, but we leverage tools from generative and sequence models to enable efficient sampling and inference. We show that the BPD enables prediction of human behavior and human-AI collaboration equally as well as imitation learning-based human models while using far less data. | THE BOLTZMANN POLICY DISTRIBUTION: ACCOUNTING FOR SYSTEMATIC SUBOPTIMALITY IN HUMAN MODELS |
d235212307 | The randomized singular value decomposition (SVD) is a popular and effective algorithm for computing a near-best rank k approximation of a matrix A using matrix-vector products with standard Gaussian vectors. Here, we generalize the randomized SVD to multivariate Gaussian vectors, allowing one to incorporate prior knowledge of A into the algorithm. This enables us to explore the continuous analogue of the randomized SVD for Hilbert-Schmidt (HS) operators using operator-function products with functions drawn from a Gaussian process (GP). We then construct a new covariance kernel for GPs, based on weighted Jacobi polynomials, which allows us to rapidly sample the GP and control the smoothness of the randomly generated functions. Numerical examples on matrices and HS operators demonstrate the applicability of the algorithm. arXiv:2105.13052v3 [math.NA] 21 Jan 2022Published as a conference paper at ICLR 2022 based on weighted Jacobi polynomials for learning HS operators. One of the main advantages of this kernel is that it is directly expressed as a Karhunen-Loève expansion(Karhunen, 1946;Loève, 1946)so that it is faster to sample functions from the associated GP than using a standard squaredexponential kernel. In addition, we show that the smoothness of the functions sampled from a GP with the Jacobi kernel can be controlled as it is related to the decay rate of the kernel's eigenvalues.Contributions. We summarize our novel contributions as follows:1. We provide new theoretical bounds for the randomized SVD for matrices or HS operators when using random input vectors generated from any multivariate Gaussian distribution. This shows when it is beneficial to use nonstandard Gaussian random vectors in the randomized SVD for constructing low-rank approximations. 2. We generalize the randomized SVD to HS operators and provide numerical examples to learn integral kernels. 3. We propose a covariance kernel based on weighted Jacobi polynomials and show that one can select the smoothness of the sampled random functions by choosing the decay rate of the kernel eigenvalues. | A GENERALIZATION OF THE RANDOMIZED SINGULAR VALUE DECOMPOSITION |
d238419003 | Cross-domain imitation learning studies how to leverage expert demonstrations of one agent to train an imitation agent with a different embodiment or morphology. Comparing trajectories and stationary distributions between the expert and imitation agents is challenging because they live on different systems that may not even have the same dimensionality. We propose Gromov-Wasserstein Imitation Learning (GWIL), a method for cross-domain imitation that uses the Gromov-Wasserstein distance to align and compare states between the different spaces of the agents. Our theory formally characterizes the scenarios where GWIL preserves optimality, revealing its possibilities and limitations. We demonstrate the effectiveness of GWIL in non-trivial continuous control domains ranging from simple rigid transformation of the expert domain to arbitrary transformation of the state-action space. 1 | CROSS-DOMAIN IMITATION LEARNING VIA OPTIMAL TRANSPORT |
d23387956 | The Fisher information metric is an important foundation of information geometry, wherein it allows us to approximate the local geometry of a probability distribution. Recurrent neural networks such as the Sequence-to-Sequence (Seq2Seq) networks that have lately been used to yield state-of-the-art performance on speech translation or image captioning have so far ignored the geometry of the latent embedding, that they iteratively learn. We propose the information geometric Seq2Seq network which abridges the gap between deep recurrent neural networks and information geometry. Specifically, the latent embedding offered by a recurrent network is encoded as a Fisher kernel of a parametric Gaussian Mixture Model, a formalism common in computer vision. We utilise such a network to predict the shortest routes between two nodes of a graph by learning the adjacency matrix using the information geometric Seq2Seq model; our results show that for such a problem the probabilistic representation of the latent embedding supersedes the non-probabilistic embedding by 10-15%. | GEOSEQ2SEQ: INFORMATION GEOMETRIC SEQUENCE-TO-SEQUENCE NETWORKS |
d52920181 | Rewards are sparse in the real world and most today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself -thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. Such bonus is summed up with the real task reward -making it possible for RL algorithms to learn from the combined reward. We propose a new curiosity method which uses episodic memory to form the novelty bonus. To determine the bonus, the current observation is compared with the observations in memory. Crucially, the comparison is done based on how many environment steps it takes to reach the current observation from those in memory -which incorporates rich information about environment dynamics. This allows us to overcome the known "couch-potato" issues of prior work -when the agent finds a way to instantly gratify itself by exploiting actions which lead to hardly predictable consequences. We test our approach in visually rich 3D environments in VizDoom, DMLab and MuJoCo. In navigational tasks from VizDoom and DMLab, our agent outperforms the state-of-the-art curiosity method ICM. In MuJoCo, an ant equipped with our curiosity module learns locomotion out of the first-person-view curiosity only. * Shared first authorship. | EPISODIC CURIOSITY THROUGH REACHABILITY |
d254877694 | Text classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as gender or ethnicity. However, there is a gap between human intuition about these perturbations and the formal similarity specifications capturing them. While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e.g., in cases of asymmetric counterfactuals). This work proposes novel methods for bridging this gap by discovering expressive and intuitive individual fairness specifications. We show how to leverage unsupervised style transfer and GPT-3's zero-shot capabilities to automatically generate expressive candidate pairs of semantically similar sentences that differ along sensitive attributes. We then validate the generated pairs via an extensive crowdsourcing study, which confirms that a lot of these pairs align with human intuition about fairness in the context of toxicity classification. Finally, we show how limited amounts of human feedback can be leveraged to learn a similarity specification that can be used to train downstream fairness-aware models. | HUMAN-GUIDED FAIR CLASSIFICATION FOR NATURAL LANGUAGE PROCESSING |
d220496457 | Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution. While methods that harness spatial and temporal structures find broad application, recent work has demonstrated the potential of models that leverage sparse and modular structure using an ensemble of sparingly interacting modules. In this work, we take a step towards dynamic models that are capable of simultaneously exploiting both modular and spatiotemporal structures. We accomplish this by abstracting the modeled dynamical system as a collection of autonomous but sparsely interacting sub-systems. The sub-systems interact according to a topology that is learned, but also informed by the spatial structure of the underlying real-world system. This results in a class of models that are well suited for modeling the dynamics of systems that only offer local views into their state, along with corresponding spatial locations of those views. On the tasks of video prediction from cropped frames and multi-agent world modeling from partial observations in the challenging Starcraft2 domain, we find our models to be more robust to the number of available views and better capable of generalization to novel tasks without additional training, even when compared against strong baselines that perform equally well or better on the training distribution. | Spatially Structured Recurrent Modules |
d264172710 | As large language models (LLMs) are adopted as a fundamental component of language technologies, it is crucial to accurately characterize their performance. Because choices in prompt design can strongly influence model behavior, this design process is critical in effectively using any modern pre-trained generative language model. In this work, we focus on LLM sensitivity to a quintessential class of meaning-preserving design choices: prompt formatting. We find that several widely used open-source LLMs are extremely sensitive to subtle changes in prompt formatting in few-shot settings, with performance differences of up to 76 accuracy points when evaluated using LLaMA-2-13B. Sensitivity remains even when increasing model size, the number of few-shot examples, or performing instruction tuning. Our analysis suggests that work evaluating LLMs with prompting-based methods would benefit from reporting a range of performance across plausible prompt formats, instead of the currently-standard practice of reporting performance on a single format. We also show that format performance only weakly correlates between models, which puts into question the methodological validity of comparing models with an arbitrarily chosen, fixed prompt format. To facilitate systematic analysis we propose FORMATSPREAD, an algorithm that rapidly evaluates a sampled set of plausible prompt formats for a given task, and reports the interval of expected performance without accessing model weights 1 . Furthermore, we present a suite of analyses that characterize the nature of this sensitivity, including exploring the influence of particular atomic perturbations and the internal representation of particular formats. | QUANTIFYING LANGUAGE MODELS' SENSITIVITY TO SPURIOUS FEATURES IN PROMPT DESIGN or: How I learned to start worrying about prompt formatting |
d253098972 | Prompt tuning approaches, which learn task-specific soft prompts for a downstream task conditioning on frozen pre-trained models, have attracted growing interest due to its parameter efficiency. With large language models and sufficient training data, prompt tuning performs comparably to full-model tuning. However, with limited training samples in few-shot settings, prompt tuning fails to match the performance of full-model fine-tuning. In this work, we focus on improving the few-shot performance of prompt tuning by transferring knowledge from soft prompts of source tasks. Recognizing the good generalization capabilities of ensemble methods in low-data regime, we first experiment and show that a simple ensemble of model predictions based on different source prompts, outperforms existing multi-prompt knowledge transfer approaches such as source prompt fusion in the few-shot setting. Motivated by this observation, we further investigate model ensembles and propose Sample-specific Ensemble of Source Models (SESoM). SESoM learns to adjust the contribution of each source model for each target sample separately when ensembling source model outputs. Through this way, SESoM inherits the superior generalization of model ensemble approaches and simultaneously captures the sample-specific competence of each source prompt. We conduct experiments across a diverse set of eight NLP tasks using models of different scales (T5-{base, large, XL}) and find that SESoM consistently outperforms the existing models of the same as well as larger parametric scale by a large margin. | MODEL ENSEMBLE INSTEAD OF PROMPT FUSION: A SAMPLE-SPECIFIC KNOWLEDGE TRANSFER METHOD FOR FEW-SHOT PROMPT TUNING |
d253237991 | We consider the problem of clustering in the learning-augmented setting, where we are given a data set in d-dimensional Euclidean space, and a label for each data point given by an oracle indicating what subsets of points should be clustered together. This setting captures situations where we have access to some auxiliary information about the data set relevant for our clustering objective, for instance the labels output by a neural network. Following prior work, we assume that there are at most an α ∈ (0, c) for some c < 1 fraction of false positives and false negatives in each predicted cluster, in the absence of which the labels would attain the optimal clustering cost OPT. For a dataset of size m, we propose a deterministic kmeans algorithm that produces centers with improved bound on clustering cost compared to the previous randomized algorithm while preserving the O(dm log m) runtime. Furthermore, our algorithm works even when the predictions are not very accurate, i.e. our bound holds for α up to 1/2, an improvement over α being at most 1/7 in the previous work. For the k-medians problem we improve upon prior work by achieving a biquadratic improvement in the dependence of the approximation factor on the accuracy parameter α to get a cost of (1 + O(α))OPT, while requiring essentially just O(md log 3 m/α) runtime. * Equal contribution. | Improved Learning-augmented Algorithms for k-means and k-medians Clustering |
d254535963 | Inferring reward functions from human behavior is at the center of value alignment -aligning AI objectives with what we, humans, actually want.But doing so relies on models of how humans behave given their objectives.After decades of research in cognitive science, neuroscience, and behavioral economics, obtaining accurate human models remains an open research topic.This begs the question: how accurate do these models need to be in order for the reward inference to be accurate?On the one hand, if small errors in the model can lead to catastrophic error in inference, the entire framework of reward learning seems ill-fated, as we will never have perfect models of human behavior.On the other hand, if as our models improve, we can have a guarantee that reward accuracy also improves, this would show the benefit of more work on the modeling side.We study this question both theoretically and empirically.We do show that it is unfortunately possible to construct small adversarial biases in behavior that lead to arbitrarily large errors in the inferred reward.However, and arguably more importantly, we are also able to identify reasonable assumptions under which the reward inference error can be bounded linearly in the error in the human model.Finally, we verify our theoretical insights in discrete and continuous control tasks with simulated and human data. | ON THE SENSITIVITY OF REWARD INFERENCE TO MISSPECIFIED HUMAN MODELS |
d263605735 | Dueling bandits is a prominent framework for decision-making involving preferential feedback, a valuable feature that fits various applications involving human interaction, such as ranking, information retrieval, and recommendation systems.While substantial efforts have been made to minimize the cumulative regret in dueling bandits, a notable gap in the current research is the absence of regret bounds that account for the inherent uncertainty in pairwise comparisons between the dueling arms.Intuitively, greater uncertainty suggests a higher level of difficulty in the problem.To bridge this gap, this paper studies the problem of contextual dueling bandits, where the binary comparison of dueling arms is generated from a generalized linear model (GLM).We propose a new SupLinUCB-type algorithm that enjoys computational efficiency and a variance-aware regret bound O d T t=1 σ 2 t + d , where σ t is the variance of the pairwise comparison in round t, d is the dimension of the context vectors, and T is the time horizon.Our regret bound naturally aligns with the intuitive expectation -in scenarios where the comparison is deterministic, the algorithm only suffers from an O(d) regret.We perform empirical experiments on synthetic data to confirm the advantage of our method over previous variance-agnostic algorithms. | Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits |
d35432793 | We study the properties of common loss surfaces through their Hessian matrix. In particular, in the context of deep learning, we empirically show that the spectrum of the Hessian is composed of two parts: (1) the bulk centered near zero, (2) and outliers away from the bulk. We present numerical evidence and mathematical justifications to the following conjectures laid out by : Fixing data, increasing the number of parameters merely scales the bulk of the spectrum; fixing the dimension and changing the data (for instance adding more clusters or making the data less separable) only affects the outliers. We believe that our observations have striking implications for non-convex optimization in high dimensions. First, the flatness of such landscapes (which can be measured by the singularity of the Hessian) implies that classical notions of basins of attraction may be quite misleading. And that the discussion of wide/narrow basins may be in need of a new perspective around over-parametrization and redundancy that are able to create large connected components at the bottom of the landscape. Second, the dependence of small number of large eigenvalues to the data distribution can be linked to the spectrum of the covariance matrix of gradients of model outputs. With this in mind, we may reevaluate the connections within the data-architecturealgorithm framework of a model, hoping that it would shed light into the geometry of high-dimensional and non-convex spaces in modern applications. In particular, we present a case that links the two observations: a gradient based method appears to be first climbing uphill and then falling downhill between two points; whereas, in fact, they lie in the same basin. | Empirical Analysis of the Hessian of Over-Parametrized Neural Networks |
d258865444 | In this work we present an approach for generating alternative text (or alt-text) descriptions for images shared on social media, specifically Twitter.More than just a special case of image captioning, alt-text is both more literally descriptive and context-specific.Also critically, images posted to Twitter are often accompanied by user-written text that despite not necessarily describing the image may provide useful context that if properly leveraged can be informative.We address this task with a multimodal model that conditions on both textual information from the associated social media post as well as visual signal from the image, and demonstrate that the utility of these two information sources stacks.We put forward a new dataset of 371k images paired with alt-text and tweets scraped from Twitter and evaluate on it across a variety of automated metrics as well as human evaluation.We show that our approach of conditioning on both tweet text and visual information significantly outperforms prior work, by more than 2x on BLEU@4. | ALT-TEXT WITH CONTEXT: IMPROVING ACCESSIBILITY FOR IMAGES ON TWITTER |
d257687492 | Quality Diversity (QD) has emerged as a powerful alternative optimization paradigm that aims at generating large and diverse collections of solutions, notably with its flagship algorithm MAP-ELITES (ME) which evolves solutions through mutations and crossovers. While very effective for some unstructured problems, early ME implementations relied exclusively on random search to evolve the population of solutions, rendering them notoriously sample-inefficient for highdimensional problems, such as when evolving neural networks. Follow-up works considered exploiting gradient information to guide the search in order to address these shortcomings through techniques borrowed from either Black-Box Optimization (BBO) or Reinforcement Learning (RL). While mixing RL techniques with ME unlocked state-of-the-art performance for robotics control problems that require a good amount of exploration, it also plagued these ME variants with limitations common among RL algorithms that ME was free of, such as hyperparameter sensitivity, high stochasticity as well as training instability, including when the population size increases as some components are shared across the population in recent approaches. Furthermore, existing approaches mixing ME with RL tend to be tied to a specific RL algorithm, which effectively prevents their use on problems where the corresponding RL algorithm fails. To address these shortcomings, we introduce a flexible framework that allows the use of any RL algorithm and alleviates the aforementioned limitations by evolving populations of agents (whose definition include hyperparameters and all learnable parameters) instead of just policies. We demonstrate the benefits brought about by our framework through extensive numerical experiments on a number of robotics control problems, some of which with deceptive rewards, taken from the QD-RL literature. We open source an efficient JAX-based implementation of our algorithm in the QDax library 1 . | EVOLVING POPULATIONS OF DIVERSE RL AGENTS WITH MAP-ELITES |
d3497822 | Recurrent neural networks (RNNs) are widely used to model sequential data but their non-linear dependencies between sequence elements prevent parallelizing training over sequence length. We show the training of RNNs with only linear sequential dependencies can be parallelized over the sequence length using the parallel scan algorithm, leading to rapid training on long sequences with small minibatch size. We abstract prior linear sequence models into a new framework of linear surrogate RNNs and develop a linear surrogate long short-term memory (LS-LSTM) powered by a parallel linear recurrence CUDA kernel we implemented. We evaluate the LS-LSTM on a long sequence noisy autoregressive task and find the LS-LSTM achieves slightly superior train and test performance to a similar sized LSTM in 4x less training time. We analyze latency and throughput of the LS-LSTM and find the LS-LSTM reaches up to 175x the throughput of the LSTM in the small minibatch long sequence regime. | Parallelizing Linear Recurrent Neural Nets Over Sequence Length |
d261100891 | Machine learning models are often used to decide who will receive a loan, a job interview, or a public benefit. Standard techniques to build these models use features about people but overlook their actionability. In turn, models can assign predictions that are fixed -meaning that consumers who are denied loans, interviews, or benefits may be permanently locked out from access to credit, employment, or assistance. In this work, we introduce a formal testing procedure to flag models that assign fixed predictions that we call recourse verification. We develop machinery to reliably determine if a given model can provide recourse to its decision subjects from a set of user-specified actionability constraints. We demonstrate how our tools can ensure recourse and adversarial robustness in real-world datasets and use them to study the infeasibility of recourse in real-world lending datasets. Our results highlight how models can inadvertently assign fixed predictions that permanently bar access, and we provide tools to design algorithms that account for actionability when developing models. arXiv:2308.12820v1 [cs.LG] 24 Aug 2023 2. We develop fast algorithms to delineate reachable sets from complex actionability constraints. Our algorithms can be used to ensure that a model can provide recourse in model development or deployment, and are designed to abstain when they are unable to certify recourse in order to avoid incorrect outputs.3. We present an empirical study of the infeasibility of recourse using several real-world datasets, realistic actionability constraints, and common model classes. Our results illustrate the prevalence of predictions without recourse in lending applications, and highlight pitfalls in flagging these examples with recourse provision. Finally, we demonstrate how our methods can be used to ensure recourse in consumer-facing applications like lending and content moderation.Related Work This work opens a new direction for research on algorithmic recourse, which studies how to change the prediction of a given model through actions in a feature space[73,75]. Much work on recourse develops methods for recourse provision -i.e., methods to provide a person with an action to change the prediction of a given model or, relatedly, counterfactual explanations -i.e., methods that explain a model's decision by showing what actions would change it [see e.g., 35, 18, 59, 42, 76, 77, 66, 38]. We focus instead on verification of models in terms of recourse feasibility -i.e., testing if a model assigns predictions that a given person can change using any feasible action. The need for verification arises because algorithmic recourse may be infeasible under realistic actionability constraints. Although actionability is a defining characteristic of recourse [see e.g., 75], the fact that such constraints may lead to infeasibility is not well-known in the literature. The exceptions [73, 43, 16] mention infeasibility but do not study it in detail. In contrast to the lack of attention in the literature, we show that recourse infeasibility is pervasive and is completely missed by most of the existing methods for recourse provision. | Prediction without Preclusion: Recourse Verification with Reachable Sets |
d27494814 | Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports(Han et al., 2015a;Narang et al., 2017)prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for model compression might be to simply reduce the number of hidden units while maintaining the model's dense connection structure, exposing a similar trade-off in model size and accuracy. We investigate these two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning and can be seamlessly incorporated within the training process. We compare the accuracy of large, but pruned models (large-sparse) and their smaller, but dense (small-dense) counterparts with identical memory footprint. Across a broad range of neural network architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find large-sparse models to consistently outperform small-dense models and achieve up to 10x reduction in number of non-zero parameters with minimal loss in accuracy. | To prune, or not to prune: exploring the efficacy of pruning for model compression |
d3340951 | The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight pruning or quantization. In this paper, we present a novel scheme for lossy weight encoding which complements conventional compression techniques. The encoding is based on the Bloomier filter, a probabilistic data structure that can save space at the cost of introducing random errors. Leveraging the ability of neural networks to tolerate these imperfections and by re-training around the errors, the proposed technique, Weightless, can compress DNN weights by up to 496× with the same model accuracy. This results in up to a 1.51× improvement over the state-of-the-art. | WEIGHTLESS: LOSSY WEIGHT ENCODING FOR DEEP NEURAL NETWORK COMPRESSION |
d249625810 | We consider the standard K-armed bandit problem under a distributed trust model of differential privacy (DP), which enables to guarantee privacy without a trustworthy server. Under this trust model, previous work largely focus on achieving privacy using a shuffle protocol, where a batch of users data are randomly permuted before sending to a central server. This protocol achieves (ε, δ) or approximate-DP guarantee by sacrificing an additional additive O K log T √ log(1/δ) ε cost in T -step cumulative regret. In contrast, the optimal privacy cost for achieving a stronger (ε, 0) or pure-DP guarantee under the widely used central trust model is only Θ K log T ε , where, however, a trusted server is required. In this work, we aim to obtain a pure-DP guarantee under distributed trust model while sacrificing no more regret than that under central trust model. We achieve this by designing a generic bandit algorithm based on successive arm elimination, where privacy is guaranteed by corrupting rewards with an equivalent discrete Laplace noise ensured by a secure computation protocol. We also show that our algorithm, when instantiated with Skellam noise and the secure protocol, ensures Rényi differential privacy -a stronger notion than approximate DP -under distributed trust model with a privacy cost of O K √ log T ε . * Equal contributions | Distributed Differential Privacy in Multi-Armed Bandits |
d9725544 | Given the recent successes of deep learning applied to style transfer and texture synthesis, we propose a new theoretical framework to construct visual metamers: a family of perceptually identical, yet physically different images. We review work both in neuroscience related to metameric stimuli, as well as computer vision research in style transfer. We propose our NeuroFovea metamer model that is based on a mixture of peripheral representations and style transfer forward-pass algorithms for any image from the recent work of Adaptive Instance Normalization (Huang & Belongie). Our model is parametrized by a VGG-Net versus a set of joint statistics of complex wavelet coefficients which allows us to encode images in high dimensional space and interpolate between the content and texture information. We empirically show that human observers discriminate our metamers at a similar rate as the metamers of Freeman & Simoncelli (FS) In addition, our NeuroFovea metamer model gives us the benefit of near real-time generation which presents a ×1000 speed-up compared to previous work. Critically, psychophysical studies show that both the FS and NeuroFovea metamers are discriminable from the original images highlighting an important limitation of current metamer generation methods. | Towards Metamerism via Foveated Style Transfer |
d235417313 | Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a blessing). However, little research has considered both directions simultaneously. In this paper, we first investigate the relationship between them by analyzing Federated Averaging (McMahan et al., 2017) at the client level and determine that a better federated global model performance does not constantly improve personalization. To elucidate the cause of this personalization performance degradation problem, we decompose the entire network into the body (extractor), which is related to universality, and the head (classifier), which is related to personalization. We then point out that this problem stems from training the head. Based on this observation, we propose a novel federated learning algorithm, coined FedBABU, which only updates the body of the model during federated training (i.e., the head is randomly initialized and never updated), and the head is fine-tuned for personalization during the evaluation process. Extensive experiments show consistent performance improvements and an efficient personalization of FedBABU.Same classifierBroadcastingLocal Update AggregationClient 1 Client 2 Client 3Central Server (b) FedBABU. We control FL environments with three hyperparameters: client fraction ratio f , local epochs τ , and shards per user s. f is the number of participating clients out of the total number of clients in every round and a small f is natural in the FL settings because the total number of clients is numerous. The local epochs τ are equal to the interval between two consecutive communication rounds. To fix the number of total updates to ensure the consistency in all experiments, we fix the product of communication rounds and local epochs to 320 (e.g., if local epochs are four, then the total number of communication rounds is 80). The learning rate starts with 0.1 and is decayed by a factor of 0.1 at half and three-quarters of total updates. τ is closely related to the trade-off between accuracy and communication costs. A small τ provides an accurate federation but requires considerable communication costs. s is related to the maximum number of classes each user can have; hence, as s decreases, the degree of data heterogeneity increases.Evaluation We calculate the initial accuracy and personalized accuracy of FedAvg and FedBABU following the federated personalization evaluation procedure proposed in Wang et al. (2019) to analyze the algorithms at the client level: (1) the learned global model is broadcast to all clients and is then evaluated on the test data set of each client D ts i (referred to as the initial accuracy), (2) the learned global model is personalized using the training data set of each client D tr i by fine-tuning with the fine-tuning epochs of τ f ; the personalized models are then evaluated on the test data set of each client D ts i (referred to as the personalized accuracy). In addition, we calculate the personalized accuracy of other personalized FL algorithms (such as FedPer, LG-FedAvg, and FedRep). Algorithm 2 in Appendix A describes the evaluation procedure. The values (X±Y) in all tables indicate the mean±standard deviation of the accuracies across all clients, not across multiple seeds. Here, reducing the variance over the clients could be interesting but goes beyond the scope of this study.Published as a conference paper at ICLR 2022 : Pre-training of deep bidirectional transformers for language understanding. In Astraea: Self-balancing federated learning for improving classification accuracy of mobile deep learning applications. federated learning: A metalearning approach. arXiv preprint arXiv:2002.07948, 2020.Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. . Improving federated learning personalization via model agnostic meta learning. arXiv preprint arXiv: | FEDBABU: TOWARD ENHANCED REPRESENTATION FOR FEDERATED IMAGE CLASSIFICATION |
d220249871 | How to explicitly encode positional information into neural networks is important in learning the representation of natural languages, such as BERT. Based on the Transformer architecture, the positional information is simply encoded as embedding vectors, which are used in the input layer, or encoded as a bias term in the self-attention module. In this work, we investigate the problems in the previous formulations and propose a new positional encoding method for BERT called Transformer with Untied Positional Encoding (TUPE). Different from all other works, TUPE only uses the word embedding as input. In the self-attention module, the word contextual correlation and positional correlation are computed separately with different parameterizations and then added together. This design removes the addition over heterogeneous embeddings in the input, which may potentially bring randomness, and gives more expressiveness to characterize the relationship between words/positions by using different projection matrices. Furthermore, TUPE unties the [CLS] symbol from other positions to provide it with a more specific role to capture the global representation of the sentence. Extensive experiments and ablation studies on GLUE benchmark demonstrate the effectiveness and efficiency of the proposed method: TUPE outperforms several baselines on almost all tasks by a large margin. In particular, it can achieve a higher score than baselines while only using 30% pre-training computational costs. We release our code at https://github.com/guolinke/TUPE. | RETHINKING POSITIONAL ENCODING IN LANGUAGE PRE-TRAINING |
d60440615 | Multilingual training of neural machine translation (NMT) systems has led to impressive accuracy improvements on low-resource languages. However, there are still significant challenges in efficiently learning word representations in the face of paucity of data. In this paper, we propose Soft Decoupled Encoding (SDE), a multilingual lexicon encoding framework specifically designed to share lexicallevel information intelligently without requiring heuristic preprocessing such as pre-segmenting the data. SDE represents a word by its spelling through a character encoding, and its semantic meaning through a latent embedding space shared by all languages. Experiments on a standard dataset of four low-resource languages show consistent improvements over strong multilingual NMT baselines, with gains of up to 2 BLEU on one of the tested languages, achieving the new state-of-the-art on all four language pairs 1 . | MULTILINGUAL NEURAL MACHINE TRANSLATION WITH SOFT DECOUPLED ENCODING |
d252762329 | Recently, researchers observed that gradient descent for deep neural networks operates in an "edge-ofstability" (EoS) regime: the sharpness (maximum eigenvalue of the Hessian) is often larger than stability threshold 2/η (where η is the step size). Despite this, the loss oscillates and converges in the long run, and the sharpness at the end is just slightly below 2/η. While many other well-understood nonconvex objectives such as matrix factorization or two-layer networks can also converge despite large sharpness, there is often a larger gap between sharpness of the endpoint and 2/η. In this paper, we study EoS phenomenon by constructing a simple function that has the same behavior. We give rigorous analysis for its training dynamics in a large local region and explain why the final converging point has sharpness close to 2/η. Globally we observe that the training dynamics for our example has an interesting bifurcating behavior, which was also observed in the training of neural nets. * Equal Contribution. | Understanding Edge-of-Stability Training Dynamics with a Minimalist Example |
d253098739 | Meta-learning aims to extract useful inductive biases from a set of related datasets. In Bayesian meta-learning, this is typically achieved by constructing a prior distribution over neural network parameters. However, specifying families of computationally viable prior distributions over the high-dimensional neural network parameters is difficult. As a result, existing approaches resort to meta-learning restrictive diagonal Gaussian priors, severely limiting their expressiveness and performance. To circumvent these issues, we approach meta-learning through the lens of functional Bayesian neural network inference, which views the prior as a stochastic process and performs inference in the function space. Specifically, we view the meta-training tasks as samples from the data-generating process and formalize meta-learning as empirically estimating the law of this stochastic process. Our approach can seamlessly acquire and represent complex prior knowledge by meta-learning the score function of the data-generating process marginals instead of parameter space priors. In a comprehensive benchmark, we demonstrate that our method achieves state-of-the-art performance in terms of predictive accuracy and substantial improvements in the quality of uncertainty estimates. * Equal contribution. | MARS: META-LEARNING AS SCORE MATCHING IN THE FUNCTION SPACE |
d251710555 | Standard inference and training with transformer based architectures scale quadratically with input sequence length. This is prohibitively large for a variety of applications especially in web-page translation, query-answering etc. Consequently, several approaches have been developed recently to speedup attention computation by enforcing different attention structures such as sparsity (Zaheer et al., 2020), low-rank (Wang et al., 2020), approximating attention using kernels(Choromanski et al., 2021). In this work, we view attention computation as that of nearest neighbor retrieval, and use decision tree based hierarchical navigation to reduce the retrieval cost per query token from linear in sequence length to nearly logarithmic. Based on such hierarchical navigation, we design Treeformer which can use one of two efficient attention layers -TF-ATTENTION and TC-ATTENTION. TF-ATTENTION computes the attention in a fine-grained style, while TC-ATTENTION is a coarse attention layer which also ensures that the gradients are "dense". To optimize such challenging discrete layers, we propose a two-level bootstrapped training method. Using extensive experiments on standard NLP benchmarks, especially for long-sequences, we demonstrate that our TREEFORMER architecture can be almost as accurate as baseline Transformer while using 30x lesser FLOPs in the attention layer. Compared to Linformer, the accuracy can be as much as 12% higher while using similar FLOPs in the attention layer. | TREEFORMER: DENSE GRADIENT TREES FOR EFFICIENT ATTENTION COMPUTATION |
d204509033 | Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables. However, they can be difficult to use in practice due to poorly understood bias/variance tradeoffs. We theoretically show that, under some conditions, estimators such as MINE exhibit variance that could grow exponentially with the true amount of underlying MI. We also empirically demonstrate that existing estimators fail to satisfy basic self-consistency properties of MI, such as data processing and additivity under independence. Based on a unified perspective of variational approaches, we develop a new estimator that focuses on variance reduction. Empirical results on standard benchmark tasks demonstrate that our proposed estimator exhibits improved biasvariance trade-offs on standard benchmark tasks. | UNDERSTANDING THE LIMITATIONS OF VARIATIONAL MUTUAL INFORMATION ESTIMATORS |
d251197051 | Recent Language Models (LMs) achieve breakthrough performance in code generation when trained on human-authored problems, even solving some competitive-programming problems. Self-play has proven useful in games such as Go, and thus it is natural to ask whether LMs can generate their own instructive programming problems to improve their performance. We show that it is possible for an LM to synthesize programming problems and solutions, which are filtered for correctness by a Python interpreter. The LM's performance is then seen to improve when it is fine-tuned on its own synthetic problems and verified solutions; thus the model "improves itself" using the Python interpreter. Problems are specified formally as programming puzzles[Schuster et al., 2021], a code-based problem format where solutions can easily be verified for correctness by execution. In experiments on publicly-available LMs, test accuracy more than doubles. This work demonstrates the potential for code LMs, with an interpreter, to generate instructive problems and improve their own performance.Published as a conference paper at ICLR 2023 but any set of human-authored problems (and variants) is inherently limited by the accuracy and effort of human creators. AI systems have the potential to go beyond templates and superficial changes to generate vast quantities of novel challenges and innovative solutions. Moreover, self-play might be necessary to one day surpass human code quality, just as AlphaZero surpassed human Go play.The first challenge in self-play for code LMs, unlike Go where the win-condition is clearly evaluable, is that the goal in code generation is not obvious. How should problems be specified? Programming problems are often described in English and/or examples and evaluated with hidden test cases in programming competitions and code-generation benchmarks such as CodeContests [Li et al., 2022], HumanEval [Chen et al., 2021], and APPS [Hendrycks et al., 2021]. While LMs have in fact been shown to be capable of generating largely-correct English programming problems[Sarsa et al., 2022], human oversight is still required for vetting the descriptions and test cases. | Language Models Can Teach Themselves to Program Better |
d263310678 | The massive interest in deep neural networks (DNNs) for both computer vision and natural language processing has been sparked by the growth in computational power.However, this led to an increase in the memory footprint, to a point where it can be challenging to simply load a model on commodity devices such as mobile phones.To address this limitation, quantization is a favored solution as it maps high precision tensors to a low precision, memory efficient format.In terms of memory footprint reduction, its most effective variants are based on codebooks.These methods, however, suffer from two limitations.First, they either define a single codebook for each tensor, or use a memory-expensive mapping to multiple codebooks.Second, gradient descent optimization of the mapping favors jumps toward extreme values, hence not defining a proximal search.In this work, we propose to address these two limitations.First, we initially group similarly distributed neurons and leverage the re-ordered structure to either apply different scale factors to the different groups, or map weights that fall in these groups to several codebooks, without any mapping overhead.Second, stemming from this initialization, we propose a joint learning of the codebook and weight mappings that bears similarities with recent gradient-based post-training quantization techniques.Third, drawing estimation from straight-through estimation techniques, we introduce a novel gradient update definition to enable a proximal search of the codebooks and their mappings.The proposed jointly learnable codebooks and mappings (JLCM) method allows a very efficient approximation of any DNN: as such, a Llama 7B can be compressed down to 2Go and loaded on 5-year-old smartphones. | NETWORK MEMORY FOOTPRINT COMPRESSION THROUGH JOINTLY LEARNABLE CODEBOOKS AND MAPPINGS |
d248266388 | The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a notoriously difficult problem domain for the application of neural networks. We identify a mechanism by which non-stationary prediction targets can prevent learning progress in deep RL agents: capacity loss, whereby networks trained on a sequence of target values lose their ability to quickly update their predictions over time. We demonstrate that capacity loss occurs in a range of RL agents and environments, and is particularly damaging to performance in sparsereward tasks. We then present a simple regularizer, Initial Feature Regularization (InFeR), that mitigates this phenomenon by regressing a subspace of features towards its value at initialization, leading to significant performance improvements in sparse-reward environments such as Montezuma's Revenge. We conclude that preventing capacity loss is crucial to enable agents to maximally benefit from the learning signals they obtain throughout the entire training trajectory. * Correspondence to clare. | UNDERSTANDING AND PREVENTING CAPACITY LOSS IN REINFORCEMENT LEARNING |
d221447287 | This paper introduces WaveGrad, a conditional model for waveform generation through estimating gradients of the data density. This model is built on the prior work on score matching and diffusion probabilistic models. It starts from Gaussian white noise and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad is non-autoregressive, and requires only a constant number of generation steps during inference. It can use as few as 6 iterations to generate high fidelity audio samples. WaveGrad is simple to train, and implicitly optimizes for the weighted variational lower-bound of the log-likelihood. Empirical experiments reveal WaveGrad to generate high fidelity audio samples matching a strong likelihood-based autoregressive baseline with less sequential operations. * Work done during an internship at Google Brain. † Equal contribution. | WAVEGRAD: ESTIMATING GRADIENTS FOR WAVEFORM GENERATION |
d247026123 | Many Neural Network Pruning approaches consist of several iterative training and pruning steps, seemingly losing a significant amount of their performance after pruning and then recovering it in the subsequent retraining phase. Recent works of Renda et al. (2020) and Le & Hua (2021) demonstrate the significance of the learning rate schedule during the retraining phase and propose specific heuristics for choosing such a schedule for IMP (Han et al., 2015). We place these findings in the context of the results of Li et al. (2020) regarding the training of models within a fixed training budget and demonstrate that, consequently, the retraining phase can be massively shortened using a simple linear learning rate schedule. Improving on existing retraining approaches, we additionally propose a method to adaptively select the initial value of the linear schedule. Going a step further, we propose similarly imposing a budget on the initial dense training phase and show that the resulting simple and efficient method is capable of outperforming significantly more complex or heavily parameterized state-of-the-art approaches that attempt to sparsify the network during training. These findings not only advance our understanding of the retraining phase, but more broadly question the belief that one should aim to avoid the need for retraining and reduce the negative effects of 'hard' pruning by incorporating the sparsification process into the standard training.Published as a conference paper at ICLR 2023 model but incorporate the sparsification into the training. We refer to such dense-to-sparse methods as pruning-stable(Bartoldson et al., 2020).Motivated by recent results of Li et al. (2020) regarding the training of Neural Networks under constraints on the number of training iterations, we challenge these commonly held beliefs by rethinking the retraining phase of IMP within the context of Budgeted Training and demonstrate that it can be massively shortened by using a simple linearly decaying learning rate schedule. We further demonstrate the importance of the learning rate scheme during the retraining phase and improve upon the results of Renda et al. (2020) and Le & Hua (2021) by proposing a simple and efficient approach to also choose the initial value of the learning rate, a problem which has not been previously addressed in the context of pruning. We also propose likewise imposing a budget on the initial dense training phase of IMP, turning it into a method capable of efficiently producing sparse, trained networks without the need for a pretrained model by effectively leveraging a cyclic linear learning rate schedule. The resulting method is able to outperform significantly more complex and heavily parameterized state-of-the-art approaches, that aim to reach pruning-stability at the end of training by incorporating the sparsification into the training process, while using less computational resources.Contributions. The major contributions are as follows:1. We empirically find that the results of Li et al.(2020)regarding the Budgeted Training of Neural Networks apply to the retraining phase of IMP, providing further context for the results of Renda et al. (2020) and Le & Hua(2021). Building on this, we find that the runtime of IMP can be drastically shortened by using a simple linear learning rate schedule with little to no degradation in model performance.2. We propose a novel way to choose the initial value of this linear schedule without the need to tune additional hyperparameters in the form of ADAPTIVE LINEAR LEARNING RATE RESTARTING (ALLR). Our approach takes the impact of pruning as well as the overall retraining time into account, improving upon previously proposed retraining schedules on a variety of learning tasks. | HOW I LEARNED TO STOP WORRYING AND LOVE RETRAINING |
d48361056 | We examine two different techniques for parameter averaging in GAN training. Moving Average (MA) computes the time-average of parameters, whereas Exponential Moving Average (EMA) computes an exponentially discounted sum. Whilst MA is known to lead to convergence in bilinear settings, we provide theto our knowledge -first theoretical arguments in support of EMA. We show that EMA converges to limit cycles around the equilibrium with vanishing amplitude as the discount parameter approaches one for simple bilinear games and also enhances the stability of general GAN training. We establish experimentally that both techniques are strikingly effective in the non-convex-concave GAN setting as well. Both improve inception and FID scores on different architectures and for different GAN objectives. We provide comprehensive experimental results across a range of datasets -mixture of Gaussians, CIFAR-10, STL-10, CelebA and ImageNet -to demonstrate its effectiveness. We achieve state-of-the-art results on CIFAR-10 and produce clean CelebA face images. 1In this work, we explore in detail simple strategies for tackling the cycling behavior without influencing the adversarial game. Our strategies average generator parameters over time outside the training loop. Averaging generator and discriminator parameters is known to be an optimal solution for convex-concave min-max games(Freund & Schapire, 1999). However, no such guarantees are known (even for bilinear games) if we apply exponential discounting.Our contributions are the following: (i) We show theoretically that although EMA does not converge to equilibrium, even in bilinear games, it nevertheless helps to stabilize cyclic behavior by shrinking its amplitude. In non-bilinear settings it preserves the stability of locally stable fixed points. (ii) We demonstrate that both averaging techniques consistently improve results for several different datasets, network architectures, and GAN objectives. (i) We compare it with several other methods that try to alleviate the cycling or non-convergence problem and demonstrate its unusual effectiveness. | THE UNUSUAL EFFECTIVENESS OF AVERAGING IN GAN TRAINING |
d236635303 | This paper considers two-player zero-sum finite-horizon Markov games with simultaneous moves. The study focuses on the challenging settings where the value function or the model is parameterized by general function classes. Provably efficient algorithms for both decoupled and coordinated settings are developed. In the decoupled setting where the agent controls a single player and plays against an arbitrary opponent, we propose a new model-free algorithm. The sample complexity is governed by the Minimax Eluder dimension-a new dimension of the function class in Markov games. As a special case, this method improves the state-of-the-art algorithm by a √ d factor in the regret when the reward function and transition kernel are parameterized with d-dimensional linear features. In the coordinated setting where both players are controlled by the agent, we propose a model-based algorithm and a modelfree algorithm. In the model-based algorithm, we prove that sample complexity can be bounded by a generalization of Witness rank to Markov games. The model-free algorithm enjoys a √ K-regret upper bound where K is the number of episodes. * baihehuang@pku.edu.cn. Peking University. † Jasondl@princeton.edu. Princeton University. ‡ zhaoranwang@gmail.com. Northwestern University. § zy6@princeton.edu. Princeton University.There is a rich literature studying the learning and decision-making of Markov Games [LS96, GHS03, GMLBA18, PPP18, SLZ + 18, SWYY20, WHL17, PSPP17, BJ20, BJWX21, BJY20, ZKBY20, ZTLD21]. The most related to us are perhaps [XCWY20, CZG21, JLY21], where the authors address the challenge of exploration-exploitation tradeoff in large state spaces. Due to space constraint, a detailed literature discussion is deferred to Appendix A.Technical challengesPrevious work [XCWY20] imposes optimistic bonus on the action-value functions in every state-action pairs and performs planning by the Coarse Correlated Equilibrium (CCE) on the optimistic value functions. To achieve improved rates, we leverage the idea of 'global optimism' [ZLKB20, JLM21, DKL + 21], which maintains a constraint set of candidate functions that do not deviate much from the empirical estimates and performs optimistic planning on the initial state. However, going beyond MDPs towards MGs, two problems arise. First, the concentration property of functions in constraint set is hard to characterize due to multi-agent interplay. For this, we use the concentration methods in [JLM21] and extend it from MDPs to MGs. The second and more prominent issue is in the optimistic planning procedure. Since 'global optimism' only obtains optimism along the trajectories of behaviour policies, this causes the distribution shift from the target policies (i.e. NE). As a result, using CCE to plan will cause the regret to diverge. To deal with this problem, we apply a method called 'alternate optimism', which was previously used in [WHL17] for model-based methods. The 'alternate optimism' used in this work also works for valuebased methods. We prove two regret decomposition lemmata, laying a theoretical footing for its strong applicability in MGs.PreliminariesWe consider two-player zero-sum simultaneous-moves episodic Markov game, defined by the tuplewhere S is the state space, A i is a finite set of actions that player i ∈ {1, 2} can take, r is the reward function, P is the transition kernel and H is the number of time steps. At each time step h ∈ [H], player P1 and P2 take actions a ∈ A 1 and b ∈ A 2 respectively upon observing the state x ∈ S, and then both receive the reward r h (x, a, b). The system then transitions to a new state x ′ ∼ P h (·|x, a, b) according to the transition kernel P. Throughout this paper, we assume for simplicity that A 1 = A 2 = A and that the rewards r h (x, a, b) are deterministic functions of the tuple (x, a, b) taking values in [−1, 1]. Turn-based games are special cases of simultaneous games in the sense that at each state the reward and | Towards General Function Approximation in Zero-Sum Markov Games |
d1803861 | We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture whose compositional structure factorizes object dynamics into pairwise interactions. Like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions; realized as a neural network, it can be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds. By comparing to less structured architectures, we show that the NPE's compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass. | A COMPOSITIONAL OBJECT-BASED APPROACH TO LEARNING PHYSICAL DYNAMICS |
d231719730 | We propose the task of disambiguating symbolic expressions in informal STEM documents in the form of L A T E X files -that is, determining their precise semantics and abstract syntax tree -as a neural machine translation task. We discuss the distinct challenges involved and present a dataset with roughly 33,000 entries. We evaluated several baseline models on this dataset, which failed to yield even syntactically valid L A T E X before overfitting. Consequently, we describe a methodology using a transformer language model pre-trained on sources obtained from arxiv.org, which yields promising results despite the small size of the dataset. We evaluate our model using a plurality of dedicated techniques, taking the syntax and semantics of symbolic expressions into account. | DISAMBIGUATING SYMBOLIC EXPRESSIONS IN INFORMAL DOCUMENTS |
d258947377 | Whitening loss provides theoretical guarantee in avoiding feature collapse for self-supervised learning (SSL) using joint embedding architectures.One typical implementation of whitening loss is hard whitening that designs whitening transformation over embedding and imposes the loss on the whitened output.In this paper, we propose spectral transformation (ST) framework to map the spectrum of embedding to a desired distribution during forward pass, and to modulate the spectrum of embedding by implicit gradient update during backward pass.We show that whitening transformation is a special instance of ST by definition, and there exist other instances that can avoid collapse by our empirical investigation.Furthermore, we propose a new instance of ST, called IterNorm with trace loss (INTL).We theoretically prove that INTL can avoid collapse and modulate the spectrum of embedding towards an equal-eigenvalue distribution during the course of optimization.Moreover, INTL achieves 76.6% top-1 accuracy in linear evaluation on ImageNet using ResNet-50, which exceeds the performance of the supervised baseline, and this result is obtained by using a batch size of only 256.Comprehensive experiments show that INTL is a promising SSL method in practice.The code is available at https://github.com/winci-ai/intl. | Modulate Your Spectrum in Self-Supervised Learning |
d58014184 | Neural Processes (NPs)(Garnelo et al., 2018a;b)approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled. | ATTENTIVE NEURAL PROCESSES |
d249538415 | Construction of a scaffold structure that supports a desired motif, conferring protein function, shows promise for the design of vaccines and enzymes. But a general solution to this motif-scaffolding problem remains open. Current machine-learning techniques for scaffold design are either limited to unrealistically small scaffolds (up to length 20) or struggle to produce multiple diverse scaffolds. We propose to learn a distribution over diverse and longer protein backbone structures via an E(3)equivariant graph neural network. We develop SMCDiff to efficiently sample scaffolds from this distribution conditioned on a given motif; our algorithm is the first to theoretically guarantee conditional samples from a diffusion model in the largecompute limit. We evaluate our designed backbones by how well they align with AlphaFold2-predicted structures. We show that our method can (1) sample scaffolds up to 80 residues and (2) achieve structurally diverse scaffolds for a fixed motif. 1 * Contributed equally to this work. . Robust deep learning-based protein sequence design using ProteinMPNN. Science, 378(6615):49-56, 2022.Arnaud Doucet and Adam M Johansen. A tutorial on particle filtering and smoothing: Fifteen years later. | DIFFUSION PROBABILISTIC MODELING OF PROTEIN BACKBONES IN 3D FOR THE MOTIF-SCAFFOLDING PROBLEM |
d1248661 | Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-called 'adversarial subspaces') in which adversarial examples lie. In particular, effective measures are required to discriminate adversarial examples from normal examples in such regions. We tackle this challenge by characterizing the dimensional properties of adversarial regions, via the use of Local Intrinsic Dimensionality (LID). LID assesses the space-filling capability of the region surrounding a reference example, based on the distance distribution of the example to its neighbors. We first provide explanations about how adversarial perturbation can affect the LID characteristic of adversarial regions, and then show empirically that LID characteristics can facilitate the detection of adversarial examples generated using the state-of-the-art attacks. We show that when applied for adversarial detection, an LID-based method can outperform several state-of-the-art detection measures by large margins for five attack strategies across three benchmark datasets. Our analysis of the LID characteristic for adversarial regions not only motivates new directions of effective adversarial defense, but also opens up more challenges for developing new attacks to better understand the vulnerabilities of DNNs. | CHARACTERIZING ADVERSARIAL SUBSPACES USING LOCAL INTRINSIC DIMENSIONALITY |
d3051911 | We describe a neural attention model with a learnable retinal sampling lattice. The model is trained on a visual search task requiring the classification of an object embedded in a visual scene amidst background distractors using the smallest number of fixations. We explore the tiling properties that emerge in the model's retinal sampling lattice after training. Specifically, we show that this lattice resembles the eccentricity dependent sampling lattice of the primate retina, with a high resolution region in the fovea surrounded by a low resolution periphery. Furthermore, we find conditions where these emergent properties are amplified or eliminated providing clues to their function. | EMERGENCE OF FOVEAL IMAGE SAMPLING FROM LEARNING TO ATTEND IN VISUAL SCENES |
d252968170 | While large-scale sequence modeling from offline data has led to impressive performance gains in natural language and image generation, directly translating such ideas to robotics has been challenging. One critical reason for this is that uncurated robot demonstration data, i.e. play data, collected from non-expert human demonstrators are often noisy, diverse, and distributionally multi-modal. This makes extracting useful, task-centric behaviors from such data a difficult generative modeling problem. In this work, we present Conditional Behavior Transformers (C-BeT), a method that combines the multi-modal generation ability of Behavior Transformer with future-conditioned goal specification. On a suite of simulated benchmark tasks, we find that C-BeT improves upon prior state-of-the-art work in learning from play data by an average of 45.7%. Further, we demonstrate for the first time that useful task-centric behaviors can be learned on a real-world robot purely from play data without any task labels or reward information. Robot videos are best viewed on our project website: play-to-policy. | FROM PLAY TO POLICY: CONDITIONAL BEHAVIOR GENERATION FROM UNCURATED ROBOT DATA |
d264406180 | Understanding the inner workings of machine learning models like Transformers is vital for their safe and ethical use.This paper presents an in-depth analysis of a one-layer Transformer model trained for integer addition.We reveal that the model divides the task into parallel, digit-specific streams and employs distinct algorithms for different digit positions.Our study also finds that the model starts calculations late but executes them rapidly.A rare use case with high loss is identified and explained.Overall the model's algorithm is explained in detail.These findings are validated through rigorous testing and mathematical modeling, contributing to the broader works in Mechanistic Interpretability, AI safety, and alignment.Our approach opens the door for analyzing more complex tasks and multi-layer Transformer models. | Understanding Addition in Transformers |
d59413817 | Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory. It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency -a shallow network of exponentially large width is necessary to realize the same score function as computed by such an RNN. Such networks, however, are not very often applied to real life tasks. In this work, we attempt to reduce the gap between theory and practice by extending the theoretical analysis to RNNs which employ various nonlinearities, such as Rectified Linear Unit (ReLU), and show that they also benefit from properties of universality and depth efficiency. Our theoretical results are verified by a series of extensive computational experiments. | GENERALIZED TENSOR MODELS FOR RECURRENT NEURAL NETWORKS |
d256826752 | We introduce the use of generative adversarial learning to compute equilibria in general gametheoretic settings, specifically the generalized Nash equilibrium (GNE) in pseudo-games, and its specific instantiation as the competitive equilibrium (CE) in Arrow-Debreu competitive economies. Pseudo-games are a generalization of games in which players' actions affect not only the payoffs of other players but also their feasible action spaces. Although the computation of GNE and CE is intractable in the worst-case, i.e., PPAD-hard, in practice, many applications only require solutions with high accuracy in expectation over a distribution of problem instances. We introduce Generative Adversarial Equilibrium Solvers (GAES): a family of generative adversarial neural networks that can learn GNE and CE from only a sample of problem instances. We provide computational and sample complexity bounds, and apply the framework to finding Nash equilibria in normal-form games, CE in Arrow-Debreu competitive economies, and GNE in an environmental economic model of the Kyoto mechanism. * Research conducted while the author was an intern at DeepMind. Generative Adversarial Equilibrium Solvers strategy sets. 3 The formalism of pseudo-games was introduced by Arrow & Debreu (1954), who used it in studying their foundational microeconomic equilibrium model, the competitive economy model. | GENERATIVE ADVERSARIAL EQUILIBRIUM SOLVERS |
d238419359 | Question Answering (QA) has been a long-standing research topic in AI and NLP fields, and a wealth of studies have been conducted to attempt to equip QA systems with human-level reasoning capability. To approximate the complicated human reasoning process, state-of-the-art QA systems commonly use pre-trained language models (LMs) to access knowledge encoded in LMs together with elaborately designed modules based on Graph Neural Networks (GNNs) to perform reasoning over knowledge graphs (KGs). However, many problems remain open regarding the reasoning functionality of these GNN-based modules. Can these GNN-based modules really perform a complex reasoning process? Are they under-or overcomplicated for QA? To open the black box of GNN and investigate these problems, we dissect state-of-the-art GNN modules for QA and analyze their reasoning capability. We discover that even a very simple graph neural counter can outperform all the existing GNN modules on CommonsenseQA and OpenBookQA, two popular QA benchmark datasets which heavily rely on knowledge-aware reasoning. Our work reveals that existing knowledge-aware GNN modules may only carry out some simple reasoning such as counting. It remains a challenging open problem to build comprehensive reasoning modules for knowledge-powered QA. * Work done during an internship at MSRA | GNN IS A COUNTER? REVISITING GNN FOR QUESTION ANSWERING |
d264306063 | Widely used language models (LMs) are typically built by scaling up a two-stage training pipeline: a pretraining stage that uses a very large, diverse dataset of text and a fine-tuning (sometimes, 'alignment') stage that uses targeted examples or other specifications of desired behaviors.While it has been hypothesized that knowledge and skills come from pre-training, and fine-tuning mostly filters this knowledge and skillset, this intuition has not been extensively tested.To aid in doing so, we introduce a novel technique for decoupling the knowledge and skills gained in these two stages, enabling a direct answer to the question, What would happen if we combined the knowledge learned by a large model during pre-training with the knowledge learned by a small model during fine-tuning (or vice versa)?Using an RL-based framework derived from recent developments in learning from human preferences, we introduce emulated fine-tuning (EFT), a principled and practical method for sampling from a distribution that approximates (or 'emulates') the result of pre-training and fine-tuning at different scales.Our experiments with EFT show that scaling up fine-tuning tends to improve helpfulness, while scaling up pre-training tends to improve factuality.Beyond decoupling scale, we show that EFT enables test-time adjustment of competing behavioral traits like helpfulness and harmlessness without additional training.Finally, a special case of emulated finetuning, which we call LM up-scaling, avoids resource-intensive fine-tuning of large pre-trained models by ensembling them with small fine-tuned models, essentially emulating the result of fine-tuning the large pre-trained model.Up-scaling consistently improves helpfulness and factuality of instruction-following models in the Llama, Llama-2, and Falcon families, without additional hyperparameters or training. | An Emulator for Fine-Tuning Large Language Models using Small Language Models |
d244714829 | We propose a novel scene representation that encodes reaching distance -the distance between any position in the scene to a goal along a feasible trajectory. We demonstrate that this environment field representation can directly guide the dynamic behaviors of agents in 2D mazes or 3D indoor scenes. Our environment field is a continuous representation and learned via a neural implicit function using discretely sampled training data. We showcase its application for agent navigation in 2D mazes, and human trajectory prediction in 3D indoor environments. To produce physically plausible and natural trajectories for humans, we additionally learn a generative model that predicts regions where humans commonly appear, and enforce the environment field to be defined within such regions. Extensive experiments demonstrate that the proposed method can generate both feasible and plausible trajectories efficiently and accurately. | LEARNING CONTINUOUS ENVIRONMENT FIELDS VIA IMPLICIT FUNCTIONS |
d253510295 | We propose a novel edge guided generative adversarial network with contrastive learning (ECGAN) for the challenging semantic image synthesis task. Although considerable improvement has been achieved, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic labels do not provide detailed structural information, making it difficult to synthesize local details and structures. 2) The widely adopted CNN operations such as convolution, down-sampling, and normalization usually cause spatial resolution loss and thus cannot fully preserve the original semantic information, leading to semantically inconsistent results (e.g., missing small objects). 3) Existing semantic image synthesis methods focus on modeling "local" semantic information from a single input semantic layout. However, they ignore "global" semantic information of multiple input semantic layouts, i.e., semantic cross-relations between pixels across different input layouts. To tackle 1), we propose to use edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module. Edge information is produced by a convolutional generator and introduces detailed structure information. To tackle 2), we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout to preserve the semantic information. To tackle 3), inspired by current methods in contrastive learning, we propose a novel contrastive learning method, which aims to enforce pixel embeddings belonging to the same semantic class to generate more similar image content than those from different classes. Doing so can capture more semantic relations by explicitly exploring the structures of labeled pixels from multiple input semantic layouts. Experiments on three challenging datasets show that our ECGAN achieves significantly better results than state-of-the-art methods.Figure 1: Overview of the proposed ECGAN. It consists of a parameter-sharing encoder E, an edge generator G e , an image generator G i , an attention guided edge transfer module G t , a label generator G l , a similarity loss module, a contrastive learning module G c (not shown for brevity), and a multimodality discriminator D. G e and G i are connected by G t from two levels, i.e., edge feature-level and content-level, to generate realistic images. G s is proposed to preserve the semantic information of the input semantic labels. G l aims to transfer the generated image back to the label for calculating the similarity loss. G c tries to capture more semantic relations by explicitly exploring the structures of labeled pixels from multiple input semantic layouts. D aims to distinguish the outputs from two modalities, i.e., edge and image. The symbol c denotes channel-wise concatenation.Published as a conference paper at ICLR 2023 Xun Huang and Serge Belongie. Arbitrary style transfer in real-time with adaptive instance normalization. In ICCV, 2017. 6 | EDGE GUIDED GANS WITH CONTRASTIVE LEARNING FOR SEMANTIC IMAGE SYNTHESIS |
d252873224 | The implicit biases of gradient-based optimization algorithms are conjectured to be a major factor in the success of modern deep learning. In this work, we investigate the implicit bias of gradient flow and gradient descent in two-layer fully-connected neural networks with leaky ReLU activations when the training data are nearly-orthogonal, a common property of high-dimensional data. For gradient flow, we leverage recent work on the implicit bias for homogeneous neural networks to show that asymptotically, gradient flow produces a neural network with rank at most two. Moreover, this network is an * Equal contribution. | Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data |
d14298291 | Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence networks, for the problem of learning continuous semantic representations of algebraic and logical expressions. These networks are trained to represent semantic equivalence, even of expressions that are syntactically very different. The challenge is that semantic representations must be computed in a syntax-directed manner, because semantics is compositional, but at the same time, small changes in syntax can lead to very large changes in semantics, which can be difficult for continuous neural architectures. We perform an exhaustive evaluation on the task of checking equivalence on a highly diverse class of symbolic algebraic and boolean expression types, showing that our model significantly outperforms existing architectures. | Learning Continuous Semantic Representations of Symbolic Expressions |
d228705808 | Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains. In this paper, we propose a novel framework for inferring multivariate Granger causality under nonlinear dynamics based on an extension of self-explaining neural networks. This framework is more interpretable than other neural-network-based techniques for inferring Granger causality, since in addition to relational inference, it also allows detecting signs of Granger-causal effects and inspecting their variability over time. In comprehensive experiments on simulated data, we show that our framework performs on par with several powerful baseline methods at inferring Granger causality and that it achieves better performance at inferring interaction signs. The results suggest that our framework is a viable and more interpretable alternative to sparse-input neural networks for inferring Granger causality. | INTERPRETABLE MODELS FOR GRANGER CAUSALITY USING SELF-EXPLAINING NEURAL NETWORKS |
d49428777 | Neuronal assemblies, loosely defined as subsets of neurons with reoccurring spatiotemporally coordinated activation patterns, or "motifs", are thought to be building blocks of neural representations and information processing. We here propose LeMoNADe, a new exploratory data analysis method that facilitates hunting for motifs in calcium imaging videos, the dominant microscopic functional imaging modality in neurophysiology. Our nonparametric method extracts motifs directly from videos, bypassing the difficult intermediate step of spike extraction. Our technique augments variational autoencoders with a discrete stochastic node, and we show in detail how a differentiable reparametrization and relaxation can be used. An evaluation on simulated data, with available ground truth, reveals excellent quantitative performance. In real video data acquired from brain slices, with no ground truth available, LeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses for more focused biological investigations. | LEMONADE: LEARNED MOTIF AND NEURONAL ASSEMBLY DETECTION IN CALCIUM IMAGING VIDEOS |
d256846551 | We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect models to not only answer questions, but also produce step-by-step structured explanations describing how premises in the question are used to produce intermediate conclusions that can prove the correctness of a certain answer. We perform extensive evaluation with popular language models such as few-shot prompting GPT-3 and fine-tuned T5. We find that these models still lag behind human performance when producing such structured reasoning steps. We believe this work will provide a way for the community to better train and test systems on multi-step reasoning and explanations in natural language. | STREET: A MULTI-TASK STRUCTURED REASONING AND EXPLANATION BENCHMARK |
d252683429 | We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods. TabPFN performs in-context learning (ICL), it learns to make predictions using sequences of labeled examples (x, f(x)) given in the input, without requiring further parameter updates. TabPFN is fully entailed in the weights of our network, which accepts training and test samples as a set-valued input and yields predictions for the entire test set in a single forward pass. TabPFN is a Prior-Data Fitted Network (PFN) and is trained offline once, to approximate Bayesian inference on synthetic datasets drawn from our prior. This prior incorporates ideas from causal reasoning: It entails a large space of structural causal models with a preference for simple structures. On the 18 datasets in the OpenML-CC18 suite that contain up to 1 000 training data points, up to 100 purely numerical features without missing values, and up to 10 classes, we show that our method clearly outperforms boosted trees and performs on par with complex state-of-the-art AutoML systems with up to 230× speedup. This increases to a 5 700× speedup when using a GPU. We also validate these results on an additional 67 small numerical datasets from OpenML. We provide all our code, the trained TabPFN, an interactive browser demo and a Colab notebook at https://github.com/automl/TabPFN.arXiv:2207.01848v6 [cs.LG] 16 Sep 2023Published as a conference paper at ICLR 2023 prior is defined via parametric distributions, e.g., a log-scaled uniform distribution for the average number of nodes in data-generating SCMs. The resulting PPD implicitly models uncertainty over all possible data-generating mechanisms, weighting them by their likelihood given the data and their prior probability. Thus, the PPD corresponds to an infinitely large ensemble of data-generating mechanisms, i.e., instantiations of SCMs and BNNs. We learn to approximate this complex PPD in a single forward-pass, requiring no cross-validation or model selection.Our key contribution is to introduce the TabPFN (see Section 3), a single Transformer that has been pre-trained to approximate probabilistic inference for the novel prior above (described in more detail in Section 4) in a single forward pass, and has thus learned to solve novel small tabular classification tasks (≤ 1 000 training examples, ≤ 100 purely numerical features without missing values and ≤ 10 classes) in less than a second yielding state-of-the-art performance. | TABPFN: A TRANSFORMER THAT SOLVES SMALL TABULAR CLASSIFICATION PROBLEMS IN A SECOND |
d53094405 | Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments. Herein, we show that by using notes as an intermediate representation, we can train a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure on timescales spanning six orders of magnitude (∼0.1 ms to ∼100 s), a process we call Wave2Midi2Wave. This large advance in the state of the art is enabled by our release of the new MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) dataset, composed of over 172 hours of virtuosic piano performances captured with fine alignment (≈3 ms) between note labels and audio waveforms. The networks and the dataset together present a promising approach toward creating new expressive and interpretable neural models of music. | ENABLING FACTORIZED PIANO MUSIC MODELING AND GENERATION WITH THE MAESTRO DATASET |
d227127234 | We present a hierarchical VAE that, for the first time, outperforms the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that VAEs can actually implement autoregressive models, and other, more efficient generative models, if made sufficiently deep. Despite this, autoregressive models have traditionally outperformed VAEs. We test if insufficient depth explains why by scaling a VAE to greater stochastic depth than previously explored and evaluating it on CIFAR-10, ImageNet, and FFHQ. We find that, in comparison to the PixelCNN, these very deep VAEs achieve higher likelihoods, use fewer parameters, generate samples thousands of times faster, and are more easily applied to high-resolution images. We visualize the generative process and show the VAEs learn efficient hierarchical visual representations. We release our source code and models at https://github.com/openai/vdvae.Low resolutionHigh resolution | VERY DEEP VAES GENERALIZE AUTOREGRESSIVE MODELS AND CAN OUTPERFORM THEM ON IMAGES |
d252693111 | Recently, Transformer-based image restoration networks have achieved promising improvements over convolutional neural networks due to parameter-independent global interactions. To lower computational cost, existing works generally limit self-attention computation within non-overlapping windows. However, each group of tokens are always from a dense area of the image. This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions. Obviously, this strategy could result in restricted receptive fields. To address this issue, we propose Attention Retractable Transformer (ART) for image restoration, which presents both dense and sparse attention modules in the network. The sparse attention module allows tokens from sparse areas to interact and thus provides a wider receptive field. Furthermore, the alternating application of dense and sparse attention modules greatly enhances representation ability of Transformer while providing retractable attention on the input image.We conduct extensive experiments on image super-resolution, denoising, and JPEG compression artifact reduction tasks. Experimental results validate that our proposed ART outperforms state-ofthe-art methods on various benchmark datasets both quantitatively and visually. We also provide code and models at https://github.com/gladzhang/ART. | ACCURATE IMAGE RESTORATION WITH ATTENTION RETRACTABLE TRANSFORMER |
d211068821 | Training machine learning models that can learn complex spatiotemporal dynamics and generalize under distributional shift is a fundamental challenge. The symmetries in a physical system play a unique role in characterizing unchanged features under transformation. We propose a systematic approach to improve generalization in spatiotemporal models by incorporating symmetries into deep neural networks. Our general framework to design equivariant convolutional models employs (1) convolution with equivariant kernels, (2) conjugation by averaging operators in order to force equivariance, (3) and a naturally equivariant generalization of convolution called group correlation. Our framework is both theoretically and experimentally robust to distributional shift by a symmetry group and enjoys favorable sample complexity. We demonstrate the advantage of our approach on a variety of physical dynamics including turbulence and diffusion systems. This is the first time that equivariant CNNs have been used to forecast physical dynamics. | Incorporating Symmetry into Deep Dynamics Models for Improved Generalization |
d3509777 | It is well-known that neural networks are universal approximators, but that deeper networks tend to be much more efficient than shallow ones. We shed light on this by proving that the total number of neurons m required to approximate natural classes of multivariate polynomials of n variables grows only linearly with n for deep neural networks, but grows exponentially when merely a single hidden layer is allowed. We also provide evidence that when the number of hidden layers is increased from 1 to k, the neuron requirement grows exponentially not with n but with n 1/k , suggesting that the minimum number of layers required for computational tractability grows only logarithmically with n. | The power of deeper networks for expressing natural functions |
d247447287 | Humans show language-biased image recognition for a word-embedded image, known as picture-word interference. Such interference depends on hierarchical semantic categories and reflects that human language processing highly interacts with visual processing. Similar to humans, recent artificial models jointly trained on texts and images, e.g., OpenAI CLIP, show language-biased image classification. Exploring whether the bias leads to interference similar to those observed in humans can contribute to understanding how much the model acquires hierarchical semantic representations from joint learning of language and vision. The present study introduces methodological tools from the cognitive science literature to assess the biases of artificial models. Specifically, we introduce a benchmark task to test whether words superimposed on images can distort the image classification across different category levels and, if it can, whether the perturbation is due to the shared semantic representation between language and vision. Our dataset is a set of word-embedded images and consists of a mixture of natural image datasets and hierarchical word labels with superordinate/basic category levels. Using this benchmark test, we evaluate the CLIP model. We show that presenting words distorts the image classification by the model across different category levels, but the effect does not depend on the semantic relationship between images and embedded words. This suggests that the semantic word representation in the CLIP visual processing is not shared with the image representation, although the word representation strongly dominates for word-embedded images. * equal contribution † | LANGUAGE-BIASED IMAGE CLASSIFICATION: EVALUATION BASED ON SEMANTIC REPRESENTATIONS |
d52944914 | Deep neural networks, in particular convolutional neural networks, have become highly effective tools for compressing images and solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements. This success can be attributed in part to their ability to represent and generate natural images well. Contrary to classical tools such as wavelets, image-generating deep neural networks have a large number of parameters-typically a multiple of their output dimension-and need to be trained on large datasets. In this paper, we propose an untrained simple image model, called the deep decoder, which is a deep neural network that can generate natural images from very few weight parameters. The deep decoder has a simple architecture with no convolutions and fewer weight parameters than the output dimensionality. This underparameterization enables the deep decoder to compress images into a concise set of network weights, which we show is on par with wavelet-based thresholding. Further, underparameterization provides a barrier to overfitting, allowing the deep decoder to have state-of-the-art performance for denoising. The deep decoder is simple in the sense that each layer has an identical structure that consists of only one upsampling unit, pixel-wise linear combination of channels, ReLU activation, and channelwise normalization. This simplicity makes the network amenable to theoretical analysis, and it sheds light on the aspects of neural networks that enable them to form effective signal representations. | Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks |
d254044229 | Understanding self-supervised learning is important but challenging. Previous theoretical works study the role of pretraining losses, and view neural networks as general black boxes. However, the recent work ofSaunshi et al. (2022)argues that the model architecture -a component largely ignored by previous worksalso has significant influences on the downstream performance of self-supervised learning. In this work, we provide the first theoretical analysis of self-supervised learning that incorporates the effect of inductive biases originating from the model class. In particular, we focus on contrastive learning -a popular self-supervised learning method that is widely used in the vision domain. We show that when the model has limited capacity, contrastive representations would recover certain special clustering structures that are compatible with the model architecture, but ignore many other clustering structures in the data distribution. As a result, our theory can capture the more realistic setting where contrastive representations have much lower dimensionality than the number of clusters in the data distribution. We instantiate our theory on several synthetic data distributions, and provide empirical evidence to support the theory. | A Theoretical Study of Inductive Biases in Contrastive Learning |
d249461537 | This paper presents a method to build explicit tensor-train (TT) representations. We show that a wide class of tensors can be explicitly represented with sparse TT-cores, obtaining, in many cases, optimal TT-ranks. Numerical experiments show that our method outperforms the existing ones in several practical applications, including game theory problems. Theoretical estimations of the number of operations show that in some problems, such as permanent calculation, our methods are close to the known optimal asymptotics, which are obtained by a completely different type of methods. | Constructive TT-representation of the tensors given as index interaction functions with applications |
d57189428 | The high computational and parameter complexity of neural networks makes their training very slow and difficult to deploy on energy and storage-constrained computing systems. Many network complexity reduction techniques have been proposed including fixed-point implementation. However, a systematic approach for designing full fixed-point training and inference of deep neural networks remains elusive. We describe a precision assignment methodology for neural network training in which all network parameters, i.e., activations and weights in the feedforward path, gradients and weight accumulators in the feedback path, are assigned close to minimal precision. The precision assignment is derived analytically and enables tracking the convergence behavior of the full precision training, known to converge a priori. Thus, our work leads to a systematic methodology of determining suitable precision for fixed-point training. The near optimality (minimality) of the resulting precision assignment is validated empirically for four networks on the CIFAR-10, CIFAR-100, and SVHN datasets. The complexity reduction arising from our approach is compared with other fixed-point neural network designs. Figure 1: Problem setup: FX training at layer l of a DNN showing the quantized tensors and the associatederrors propagate to the network output thereby directly affecting its accuracy (Lin et al., 2016); 2) precision requirements of different variables in a network are interdependent and involve hard-toquantify trade-offs (Sakr et al., 2017); 3) proper quantization requires the knowledge of the dynamic range which may not be available (Pascanu et al., 2013); and 4) quantization errors may accumulate during training and can lead to stability issues(Gupta et al., 2015).Our work makes a major advance in closing this gap by proposing a systematic methodology to obtain close-to-minimum per-layer precision requirements of an FX network that guarantees statistical similarity with full precision training. In particular, we jointly address the challenges of quantization noise, inter-layer and intra-layer precision trade-offs, dynamic range, and stability. As in (Sakr et al., 2017), we do assume that a fully-trained baseline FL network exists and one can observe its learning behavior. While, in principle, such assumption requires extra FL computation prior to FX training, it is to be noted that much of training is done in FL anyway. For instance, FL training is used in order to establish benchmarking baselines such as AlexNet(Krizhevsky et al., 2012), VGG-Net (Simonyan and Zisserman, 2014), and ResNet (He et al., 2016), to name a few. Even if that is not the case, in practice, this assumption can be accounted for via a warm-up FL training on a small held-out portion of the dataset(Dwork et al., 2015).Applying our methodology to three benchmarks reveals several lessons. First and foremost, our work shows that it is possible to FX quantize all variables including back-propagated gradients even though their dynamic range is unknown(Köster et al., 2017). Second, we find that the per-layer weight precision requirements decrease from the input to the output while those of the activation gradients and weight accumulators increase. Furthermore, the precision requirements for residual networks are found to be uniform across layers. Finally, hyper-precision reduction techniques such as weight and activation binarization(Hubara et al., 2016)or gradient ternarization (Wen et al., 2017) are not as efficient as our methodology since these do not address the fundamental problem of realizing true fixed-point DNN training.We demonstrate FX training on three deep learning benchmarks (CIFAR-10, CIFAR-100, SVHN) achieving high fidelity to our FL baseline in that we observe no loss of accuracy higher then 0.56% in all of our experiments. Our precision assignment is further shown to be within 1-b per-tensor of the minimum. We show that our precision assignment methodology reduces representational, computational, and communication costs of training by up to 6×, 8×, and 4×, respectively, compared to the FL baseline and related works. | PER-TENSOR FIXED-POINT QUANTIZATION OF THE BACK-PROPAGATION ALGORITHM |
d252872923 | Devising a fair classifier that does not discriminate against different groups is an important problem in machine learning. Recently, effort-based fairness notions are getting attention, which considers the scenarios of each individual making effort to improve its feature over time. Such scenarios happen in the real world, e.g., college admission and credit loaning, where each rejected sample makes effort to change its features to get accepted afterward. In this paper, we propose a new effortbased fairness notion called Equal Improvability (EI), which equalizes the potential acceptance rate of the rejected samples across different groups assuming a bounded level of effort will be spent by each rejected sample. We also propose and study three different approaches for finding a classifier that satisfies the EI requirement. Through experiments on both synthetic and real datasets, we demonstrate that the proposed EI-regularized algorithms encourage us to find a fair classifier in terms of EI. Additionally, we ran experiments on dynamic scenarios which highlight the advantages of our EI metric in equalizing the distribution of features across different groups, after the rejected samples make some effort to improve. Finally, we provide mathematical analyses of several aspects of EI: the relationship between EI and existing fairness notions, and the effect of EI in dynamic scenarios. Codes are available in a GitHub repository 1 . | EQUAL IMPROVABILITY: A NEW FAIRNESS NOTION CONSIDERING THE LONG-TERM IMPACT |
d231648391 | Knowledge about the locations of keypoints of an object in an image can assist in fine-grained classification and identification tasks, particularly for the case of objects that exhibit large variations in poses that greatly influence their visual appearance, such as wild animals. However, supervised training of a keypoint detection network requires annotating a large image dataset for each animal species, which is a labor-intensive task. To reduce the need for labeled data, we propose to learn simultaneously keypoint heatmaps and pose invariant keypoint representations in a semi-supervised manner using a small set of labeled images along with a larger set of unlabeled images. Keypoint representations are learnt with a semantic keypoint consistency constraint that forces the keypoint detection network to learn similar features for the same keypoint across the dataset. Pose invariance is achieved by making keypoint representations for the image and its augmented copies closer together in feature space. Our semi-supervised approach significantly outperforms previous methods on several benchmarks for human and animal body landmark localization. | SEMI-SUPERVISED KEYPOINT LOCALIZATION |
d58028743 | Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters γ for expectation-based objectives E qγ (y) [f (y)]. Most existing methods either (i) suffer from high variance, seeking help from (often) complicated variance-reduction techniques; or (ii) they only apply to reparameterizable continuous random variables and employ a reparameterization trick. To address these limitations, we propose a General and One-sample (GO) gradient that (i) applies to many distributions associated with non-reparameterizable continuous or discrete random variables, and (ii) has the same low-variance as the reparameterization trick. We find that the GO gradient often works well in practice based on only one Monte Carlo sample (although one can of course use more samples if desired). Alongside the GO gradient, we develop a means of propagating the chain rule through distributions, yielding statistical back-propagation, coupling neural networks to common random variables. | GO GRADIENT FOR EXPECTATION-BASED OBJECTIVES |
d207878944 | As machine learning methods see greater adoption and implementation in high stakes applications such as medical image diagnosis, the need for model interpretability and explanation has become more critical.Classical approaches that assess feature importance (e.g., saliency maps) do not explain how and why a particular region of an image is relevant to the prediction.We propose a method that explains the outcome of a classification black-box by gradually exaggerating the semantic effect of a given class.Given a query input to a classifier, our method produces a progressive set of plausible variations of that query, which gradually changes the posterior probability from its original class to its negation.These counter-factually generated samples preserve features unrelated to the classification decision, such that a user can employ our method as a "tuning knob" to traverse a data manifold while crossing the decision boundary.Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input. | EXPLANATION BY PROGRESSIVE EXAGGERATION |
d252683376 | While the maximum entropy (MaxEnt) reinforcement learning (RL) frameworkoften touted for its exploration and robustness capabilities-is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity. In this work, we propose the adoption of latent variable policies within the MaxEnt framework, which we show can provably approximate any policy distribution, and additionally, naturally emerges under the use of world models with a latent belief state. We discuss why latent variable policies are difficult to train, how naïve approaches can fail, then subsequently introduce a series of improvements centered around low-cost marginalization of the latent state, allowing us to make full use of the latent state at minimal additional cost. We instantiate our method under the actorcritic framework, marginalizing both the actor and critic. The resulting algorithm, referred to as Stochastic Marginal Actor-Critic (SMAC), is simple yet effective. We experimentally validate our method on continuous control tasks, showing that effective marginalization can lead to better exploration and more robust training.Our implementation is open sourced at https://github.com/zdhNarsil/ Stochastic-Marginal-Actor-Critic. * Work done during an internship at Meta AI. Correspondence to: <dinghuai.zhang@mila.quebec>. | LATENT STATE MARGINALIZATION AS A LOW-COST APPROACH FOR IMPROVING EXPLORATION |
d15816492 | We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control how much of the conditioning sequence the channel model needs to read in order to generate a subsequent symbol, we obtain a tractable and effective beam search decoder. Experimental results on abstractive sentence summarisation, morphological inflection, and machine translation show that noisy channel models outperform direct models, and that they significantly benefit from increased amounts of unpaired output data that direct models cannot easily use. | THE NEURAL NOISY CHANNEL |
d265038424 | Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator. We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator. The penalty guarantees the generalization and convergence of GANs. Experiments on synthetic and large scale datasets verify our theoretical analysis.Published as a conference paper at ICLR 2019 2. We show that the original GAN objective encourages gradient exploding in the discriminator. Gradient exploding in the discriminator can lead to mode collapse in the generator.3. We propose a zero-centered gradient penalty (0-GP) for improving the generalization capability of the discriminator. We show that non-zero centered GP and the zero-centered GP proposed inMescheder et al. (2018)cannot make the discriminator generalize. Our 0-GP helps GANs to converge to generalizable equilibria. Theoretical results are verified on real world datasets.4. We show that 0-GP helps the discriminator to distribute its capacity more equally between regions of the space, effectively preventing mode collapse. Experiments on synthetic and real world datasets verify that 0-GP can prevent mode collapse. GANs with 0-GP is much more robust to changes in hyper parameters, optimizers, and network architectures than the original GAN and GANs with other gradient penalties. | IMPROVING GENERALIZATION AND STABILITY OF GENERATIVE ADVERSARIAL NETWORKS |
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