_id stringlengths 4 10 | text stringlengths 0 18.4k | title stringlengths 0 8.56k |
|---|---|---|
d259316542 | Recently, a series of papers proposed deep learning-based approaches to sample from unnormalized target densities using controlled diffusion processes. In this work, we identify these approaches as special cases of the Schrödinger bridge problem, seeking the most likely stochastic evolution between a given prior distribution and the specified target. We further generalize this framework by introducing a variational formulation based on divergences between path space measures of time-reversed diffusion processes. This abstract perspective leads to practical losses that can be optimized by gradient-based algorithms and includes previous objectives as special cases. At the same time, it allows us to consider divergences other than the reverse Kullback-Leibler divergence that is known to suffer from mode collapse. In particular, we propose the so-called logvariance loss, which exhibits favorable numerical properties and leads to significantly improved performance across all considered approaches. | Improved sampling via learned diffusions |
d252595603 | Acquiring labeled data is challenging in many machine learning applications with limited budgets. Active learning gives a procedure to select the most informative data points and improve data efficiency by reducing the cost of labeling. The infomax learning principle maximizing mutual information such as BALD has been successful and widely adapted in various active learning applications. However, this pool-based specific objective inherently introduces a redundant selection and further requires a high computational cost for batch selection. In this paper, we design and propose a new uncertainty measure, Balanced Entropy Acquisition (BalEntAcq), which captures the information balance between the uncertainty of underlying softmax probability and the label variable. To do this, we approximate each marginal distribution by Beta distribution. Beta approximation enables us to formulate BalEntAcq as a ratio between an augmented entropy and the marginalized joint entropy. The closed-form expression of BalEntAcq facilitates parallelization by estimating two parameters in each marginal Beta distribution. BalEntAcq is a purely standalone measure without requiring any relational computations with other data points. Nevertheless, BalEntAcq captures a well-diversified selection near the decision boundary with a margin, unlike other existing uncertainty measures such as BALD, Entropy, or Mean Standard Deviation (MeanSD). Finally, we demonstrate that our balanced entropy learning principle with BalEntAcq 1 consistently outperforms well-known linearly scalable active learning methods, including a recently proposed PowerBALD, a simple but diversified version of BALD, by showing experimental results obtained from MNIST, CIFAR-100, SVHN, and TinyImageNet datasets. | ACTIVE LEARNING IN BAYESIAN NEURAL NETWORKS WITH BALANCED ENTROPY LEARNING PRINCIPLE |
d222133333 | Multitask Reinforcement Learning is a promising way to obtain models with better performance, generalisation, data efficiency, and robustness. Most existing work is limited to compatible settings, where the state and action space dimensions are the same across tasks. Graph Neural Networks (GNN) are one way to address incompatible environments, because they can process graphs of arbitrary size. They also allow practitioners to inject biases encoded in the structure of the input graph. Existing work in graph-based continuous control uses the physical morphology of the agent to construct the input graph, i.e., encoding limb features as node labels and using edges to connect the nodes if their corresponded limbs are physically connected. In this work, we present a series of ablations on existing methods that show that morphological information encoded in the graph does not improve their performance. Motivated by the hypothesis that any benefits GNNs extract from the graph structure are outweighed by difficulties they create for message passing, we also propose AMORPHEUS, a transformer-based approach. Further results show that, while AMORPHEUS ignores the morphological information that GNNs encode, it nonetheless substantially outperforms GNN-based methods. | MY BODY IS A CAGE: THE ROLE OF MORPHOLOGY IN GRAPH-BASED INCOMPATIBLE CONTROL |
d252715459 | We propose topology-aware feature partitioning into k disjoint partitions for given scene features as a method for object-centric representation learning.To this end, we propose to use minimum s-t graph cuts as a partitioning method which is represented as a linear program.The method is topologically aware since it explicitly encodes neighborhood relationships in the image graph.To solve the graph cuts our solution relies on an efficient, scalable, and differentiable quadratic programming approximation.Optimizations specific to cut problems allow us to solve the quadratic programs and compute their gradients significantly more efficiently compared with the general quadratic programming approach.Our results show that our approach is scalable and outperforms existing methods on object discovery tasks with textured scenes and objects. | DIFFERENTIABLE MATHEMATICAL PROGRAMMING FOR OBJECT-CENTRIC REPRESENTATION LEARNING |
d249538584 | Reinforcement Learning (RL) methods are typically applied directly in environments to learn policies. In some complex environments with continuous state-action spaces, sparse rewards, and/or long temporal horizons, learning a good policy in the original environments can be difficult. Focusing on the offline RL setting, we aim to build a simple and discrete world model that abstracts the original environment. RL methods are applied to our world model instead of the environment data for simplified policy learning. Our world model, dubbed Value Memory Graph (VMG), is designed as a directed-graph-based Markov decision process (MDP) of which vertices and directed edges represent graph states and graph actions, separately. As state-action spaces of VMG are finite and relatively small compared to the original environment, we can directly apply the value iteration algorithm on VMG to estimate graph state values and figure out the best graph actions. VMG is trained from and built on the offline RL dataset. Together with an action translator that converts the abstract graph actions in VMG to real actions in the original environment, VMG controls agents to maximize episode returns. Our experiments on the D4RL benchmark show that VMG can outperform state-of-the-art offline RL methods in several goal-oriented tasks, especially when environments have sparse rewards and long temporal horizons. Code is available at | VALUE MEMORY GRAPH: A GRAPH-STRUCTURED WORLD MODEL FOR OFFLINE REINFORCEMENT LEARNING |
d256662277 | Denoising diffusion models have spurred significant gains in density modeling and image generation, precipitating an industrial revolution in text-guided AI art generation. We introduce a new mathematical foundation for diffusion models inspired by classic results in information theory that connect Information with Minimum Mean Square Error regression, the so-called I-MMSE relations. We generalize the I-MMSE relations to exactly relate the data distribution to an optimal denoising regression problem, leading to an elegant refinement of existing diffusion bounds. This new insight leads to several improvements for probability distribution estimation, including theoretical justification for diffusion model ensembling. Remarkably, our framework shows how continuous and discrete probabilities can be learned with the same regression objective, avoiding domain-specific generative models used in variational methods. Code to reproduce experiments is provided at https://github.com/kxh001/ITdiffusion and simplified demonstration code is at https://github.com/gregversteeg/ InfoDiffusionSimple. | INFORMATION-THEORETIC DIFFUSION |
d248228159 | Building sample-efficient agents that generalize out-of-distribution (OOD) in realworld settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition. One particularly promising approach is to begin with low-dimensional, pretrained representations of our world, which should facilitate efficient downstream learning and generalization. By training 240 representations and over 10,000 reinforcement learning (RL) policies on a simulated robotic setup, we evaluate to what extent different properties of pretrained VAE-based representations affect the OOD generalization of downstream agents. We observe that many agents are surprisingly robust to realistic distribution shifts, including the challenging sim-to-real case. In addition, we find that the generalization performance of a simple downstream proxy task reliably predicts the generalization performance of our RL agents under a wide range of OOD settings. Such proxy tasks can thus be used to select pretrained representations that will lead to agents that generalize. | THE ROLE OF PRETRAINED REPRESENTATIONS FOR THE OOD GENERALIZATION OF RL AGENTS |
d221879071 | Recent network pruning methods focus on pruning models early-on in training. To estimate the impact of removing a parameter, these methods use importance measures that were originally designed to prune trained models. Despite lacking justification for their use early-on in training, such measures result in surprisingly low accuracy loss. To better explain this behavior, we develop a general framework that uses gradient flow to unify state-of-the-art importance measures through the norm of model parameters. We use this framework to determine the relationship between pruning measures and evolution of model parameters, establishing several results related to pruning models early-on in training: (i) magnitude-based pruning removes parameters that contribute least to reduction in loss, resulting in models that converge faster than magnitude-agnostic methods; (ii) loss-preservation based pruning preserves first-order model evolution dynamics and is therefore appropriate for pruning minimally trained models; and (iii) gradient-norm based pruning affects second-order model evolution dynamics, such that increasing gradient norm via pruning can produce poorly performing models. We validate our claims on several VGG-13, MobileNet-V1, and ResNet-56 models trained on CIFAR-10/CIFAR-100. 1 gradient flow refers to gradient descent with infinitesimal learning rate; see Equation 6 for a short primer.Published as a conference paper at ICLR 2021 what properties make a parameter dispensable according to a particular importance measure. Our findings follow. (i) Magnitude-based pruning measures remove parameters that contribute least to reduction in loss. This enables magnitude-based pruned models to achieve faster convergence than magnitude-agnostic measures. (ii) Loss-preservation based measures remove parameters with the least tendency to change, thus preserving first-order model evolution dynamics. This shows that loss-preservation is appropriate for pruning models early-on in training as well. (iii) Gradient-norm based pruning is linearly related to second-order model evolution dynamics. Increasing gradient norm via pruning for even slightly trained models can permanently damage earlier layers, producing poorly performing architectures. This behavior is a result of aggressively pruning filters that maximally increase model loss. We validate our claims on several VGG-13, MobileNet-V1, and ResNet-56 models trained on CIFAR-10 and CIFAR-100. | A GRADIENT FLOW FRAMEWORK FOR ANALYZING NETWORK PRUNING |
d264436558 | The inductive bias of a graph neural network (GNN) is largely encoded in its specified graph.Latent graph inference relies on latent geometric representations to dynamically rewire or infer a GNN's graph to maximize the GNN's predictive downstream performance, but it lacks solid theoretical foundations in terms of embedding-based representation guarantees.This paper addresses this issue by introducing a trainable deep learning architecture, coined neural snowflake, that can adaptively implement fractal-like metrics on R d .We prove that any given finite weighted graph can be isometrically embedded by a standard MLP encoder, together with the metric implemented by the neural snowflake.Furthermore, when the latent graph can be represented in the feature space of a sufficiently regular kernel, we show that the combined neural snowflake and MLP encoder do not succumb to the curse of dimensionality by using only a low-degree polynomial number of parameters in the number of nodes.This implementation enables a low-dimensional isometric embedding of the latent graph.We conduct synthetic experiments to demonstrate the superior metric learning capabilities of neural snowflakes when compared to more familiar spaces like Euclidean space.Additionally, we carry out latent graph inference experiments on graph benchmarks.Consistently, the neural snowflake model achieves predictive performance that either matches or surpasses that of the state-of-the-art latent graph inference models.Importantly, this performance improvement is achieved without requiring random search for optimal latent geometry.Instead, the neural snowflake model achieves this enhancement in a differentiable manner. | NEURAL SNOWFLAKES: UNIVERSAL LATENT GRAPH INFERENCE VIA TRAINABLE LATENT GEOMETRIES |
d9226593 | Visual servoing involves choosing actions that move a robot in response to observations from a camera, in order to reach a goal configuration in the world. Standard visual servoing approaches typically rely on manually designed features and analytical dynamics models, which limits their generalization capability and often requires extensive application-specific feature and model engineering.In this work, we study how learned visual features, learned predictive dynamics models, and reinforcement learning can be combined to learn visual servoing mechanisms. We focus on target following, with the goal of designing algorithms that can learn a visual servo using low amounts of data of the target in question, to enable quick adaptation to new targets. Our approach is based on servoing the camera in the space of learned visual features, rather than image pixels or manually-designed keypoints. We demonstrate that standard deep features, in our case taken from a model trained for object classification, can be used together with a bilinear predictive model to learn an effective visual servo that is robust to visual variation, changes in viewing angle and appearance, and occlusions. A key component of our approach is to use a sample-efficient fitted Q-iteration algorithm to learn which features are best suited for the task at hand. We show that we can learn an effective visual servo on a complex synthetic car following benchmark using just 20 training trajectory samples for reinforcement learning. We demonstrate substantial improvement over a conventional approach based on image pixels or hand-designed keypoints, and we show an improvement in sample-efficiency of more than two orders of magnitude over standard model-free deep reinforcement learning algorithms. Videos are available at Rives. A new approach to visual servoing in robotics.ularized fitted Q-iteration for planning in continuous-space Markovian decision problems. In American Control Conference (ACC), pp. 725-730. IEEE, 2009. John T Feddema and Owen Robert Mitchell. Vision-guided servoing with feature-based trajectory generation (for robots). | LEARNING VISUAL SERVOING WITH DEEP FEATURES AND FITTED Q-ITERATION |
d236087592 | An important component for generalization in machine learning is to uncover underlying latent factors of variation as well as the mechanism through which each factor acts in the world. In this paper, we test whether 17 unsupervised, weakly supervised, and fully supervised representation learning approaches correctly infer the generative factors of variation in simple datasets (dSprites, Shapes3D, MPI3D) from controlled environments, and on our contributed CelebGlow dataset. In contrast to prior robustness work that introduces novel factors of variation during test time, such as blur or other (un)structured noise, we here recompose, interpolate, or extrapolate only existing factors of variation from the training data set (e.g., small and medium-sized objects during training and large objects during testing). Models that learn the correct mechanism should be able to generalize to this benchmark. In total, we train and test 2000+ models and observe that all of them struggle to learn the underlying mechanism regardless of supervision signal and architectural bias. Moreover, the generalization capabilities of all tested models drop significantly as we move from artificial datasets towards more realistic real-world datasets. Despite their inability to identify the correct mechanism, the models are quite modular as their ability to infer other in-distribution factors remains fairly stable, providing only a single factor is out-of-distribution. These results point to an important yet understudied problem of learning mechanistic models of observations that can facilitate generalization. Tenenbaum. Simulation as an engine of physical scene understanding. . Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8):1798-1828, 2013. M. Besserve, R. Sun, D. Janzing, and B. Schölkopf. A theory of independent mechanisms for extrapolation in generative models. | VISUAL REPRESENTATION LEARNING DOES NOT GENERALIZE STRONGLY WITHIN THE SAME DOMAIN |
d211818298 | Class selectivity-typically defined as how different a neuron's responses are across different classes of stimuli or data samples-is a common metric used to interpret the function of individual neurons in biological and artificial neural networks. However, it remains an open question whether it is necessary and/or sufficient for deep neural networks (DNNs) to learn class selectivity in individual units. In order to investigate the causal impact of class selectivity on network function, we directly regularize for or against class selectivity. Using this regularizer, we were able to reduce mean class selectivity across units in convolutional neural networks by a factor of 2.5 with no impact on test accuracy, and reduce it nearly to zero with only a small (∼2%) change in test accuracy. In contrast, increasing class selectivity beyond the levels naturally learned during training had rapid and disastrous effects on test accuracy. These results indicate that class selectivity in individual units is neither neither sufficient nor strictly necessary for DNN performance, and more generally encourage caution when focusing on the properties of single units as representative of the mechanisms by which DNNs function. | Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs |
d259164552 | Due to the limited scale and quality of video-text training corpus, most visionlanguage foundation models employ image-text datasets for pretraining and primarily focus on modeling visually semantic representations while disregarding temporal semantic representations and correlations. To address this issue, we propose COSA, a COncatenated SAmple pretrained vision-language foundation model. COSA jointly models visual contents and event-level temporal cues using only image-text corpora. We achieve this by sequentially concatenating multiple image-text pairs as inputs for pretraining. This transformation effectively converts existing image-text corpora into a pseudo long-form video-paragraph corpus, enabling richer scene transformations and explicit event-description correspondence. Extensive experiments demonstrate that COSA consistently improves performance across a broad range of downstream tasks, including long-form/short-form videotext tasks and image-text tasks such as retrieval, captioning, and question answering. Notably, COSA achieves state-of-the-art results on various competitive benchmarks. Code and model are released at https://github.com/TXH-mercury/COSA. * Work done during an internship at Bytedance. † Corresponding authors.Preprint. Under review. | COSA: Concatenated Sample Pretrained Vision-Language Foundation Model |
d247315014 | Fully exploiting the learning capacity of neural networks requires overparameterized dense networks. On the other side, directly training sparse neural networks typically results in unsatisfactory performance. Lottery Ticket Hypothesis (LTH) provides a novel view to investigate sparse network training and maintain its capacity. Concretely, it claims there exist winning tickets from a randomly initialized network found by iterative magnitude pruning and preserving promising trainability (or we say being in trainable condition). In this work, we regard the winning ticket from LTH as the subnetwork which is in trainable condition and its performance as our benchmark, then go from a complementary direction to articulate the Dual Lottery Ticket Hypothesis (DLTH): Randomly selected subnetworks from a randomly initialized dense network can be transformed into a trainable condition and achieve admirable performance compared with LTH -random tickets in a given lottery pool can be transformed into winning tickets. Specifically, by using uniform-randomly selected subnetworks to represent the general cases, we propose a simple sparse network training strategy, Random Sparse Network Transformation (RST), to substantiate our DLTH. Concretely, we introduce a regularization term to borrow learning capacity and realize information extrusion from the weights which will be masked. After finishing the transformation for the randomly selected subnetworks, we conduct the regular finetuning to evaluate the model using fair comparisons with LTH and other strong baselines. Extensive experiments on several public datasets and comparisons with competitive approaches validate our DLTH as well as the effectiveness of the proposed model RST. Our work is expected to pave a way for inspiring new research directions of sparse network training in the future. Our code is available at https://github.com/yueb17/DLTH. | DUAL LOTTERY TICKET HYPOTHESIS |
d52909749 | We present Optimal Completion Distillation (OCD), a training procedure for optimizing sequence to sequence models based on edit distance. OCD is efficient, has no hyper-parameters of its own, and does not require pretraining or joint optimization with conditional log-likelihood. Given a partial sequence generated by the model, we first identify the set of optimal suffixes that minimize the total edit distance, using an efficient dynamic programming algorithm. Then, for each position of the generated sequence, we use a target distribution that puts equal probability on the first token of all the optimal suffixes. OCD achieves the state-ofthe-art performance on end-to-end speech recognition, on both Wall Street Journal and Librispeech datasets, achieving 9.3% WER and 4.5% WER respectively. | OPTIMAL COMPLETION DISTILLATION FOR SEQUENCE LEARNING |
d252544861 | Sliced Wasserstein (SW) distance has been widely used in different application scenarios since it can be scaled to a large number of supports without suffering from the curse of dimensionality. The value of sliced Wasserstein distance is the average of transportation cost between one-dimensional representations (projections) of original measures that are obtained by Radon Transform (RT). Despite its efficiency in the number of supports, estimating the sliced Wasserstein requires a relatively large number of projections in high-dimensional settings. Therefore, for applications where the number of supports is relatively small compared with the dimension, e.g., several deep learning applications where the mini-batch approaches are utilized, the complexities from matrix multiplication of Radon Transform become the main computational bottleneck. To address this issue, we propose to derive projections by linearly and randomly combining a smaller number of projections which are named bottleneck projections. We explain the usage of these projections by introducing Hierarchical Radon Transform (HRT) which is constructed by applying Radon Transform variants recursively. We then formulate the approach into a new metric between measures, named Hierarchical Sliced Wasserstein (HSW) distance. By proving the injectivity of HRT, we derive the metricity of HSW. Moreover, we investigate the theoretical properties of HSW including its connection to SW variants and its computational and sample complexities. Finally, we compare the computational cost and generative quality of HSW with the conventional SW on the task of deep generative modeling using various benchmark datasets including CIFAR10, CelebA, and Tiny ImageNet 1 .arXiv:2209.13570v5 [stat.ML] 6 Feb 2023Published as a conference paper at ICLR 2023 applications (Le et al., 2021;Xu et al., 2021;Yang et al., 2020). Despite the increasing importance of Wasserstein distance in applications, prior works have alluded to the concerns surrounding the high computational complexity of that distance. When the probability measures have at most n supports, the computational complexity of Wasserstein distance scales with the order of O(n 3 log n) (Pele & Werman, 2009). Additionally, it suffers from the curse of dimensionality, i.e., its sample complexity (the bounding gap of the distance between a probability measure and the empirical measures from its random samples) is of the order of O(n −1/d ) (Fournier & Guillin, 2015), where n is the sample size and d is the number of dimensions.Published as a conference paper at ICLR 2023 randomly from the uniform distribution over the corresponding unit-hypersphere. For the same value of L, we show that the bottleneck projection approach is faster than the conventional approach when the values of L and d are large.Contribution: In summary, our main contributions are two-fold:Published as a conference paper at ICLR 2023 Kilian Fatras, Younes Zine, Rémi Flamary, Rémi Gribonval, and Nicolas Courty. Learning with minibatch Wasserstein: asymptotic and gradient properties. | HIERARCHICAL SLICED WASSERSTEIN DISTANCE |
d53535787 | Convolutional architectures have recently been shown to be competitive on many sequence modelling tasks when compared to the de-facto standard of recurrent neural networks (RNNs), while providing computational and modeling advantages due to inherent parallelism. However, currently there remains a performance gap to more expressive stochastic RNN variants, especially those with several layers of dependent random variables. In this work, we propose stochastic temporal convolutional networks (STCNs), a novel architecture that combines the computational advantages of temporal convolutional networks (TCN) with the representational power and robustness of stochastic latent spaces. In particular, we propose a hierarchy of stochastic latent variables that captures temporal dependencies at different time-scales. The architecture is modular and flexible due to decoupling of deterministic and stochastic layers. We show that the proposed architecture achieves state of the art log-likelihoods across several tasks. Finally, the model is capable of predicting high-quality synthetic samples over a long-range temporal horizon in modeling of handwritten text. | STCN: STOCHASTIC TEMPORAL CONVOLUTIONAL NETWORKS |
d209485573 | Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension. It is time to introduce more challenging datasets to push the development of this field towards more comprehensive reasoning of text. In this paper, we introduce a new Reading Comprehension dataset requiring logical reasoning (ReClor) extracted from standardized graduate admission examinations. As earlier studies suggest, human-annotated datasets usually contain biases, which are often exploited by models to achieve high accuracy without truly understanding the text. In order to comprehensively evaluate the logical reasoning ability of models on ReClor, we propose to identify biased data points and separate them into EASY set while the rest as HARD set. Empirical results show that state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set. However, they struggle on HARD set with poor performance near that of random guess, indicating more research is needed to essentially enhance the logical reasoning ability of current models. 1 * Equal contribution. . Improving language understanding by generative pre-training. URL https://s3-us-west-2. amazonaws. com/openaiassets/researchcovers/languageunsupervised/language understanding paper. pdf, 2018. . Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250, 2016. Pranav Rajpurkar, Robin Jia, and Percy Liang. Know what you don't know: Unanswerable questions for squad. arXiv preprint arXiv:1806.03822, 2018. Matthew Richardson, Christopher JC Burges, and Erin Renshaw. Mctest: A challenge dataset for the open-domain machine comprehension of text. In . Dream: A challenge data set and models for dialogue-based reading comprehension. . A broad-coverage challenge corpus for sentence understanding through inference. In | RECLOR: A READING COMPREHENSION DATASET REQUIRING LOGICAL REASONING |
d259203115 | As their size increases, Large Languages Models (LLMs) are natural candidates for network pruning methods: approaches that drop a subset of network weights while striving to preserve performance.Existing methods, however, require either retraining, which is rarely affordable for billion-scale LLMs, or solving a weight reconstruction problem reliant on second-order information, which may also be computationally expensive.In this paper, we introduce a novel, straightforward yet effective pruning method, termed Wanda (Pruning by Weights and activations), designed to induce sparsity in pretrained LLMs.Motivated by the recent observation of emergent large magnitude features in LLMs, our approach prunes weights with the smallest magnitudes multiplied by the corresponding input activations, on a per-output basis.Notably, Wanda requires no retraining or weight update, and the pruned LLM can be used as is.We conduct a thorough evaluation of our method Wanda on LLaMA and LLaMA-2 across various language benchmarks.Wanda significantly outperforms the established baseline of magnitude pruning and performs competitively against recent method involving intensive weight update.Code is available at https://github.com/locuslab/wanda. | A SIMPLE AND EFFECTIVE PRUNING APPROACH FOR LARGE LANGUAGE MODELS |
d257365071 | An energy-based model (EBM) is a popular generative framework that offers both explicit density and architectural flexibility, but training them is difficult since it is often unstable and time-consuming.In recent years, various training techniques have been developed, e.g., better divergence measures or stabilization in MCMC sampling, but there often exists a large gap between EBMs and other generative frameworks like GANs in terms of generation quality.In this paper, we propose a novel and effective framework for improving EBMs via contrastive representation learning (CRL).To be specific, we consider representations learned by contrastive methods as the true underlying latent variable.This contrastive latent variable could guide EBMs to understand the data structure better, so it can improve and accelerate EBM training significantly.To enable the joint training of EBM and CRL, we also design a new class of latent-variable EBMs for learning the joint density of data and the contrastive latent variable.Our experimental results demonstrate that our scheme achieves lower FID scores, compared to prior-art EBM methods (e.g., additionally using variational autoencoders or diffusion techniques), even with significantly faster and more memory-efficient training.We also show conditional and compositional generation abilities of our latent-variable EBMs as their additional benefits, even without explicit conditional training.The code is available at https://github.com/hankook/CLEL. | GUIDING ENERGY-BASED MODELS VIA CONTRASTIVE LATENT VARIABLES |
d213513188 | Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality. However, Wasserstein GANs require the critic to be 1-Lipschitz, which is often enforced implicitly by penalizing the norm of its gradient, or by globally restricting its Lipschitz constant via weight normalization techniques. Training with a regularization term penalizing the violation of the Lipschitz constraint explicitly, instead of through the norm of the gradient, was found to be practically infeasible in most situations. Inspired by Virtual Adversarial Training, we propose a method called Adversarial Lipschitz Regularization, and show that using an explicit Lipschitz penalty is indeed viable and leads to competitive performance when applied to Wasserstein GANs, highlighting an important connection between Lipschitz regularization and adversarial training. arXiv:1907.05681v3 [cs.LG] 3 Jan 2020Published as a conference paper at ICLR 2020 Our contributions are as follows:• We propose Adversarial Lipschitz Regularization (ALR) and apply it to penalize the violation of the Lipschitz constraint directly, resulting in Adversarial Lipschitz Penalty (ALP). | ADVERSARIAL LIPSCHITZ REGULARIZATION |
d247451183 | Reliable out-of-distribution (OOD) detection is fundamental to implementing safer modern machine learning (ML) systems. In this paper, we introduce IGEOOD, an effective method for detecting OOD samples. IGEOOD applies to any pre-trained neural network, works under various degrees of access to the ML model, does not require OOD samples or assumptions on the OOD data but can also benefit (if available) from OOD samples. By building on the geodesic (Fisher-Rao) distance between the underlying data distributions, our discriminator can combine confidence scores from the logits outputs and the learned features of a deep neural network. Empirically, we show that IGEOOD outperforms competing state-of-the-art methods on a variety of network architectures and datasets. | IGEOOD: AN INFORMATION GEOMETRY APPROACH TO OUT-OF-DISTRIBUTION DETECTION |
d263622464 | Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards.GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space.This paper proposes to train GFlowNets with local search which focuses on exploiting high rewarded sample space to resolve this issue.Our main idea is to explore the local neighborhood via destruction and reconstruction guided by backward and forward policies, respectively.This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme which uses the forward policy to generate the solution from scratch.Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks.Source code is available: https://github.com/dbsxodud-11/ls-gfn. | LOCAL SEARCH GFLOWNETS |
d247476432 | Learning energy-based model (EBM) requires MCMC sampling of the learned model as an inner loop of the learning algorithm. However, MCMC sampling of EBMs in high-dimensional data space is generally not mixing, because the energy function, which is usually parametrized by a deep network, is highly multi-modal in the data space. This is a serious handicap for both theory and practice of EBMs. In this paper, we propose to learn an EBM with a flow-based model (or in general a latent variable model) serving as a backbone, so that the EBM is a correction or an exponential tilting of the flow-based model. We show that the model has a particularly simple form in the space of the latent variables of the backbone model, and MCMC sampling of the EBM in the latent space mixes well and traverses modes in the data space. This enables proper sampling and learning of EBMs. * Equal contribution. † Majority of research was conducted at Google. | MCMC SHOULD MIX: LEARNING ENERGY-BASED MODEL WITH NEURAL TRANSPORT LATENT SPACE MCMC |
d260704723 | Recent advancements in Multimodal Large Language Models (MLLMs) have been utilizing Visual Prompt Generators (VPGs) to convert visual features into tokens that LLMs can recognize. This is achieved by training the VPGs on millions of image-caption pairs, where the VPG-generated tokens of images are fed into a frozen LLM to generate the corresponding captions. However, this image-captioning based training objective inherently biases the VPG to concentrate solely on the primary visual contents sufficient for caption generation, often neglecting other visual details. This shortcoming results in MLLMs' underperformance in comprehending demonstrative instructions consisting of multiple, interleaved, and multimodal instructions that demonstrate the required context to complete a task. To address this issue, we introduce a generic and lightweight Visual Prompt Generator Complete module (VPG-C), which can infer and complete the missing details essential for comprehending demonstrative instructions. Further, we propose a synthetic discriminative training strategy to fine-tune VPG-C, eliminating the need for supervised demonstrative instructions. As for evaluation, we build DEMON, a comprehensive benchmark for demonstrative instruction understanding. Synthetically trained with the proposed strategy, VPG-C achieves significantly stronger zero-shot performance across all tasks of DEMON. Further evaluation on the MME and OwlEval benchmarks also demonstrate the superiority of VPG-C. Our benchmark, code, and pre-trained models are available at | FINE-TUNING MULTIMODAL LLMS TO FOLLOW ZERO-SHOT DEMONSTRATIVE INSTRUCTIONS |
d249191923 | The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to training them with model-agnostic optimizers such as SGD. In this paper, we propose to incorporate model-specific prior knowledge into optimizers by modifying the gradients according to a set of model-specific hyper-parameters. Such a methodology is referred to as Gradient Re-parameterization, and the optimizers are named RepOptimizers. For the extreme simplicity of model structure, we focus on a VGG-style plain model and showcase that such a simple model trained with a RepOptimizer, which is referred to as RepOpt-VGG, performs on par with or better than the recent well-designed models. From a practical perspective, RepOpt-VGG is a favorable base model because of its simple structure, high inference speed and training efficiency. Compared to Structural Re-parameterization, which adds priors into models via constructing extra training-time structures, RepOptimizers require no extra forward/backward computations and solve the problem of quantization. We hope to spark further research beyond the realms of model structure design. Code and models https://github.com/DingXiaoH/RepOptimizers. | RE-PARAMETERIZING YOUR OPTIMIZERS RATHER THAN ARCHITECTURES |
d54083992 | Many real-world systems studied are governed by complex, nonlinear dynamics. By modeling these dynamics, we can gain insight into how these systems work, make predictions about how they will behave, and develop strategies for controlling them. While there are many methods for modeling nonlinear dynamical systems, existing techniques face a trade off between offering interpretable descriptions and making accurate predictions. Here, we develop a class of models that aims to achieve both simultaneously, smoothly interpolating between simple descriptions and more complex, yet also more accurate models. Our probabilistic model achieves this multi-scale property through a hierarchy of locally linear dynamics that jointly approximate global nonlinear dynamics. We call it the tree-structured recurrent switching linear dynamical system. To fit this model, we present a fully-Bayesian sampling procedure using Pólya-Gamma data augmentation to allow for fast and conjugate Gibbs sampling. Through a variety of synthetic and real examples, we show how these models outperform existing methods in both interpretability and predictive capability. | TREE-STRUCTURED RECURRENT SWITCHING LINEAR DYNAMICAL SYSTEMS FOR MULTI-SCALE MODELING |
d251765079 | Generating new molecules with specified chemical and biological properties via generative models has emerged as a promising direction for drug discovery. However, existing methods require extensive training/fine-tuning with a large dataset, often unavailable in real-world generation tasks. In this work, we propose a new retrieval-based framework for controllable molecule generation. We use a small set of exemplar molecules, i.e., those that (partially) satisfy the design criteria, to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria. We design a retrieval mechanism that retrieves and fuses the exemplar molecules with the input molecule, which is trained by a new selfsupervised objective that predicts the nearest neighbor of the input molecule. We also propose an iterative refinement process to dynamically update the generated molecules and retrieval database for better generalization. Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning. On various tasks ranging from simple design criteria to a challenging real-world scenario for designing lead compounds that bind to the SARS-CoV-2 main protease, we demonstrate our approach extrapolates well beyond the retrieval database, and achieves better performance and wider applicability than previous methods. * Work done during an internship at NVIDIA. † The first two authors contributed equally to this paper. | RETRIEVAL-BASED CONTROLLABLE MOLECULE GENERATION |
d257219652 | Graph unlearning, which involves deleting graph elements such as nodes, node labels, and relationships from a trained graph neural network (GNN) model, is crucial for real-world applications where data elements may become irrelevant, inaccurate, or privacy-sensitive. However, existing methods for graph unlearning either deteriorate model weights shared across all nodes or fail to effectively delete edges due to their strong dependence on local graph neighborhoods. To address these limitations, we introduce GNNDELETE, a novel model-agnostic layer-wise operator that optimizes two critical properties, namely, Deleted Edge Consistency and Neighborhood Influence, for graph unlearning. Deleted Edge Consistency ensures that the influence of deleted elements is removed from both model weights and neighboring representations, while Neighborhood Influence guarantees that the remaining model knowledge is preserved after deletion. GNNDELETE updates representations to delete nodes and edges from the model while retaining the rest of the learned knowledge. We conduct experiments on seven real-world graphs, showing that GNNDELETE outperforms existing approaches by up to 38.8% (AUC) on edge, node, and node feature deletion tasks, and 32.2% on distinguishing deleted edges from non-deleted ones. Additionally, GNNDELETE is efficient, taking 12.3x less time and 9.3x less space than retraining GNN from scratch on WordNet18. | GNNDELETE: A GENERAL STRATEGY FOR UNLEARNING IN GRAPH NEURAL NETWORKS |
d250264425 | The vanishing ideal of a set of points = {x 1 , . . . , x } ⊆ ℝ is the set of polynomials that evaluate to 0 over all points x ∈ and admits an efficient representation by a finite subset of generators. In practice, to accommodate noise in the data, algorithms that construct generators of the approximate vanishing ideal are widely studied but their computational complexities remain expensive. In this paper, we scale up the oracle approximate vanishing ideal algorithm (OAVI), the only generator-constructing algorithm with known learning guarantees. We prove that the computational complexity of OAVI is not superlinear, as previously claimed, but linear in the number of samples . In addition, we propose two modifications that accelerate OAVI's training time: Our analysis reveals that replacing the pairwise conditional gradients algorithm, one of the solvers used in OAVI, with the faster blended pairwise conditional gradients algorithm leads to an exponential speed-up in the number of features . Finally, using a new inverse Hessian boosting approach, intermediate convex optimization problems can be solved almost instantly, improving OAVI's training time by multiple orders of magnitude in a variety of numerical experiments. | Approximate Vanishing Ideal Computations at Scale |
d108300573 | In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models. 1 1 Our implementation is available at : https://github.com/mlds-lab/interp-net 1 arXiv:1909.07782v1 [cs.LG] 13 Sep 2019Published as a conference paper at ICLR 2019 trends. Similar to the work ofLipton et al. (2016)andChe et al. (2018a), our architecture also explicitly leverages a separate information channel related to patterns of observation times. However, our representation uses a semi-parametric intensity function representation of this information that is more closely related to the work of Lasko (2014) on modeling medical event point processes.Our architecture thus produces three output time series for each input time series: a smooth interpolation modeling broad trends in the input, a short time-scale interpolation modeling transients, and an intensity function modeling local observation frequencies.This work is motivated by problems in the analysis of electronic health records (EHRs)(Marlin et al., 2012;Lipton et al., 2016;Futoma et al., 2017;Che et al., 2018a). It remains rare for hospital systems to capture dense physiological data streams. Instead, it is common for the physiological time series data in electronic health records to be both sparse and irregularly sampled. The additional issue of the lack of alignment in the observation times across physiological variables is also very common.We evaluate the proposed architecture on two datasets for both classification and regression tasks. Our approach outperforms a variety of simple baseline models as well as the basic and advanced GRU models introduced by Che et al. (2018a) across several metrics. We also compare our model with to the Gaussian process adapter (Li & Marlin, 2016) and multi-task Gaussian process RNN classifier(Futoma et al., 2017). Further, we perform full ablation testing of the information channels our architecture can produce to assess their impact on classification and regression performance. | INTERPOLATION-PREDICTION NETWORKS FOR IRREGULARLY SAMPLED TIME SERIES |
d8394195 | We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to address this need, but are difficult to optimize directly due to the inherent non-differentiabilty of the compression loss. We here show that minimal changes to the loss are sufficient to train deep autoencoders competitive with JPEG 2000 and outperforming recently proposed approaches based on RNNs. Our network is furthermore computationally efficient thanks to a sub-pixel architecture, which makes it suitable for high-resolution images. This is in contrast to previous work on autoencoders for compression using coarser approximations, shallower architectures, computationally expensive methods, or focusing on small images. | LOSSY IMAGE COMPRESSION WITH COMPRESSIVE AUTOENCODERS |
d239016943 | Inspired by the notion that "to copy is easier than to memorize", in this work, we introduce GNN-LM, which extends vanilla neural language model (LM) by allowing to reference similar contexts in the entire training corpus. We build a directed heterogeneous graph between an input context and its semantically related neighbors selected from the training corpus, where nodes are tokens in the input context and retrieved neighbor contexts, and edges represent connections between nodes. Graph neural networks (GNNs) are constructed upon the graph to aggregate information from similar contexts to decode the token. This learning paradigm provides direct access to the reference contexts and helps improve a model's generalization ability. We conduct comprehensive experiments to validate the effectiveness of the GNN-LM: GNN-LM achieves a new state-of-the-art perplexity of 14.8 on WikiText-103 (a 3.9 point improvement over its counterpart of the vanilla LM model), and shows substantial improvement on One Billion Word and Enwiki8 datasets against strong baselines. In-depth ablation studies are performed to understand the mechanics of GNN-LM. 1 | GNN-LM: LANGUAGE MODELING BASED ON GLOBAL CONTEXTS VIA GNN |
d12713052 | Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214. * Work done as a member of the Google Brain Residency program (g.co/brainresidency.) Under review as a conference paper at ICLR 2017 neural network can be typically specified by a variable-length string. It is therefore possible to use a recurrent network -the controller -to generate such string. Training the network specified by the string -the "child network" -on the real data will result in an accuracy on a validation set. Using this accuracy as the reward signal, we can compute the policy gradient to update the controller. As a result, in the next iteration, the controller will give higher probabilities to architectures that receive high accuracies. In other words, the controller will learn to improve its search over time.Our experiments show that Neural Architecture Search can design good models from scratch, an achievement considered not possible with other methods. On image recognition with CIFAR-10, Neural Architecture Search can find a novel ConvNet model that is better than most human-invented architectures. Our CIFAR-10 model achieves a 3.65 test set error, while being 1.05x faster than the current best model. On language modeling with Penn Treebank, Neural Architecture Search can design a novel recurrent cell that is also better than previous RNN and LSTM architectures. The cell that our model found achieves a test set perplexity of 62.4 on the Penn Treebank dataset, which is 3.6 perplexity better than the previous state-of-the-art. | NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING |
d263609190 | State-space models (SSMs) have recently emerged as a framework for learning long-range sequence tasks.An example is the structured state-space sequence (S4) layer, which uses the diagonal-plus-low-rank structure of the HiPPO initialization framework.However, the complicated structure of the S4 layer poses challenges; and, in an effort to address these challenges, models such as S4D and S5 have considered a purely diagonal structure.This choice simplifies the implementation, improves computational efficiency, and allows channel communication.However, diagonalizing the HiPPO framework is itself an ill-posed problem.In this paper, we propose a general solution for this and related ill-posed diagonalization problems in machine learning.We introduce a generic, backward-stable "perturb-thendiagonalize" (PTD) methodology, which is based on the pseudospectral theory of non-normal operators, and which may be interpreted as the approximate diagonalization of the nonnormal matrices defining SSMs.Based on this, we introduce the S4-PTD and S5-PTD models.Through theoretical analysis of the transfer functions of different initialization schemes, we demonstrate that the S4-PTD/S5-PTD initialization strongly converges to the HiPPO framework, while the S4D/S5 initialization only achieves weak convergences.As a result, our new models show resilience to Fourier-mode noise-perturbed inputs, a crucial property not achieved by the S4D/S5 models.In addition to improved robustness, our S5-PTD model averages 87.6% accuracy on the Long-Range Arena benchmark, demonstrating that the PTD methodology helps to improve the accuracy of deep learning models. | Robustifying State-space Models for Long Sequences via Approximate Diagonalization |
d232257635 | Recently, Frankle & Carbin(2019)demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the trained dense network. However, finding these high performing trainable subnetworks is expensive, requiring iterative process of training and pruning weights. In this paper, we propose (and prove) a stronger Multi-Prize Lottery Ticket Hypothesis: A sufficiently over-parameterized neural network with random weights contains several subnetworks (winning tickets) that (a) have comparable accuracy to a dense target network with learned weights (prize 1), (b) do not require any further training to achieve prize 1 (prize 2), and (c) is robust to extreme forms of quantization (i.e., binary weights and/or activation) (prize 3). This provides a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full precision neural networks. We also propose an algorithm for finding multi-prize tickets (MPTs) and test it by performing a series of experiments on CIFAR-10 and ImageNet datasets. Empirical results indicate that as models grow deeper and wider, multi-prize tickets start to reach similar (and sometimes even higher) test accuracy compared to their significantly larger and full-precision counterparts that have been weight-trained. Without ever updating the weight values, our MPTs-1/32 not only set new binary weight network state-of-the-art (SOTA) Top-1 accuracy -94.8% on CIFAR-10 and 74.03% on ImageNet -but also outperform their full-precision counterparts by 1.78% and 0.76%, respectively. Further, our MPT-1/1 achieves SOTA Top-1 accuracy (91.9%) for binary neural networks on CIFAR-10. Code and pre-trained models are available at:Published as a conference paper at ICLR 2021 Figure 1: Multi-Prize Ticket Performance: Multi-prize tickets, obtained only by pruning and binarizing random networks, outperforms trained full precision and SOTA binary weight networks. Although pruning and quantization 1 are typical approaches used for compressing DNNs (Neill, 2020), it is not clear under what conditions and to what extent compression can be achieved without sacrificing the accuracy. The most extreme form of quanitization is binarization, where weights and/or activations can only have two possible values, namely −1(0) or +1 (the interest of this paper).In addition to saving memory, binarization results in more power efficient networks with significant computation acceleration since expensive multiply-accumulate operations (MACs) can be replaced by cheap XNOR and bit-counting operations(Qin et al., 2020a). In light of these benefits, it is of interest to question if conditions exists such that a binarized DNN can be pruned to achieve accuracy comparable to the dense FP DNN. More importantly, even if these favourable conditions are met then how do we find these extremely compressed (or compact) and highly accurate subnetworks?Traditional pruning schemes have shown that a pretrained DNN can be pruned without a significant loss in the performance. Recently, (Frankle & Carbin, 2019) made a breakthrough by showing that dense network contain sparse subnetworks that can match the performance of the original network when trained from scratch with weights being reset to their initialization (Lottery Ticket Hypothesis). Although the original approach to find these subnetworks still required training the dense network, some efforts(Wang et al., 2020b;You et al., 2019;Wang et al., 2020a)have been carried out to overcome this limitation. Recently a more intriguing phenomenon has been reported -a dense network with random initialization contains subnetworks that achieve high accuracy, without any further training(Zhou et al., 2019;Ramanujan et al., 2020;Malach et al., 2020;Orseau et al., 2020). These trends highlight good progress being made towards efficiently and accurately pruning DNNs.In contrast to these positive developments for pruning, results on binarizing DNNs have been mostly negative. To the best of our knowledge, post-training schemes have not been successful in binarizing pretrained models without retraining. Even with training binary neural networks (BNNs) from scratch (though inefficient), the community has not been able to make BNNs achieve comparable results to their full precision counterparts. The main reason being that network structures and weight optimization techniques are predominantly developed for full precision DNNs and may not be suitable for training BNNs. Thus, closing the gap in accuracy between the full precision and the binarized version may require a paradigm shift. Furthermore, this also makes one wonder if efficiently and accurately binarizing DNNs similar to the recent trends in pruning is ever feasible.In this paper, we show that a randomly initialized dense network contains extremely sparse binary subnetworks that without any weight training (i.e., efficient) have comparable performance to their trained dense and full-precision counterparts (i.e., accurate). Based on this, we state our hypothesis:Multi-Prize Lottery Ticket Hypothesis. A sufficiently over-parameterized neural network with random weights contains several subnetworks (winning tickets) that (a) have comparable accuracy to a dense target network with learned weights (prize 1), (b) do not require any further training to achieve prize 1 (prize 2), and (c) is robust to extreme forms of quantization (i.e., binary weights and/or activation) (prize 3).Contributions. First, we propose the multi-prize lottery ticket hypothesis as a new perspective on finding neural networks with drastically reduced memory size, much faster test-time inference and | Multi-Prize Lottery Ticket Hypothesis: FINDING ACCURATE BINARY NEURAL NETWORKS BY PRUNING A RANDOMLY WEIGHTED NETWORK |
d3538865 | Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed. We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time. When applied to online speech recognition, we obtain state-of-theart results and match the performance of a model using an offline soft attention mechanism. In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model. * Equal contribution. | MONOTONIC CHUNKWISE ATTENTION |
d254125312 | Privacy noise may negate the benefits of using adaptive optimizers in differentially private model training. Prior works typically address this issue by using auxiliary information (e.g., public data) to boost the effectiveness of adaptive optimization. In this work, we explore techniques to estimate and efficiently adapt to gradient geometry in private adaptive optimization without auxiliary data. Motivated by the observation that adaptive methods can tolerate stale preconditioners, we propose differentially private adaptive training with delayed preconditioners (DP 2 ), a simple method that constructs delayed but less noisy preconditioners to better realize the benefits of adaptivity. Theoretically, we provide convergence guarantees for our method for both convex and non-convex problems, and analyze trade-offs between delay and privacy noise reduction. Empirically, we explore DP 2 across several realworld datasets, demonstrating that it can improve convergence speed by as much as 4× relative to non-adaptive baselines and match the performance of state-of-the-art optimization methods that require auxiliary data.arXiv:2212.00309v2 [cs.LG] 7 Jun 2023John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. | DIFFERENTIALLY PRIVATE ADAPTIVE OPTIMIZATION WITH DELAYED PRECONDITIONERS |
d227746078 | The task of information retrieval is an important component of many natural language processing systems, such as open domain question answering. While traditional methods were based on hand-crafted features, continuous representations based on neural networks recently obtained competitive results. A challenge of using such methods is to obtain supervised data to train the retriever model, corresponding to pairs of query and support documents. In this paper, we propose a technique to learn retriever models for downstream tasks, inspired by knowledge distillation, and which does not require annotated pairs of query and documents. Our approach leverages attention scores of a reader model, used to solve the task based on retrieved documents, to obtain synthetic labels for the retriever. We evaluate our method on question answering, obtaining state-of-the-art results. | DISTILLING KNOWLEDGE FROM READER TO RETRIEVER FOR QUESTION ANSWERING |
d222177403 | Network binarization is a promising hardware-aware direction for creating efficient deep models. Despite its memory and computational advantages, reducing the accuracy gap between such models and their real-valued counterparts remains an unsolved challenging research problem. To this end, we make the following 3 contributions: (a) To increase model capacity, we propose Expert Binary Convolution, which, for the first time, tailors conditional computing to binary networks by learning to select one data-specific expert binary filter at a time conditioned on input features. (b) To increase representation capacity, we propose to address the inherent information bottleneck in binary networks by introducing an efficient width expansion mechanism which keeps the binary operations within the same budget. (c) To improve network design, we propose a principled binary network growth mechanism that unveils a set of network topologies of favorable properties. Overall, our method improves upon prior work, with no increase in computational cost by ∼ 6%, reaching a groundbreaking ∼ 71% on ImageNet classification. | HIGH-CAPACITY EXPERT BINARY NETWORKS |
d211146411 | Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge. Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation. We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks. We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset(Moghe et al., 2018). | SEQUENTIAL LATENT KNOWLEDGE SELECTION FOR KNOWLEDGE-GROUNDED DIALOGUE |
d3571422 | Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods designed for this adversarial training. In this work, we cast GAN optimization problems in the general variational inequality framework. Tapping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend techniques designed for variational inequalities to the training of GANs. We apply averaging, extrapolation and a novel computationally cheaper variant that we call extrapolation from the past to the stochastic gradient method (SGD) and Adam. | A VARIATIONAL INEQUALITY PERSPECTIVE ON GENERATIVE ADVERSARIAL NETWORKS |
d3285020 | Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared layers.The results indicate that a flexible ordering can enable more effective sharing, thus motivating the development of a soft ordering approach, which learns how shared layers are applied in different ways for different tasks. Deep MTL with soft ordering outperforms parallel ordering methods across a series of domains. These results suggest that the power of deep MTL comes from learning highly general building blocks that can be assembled to meet the demands of each task. | BEYOND SHARED HIERARCHIES: DEEP MULTITASK LEARNING THROUGH SOFT LAYER ORDERING |
d3700344 | We frame Question Answering as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black box question-answering system an which learns to reformulate questions to elicit the best possible answers.The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer.The reformulation system is trained end-to-end to maximize answer quality using policy gradient. We evaluate on SearchQA, a dataset of complex questions extracted from Jeopardy!. Our agent improves F1 by 11% over a state-of-the-art base model that uses the original question/answer pairs. | Ask the Right Questions: Active Question Reformulation with Reinforcement Learning |
d250451329 | In this paper we study online Reinforcement Learning (RL) in partially observable dynamical systems. We focus on the Predictive State Representations (PSRs) model, which is an expressive model that captures other well-known models such as Partially Observable Markov Decision Processes (POMDP). PSR represents the states using a set of predictions of future observations and is defined entirely using observable quantities. We develop a novel model-based algorithm for PSRs that can learn a near optimal policy in sample complexity scaling polynomially with respect to all the relevant parameters of the systems. Our algorithm naturally works with function approximation to extend to systems with potentially large state and observation spaces. We show that given a realizable model class, the sample complexity of learning the near optimal policy only scales polynomially with respect to the statistical complexity of the model class, without any explicit polynomial dependence on the size of the state and observation spaces. Notably, our work is the first work that shows polynomial sample complexities to compete with the globally optimal policy in PSRs. Finally, we demonstrate how our general theorem can be directly used to derive sample complexity bounds for special models including m-step weakly revealing and m-step decodable tabular POMDPs, POMDPs with low-rank latent transition, and POMDPs with linear emission and latent transition. | PAC Reinforcement Learning for Predictive State Representations |
d259203617 | Congestion is a common failure mode of markets, where consumers compete inefficiently on the same subset of goods (e.g., chasing the same small set of properties on a vacation rental platform). The typical economic story is that prices solve this problem by balancing supply and demand in order to decongest the market. But in modern online marketplaces, prices are typically set in a decentralized way by sellers, with the power of a platform limited to controlling representations-the information made available about products. This motivates the present study of decongestion by representation, where a platform uses this power to learn representations that improve social welfare by reducing congestion. The technical challenge is twofold: relying only on revealed preferences from users' past choices, rather than true valuations; and working with representations that determine which features to reveal and are inherently combinatorial. We tackle both by proposing a differentiable proxy of welfare that can be trained end-to-end on consumer choice data. We provide theory giving sufficient conditions for when decongestion promotes welfare, and present experiments on both synthetic and real data shedding light on our setting and approach. * Also DeepMind, London UK Preprint. Under review. arXiv:2306.10606v1 [cs.LG] 18 Jun 2023i.e., when multiple users are interested in a single item of which there are not sufficiently-many copies available. For example, in vacation rentals, an appealing vacation home is likely to draw the interest of many users; but at any given time, only one of them can rent it and the others cannot. Congestion is therefore undesirable because it can prevent potential transactions from materializing. This results in reduced social welfare-to the detriment of users, suppliers, and the platform itself.Market platforms should therefore seek to reduce congestion in the online markets they enable. The usual economic story is that prices serve as the primary means for combating congestion. Prices help by coordinating the revealed demand of users: in our example, if the attractive vacation home is priced sufficiently high, then this ensures that only one user (who values it most, relative to other properties) will choose it; similarly, if other items are also priced correctly in relation to user valuations, then prices can fully decongest the market and do so in a way that optimizes user welfare. A seminal result by Shapley and Shubik [22] shows how to efficiently compute these so called competitive-equilibrium prices that balance supply and demand. This approach is ill-suited for modern markets for two key reasons. First, platforms do not typically control prices-since these are typically set in a decentralized fashion by sellers, rather than by the platform itself. Second, and more subtly, equilibrium prices provide guarantees only when users choose rationally and under full information. In practice, consumers must make choices under partial information about products, due to restricted screen space, bounded cognitive capacity, limited attention spans and impatience-all of which place natural constraints on the amount of information consumed. This second point, which we argue is inherent to modern markets, will be key to our approach. | Decongestion by Representation: Learning to Improve Economic Welfare in Marketplaces |
d32654687 | DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a linear model. Yet they are used on multi-layer networks with millions of parameters. This is a cause for concern since linear models are simple neural networks. We argue that explanation methods for neural nets should work reliably in the limit of simplicity, the linear models. Based on our analysis of linear models we propose a generalization that yields two explanation techniques (PatternNet and PatternAttribution) that are theoretically sound for linear models and produce improved explanations for deep networks. | LEARNING HOW TO EXPLAIN NEURAL NETWORKS: PATTERNNET AND PATTERNATTRIBUTION |
d209315300 | Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O(L 2 ) to O(L log L), where L is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences.Published as a conference paper at ICLR 2020We introduce the Reformer model which solves these problems using the following techniques:• Reversible layers, first introduced in Gomez et al.(2017), enable storing only a single copy of activations in the whole model, so the N factor disappears. • Splitting activations inside feed-forward layers and processing them in chunks removes the d f f factor and saves memory inside feed-forward layers. | REFORMER: THE EFFICIENT TRANSFORMER |
d264172720 | In this paper, we present TOSS, which introduces text to the task of novel view synthesis (NVS) from just a single RGB image. While Zero123 has demonstrated impressive zero-shot open-set NVS capability, it treats NVS as a pure image-toimage translation problem. This approach suffers from the challengingly underconstrained nature of single-view NVS: the process lacks means of explicit user control and often results in implausible NVS generations. To address this limitation, TOSS uses text as high-level semantic information to constrain the NVS solution space. TOSS fine-tunes text-to-image Stable Diffusion pre-trained on large-scale text-image pairs and introduces modules specifically tailored to image and camera pose conditioning, as well as dedicated training for pose correctness and preservation of fine details. Comprehensive experiments are conducted with results showing that our proposed TOSS outperforms Zero123 with more plausible, controllable and multiview-consistent NVS results. We further support these results with comprehensive ablations that underscore the effectiveness and potential of the introduced semantic guidance and architecture design. See https://toss3d.github.io/ for more results. * Equal contribution. † Work done during an internship at IDEA. | TOSS: HIGH-QUALITY TEXT-GUIDED NOVEL VIEW SYNTHESIS FROM A SINGLE IMAGE |
d238744320 | Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limited. Although it is often established that the average accuracy for the entire population of data is improved, it is unclear how SSL fares with different sub-populations. Understanding the above question has substantial fairness implications when different sub-populations are defined by the demographic groups that we aim to treat fairly. In this paper, we reveal the disparate impacts of deploying SSL: the sub-population who has a higher baseline accuracy without using SSL (the "rich" one) tends to benefit more from SSL; while the sub-population who suffers from a low baseline accuracy (the "poor" one) might even observe a performance drop after adding the SSL module. We theoretically and empirically establish the above observation for a broad family of SSL algorithms, which either explicitly or implicitly use an auxiliary "pseudo-label". Experiments on a set of image and text classification tasks confirm our claims. We introduce a new metric, Benefit Ratio, and promote the evaluation of the fairness of SSL (Equalized Benefit Ratio). We further discuss how the disparate impact can be mitigated. We hope our paper will alarm the potential pitfall of using SSL and encourage a multifaceted evaluation of future SSL algorithms. * Equal contributions. Corresponding author: Yang Liu <yangliu@ucsc.edu>. | THE RICH GET RICHER: DISPARATE IMPACT OF SEMI-SUPERVISED LEARNING |
d252846166 | Discovering causal relations from observational data becomes possible with additional assumptions such as considering the functional relations to be constrained as nonlinear with additive noise (ANM). Even with strong assumptions, causal discovery involves an expensive search problem over the space of directed acyclic graphs (DAGs). Topological ordering approaches reduce the optimisation space of causal discovery by searching over a permutation rather than graph space. For ANMs, the Hessian of the data log-likelihood can be used for finding leaf nodes in a causal graph, allowing its topological ordering. However, existing computational methods for obtaining the Hessian still do not scale as the number of variables and the number of samples are increased. Therefore, inspired by recent innovations in diffusion probabilistic models (DPMs), we propose DiffAN 1 , a topological ordering algorithm that leverages DPMs for learning a Hessian function. We introduce theory for updating the learned Hessian without re-training the neural network, and we show that computing with a subset of samples gives an accurate approximation of the ordering, which allows scaling to datasets with more samples and variables. We show empirically that our method scales exceptionally well to datasets with up to 500 nodes and up to 10 5 samples while still performing on par over small datasets with state-of-the-art causal discovery methods. * pedro.sanchez@ed.ac.uk 1 Implementation is available at https://github.com/vios-s/DiffAN . | DIFFUSION MODELS FOR CAUSAL DISCOVERY VIA TOPOLOGICAL ORDERING |
d3947794 | Deep neural network classifiers are vulnerable to small input perturbations carefully generated by the adversaries. Injecting adversarial inputs during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks. To address this challenge, we propose to utilize embedding space for both classification and low-level (pixel-level) similarity learning to ignore unknown pixel level perturbation. During training, we inject adversarial images without replacing their corresponding clean images and penalize the distance between the two embeddings (clean and adversarial). This additional regularization encourages two similar images (clean and perturbed versions) to produce the same outputs, not necessarily the true labels, enhancing classifier's robustness against pixel level perturbation. Next, we show iteratively generated adversarial images easily transfer between networks trained with the same strategy. Inspired by this observation, we also propose cascade adversarial training, which transfers the knowledge of the end results of adversarial training. We train a network from scratch by injecting iteratively generated adversarial images crafted from already defended networks in addition to one-step adversarial images from the network being trained. Experimental results show that cascade adversarial training together with our proposed low-level similarity learning efficiently enhance the robustness against iterative attacks, but at the expense of decreased robustness against one-step attacks. We show that combining those two techniques can also improve robustness under the worst case black box attack scenario. | Cascade Adversarial Machine Learning Regularized with a Unified Embedding |
d257532315 | Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. As a side product, the pre-trained geometric modeling networks could bring further improvement to the depth and odometry estimation tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. | POLICY PRE-TRAINING FOR AUTONOMOUS DRIVING VIA SELF-SUPERVISED GEOMETRIC MODELING |
d252917826 | Proteins are macromolecules that perform essential functions in all living organisms.Designing novel proteins with specific structures and desired functions has been a long-standing challenge in the field of bioengineering.Existing approaches generate both protein sequence and structure using either autoregressive models or diffusion models, both of which suffer from high inference costs.In this paper, we propose a new approach capable of protein sequence and structure co-design, which iteratively translates both protein sequence and structure into the desired state from random initialization, based on context features given a priori.Our model consists of a trigonometry-aware encoder that reasons geometrical constraints and interactions from context features, and a roto-translation equivariant decoder that translates protein sequence and structure interdependently.Notably, all protein amino acids are updated in one shot in each translation step, which significantly accelerates the inference process.Experimental results across multiple tasks show that our model outperforms previous state-of-the-art baselines by a large margin, and is able to design proteins of high fidelity as regards both sequence and structure, with running time orders of magnitude less than sampling-based methods. | PROTEIN SEQUENCE AND STRUCTURE CO-DESIGN WITH EQUIVARIANT TRANSLATION |
d263606194 | In this work, we consider the optimization process of minibatch stochastic gradient descent (SGD) on a 2-layer neural network with data separated by a quadratic ground truth function. We prove that with data drawn from the d-dimensional Boolean hypercube labeled by the quadratic "XOR" function y = −xixj, it is possible to train to a population error o(1) with dpolylog(d) samples. Our result considers simultaneously training both layers of the two-layer-neural network with ReLU activations via standard minibatch SGD on the logistic loss. To our knowledge, this work is the first to give a sample complexity ofÕ(d) for efficiently learning the XOR function on isotropic data on a standard neural network with standard training. Our main technique is showing that the network evolves in two phases: a signal-finding phase where the network is small and many of the neurons evolve independently to find features, and a signal-heavy phase, where SGD maintains and balances the features. We leverage the simultaneous training of the layers to show that it is sufficient for only a small fraction of the neurons to learn features, since those neurons will be amplified by the simultaneous growth of their second layer weights. | SGD Finds then Tunes Features in Two-Layer Neural Networks with Near-Optimal Sample Complexity: A Case Study in the XOR problem |
d215737267 | This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called 'Active Neural SLAM'. Our approach leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned SLAM module, and global and local policies. The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies). Such use of learning within each module retains its benefits, while at the same time, hierarchical decomposition and modular training allow us to sidestep the high sample complexities associated with training end-to-end policies. Our experiments in visually and physically realistic simulated 3D environments demonstrate the effectiveness of our approach over past learning and geometry-based approaches. The proposed model can also be easily transferred to the PointGoal task and was the winning entry of the CVPR 2019 Habitat PointGoal Navigation Challenge.Recent work from Chen et al. (2019) has used end-to-end learning to tackle this problem. Their motivation is three-fold: a) learning provides flexibility to the choice of input modalities (classical systems rely on observing geometry through the use of specialized sensors, while learning systems can infer geometry directly from RGB images), b) use of learning can improve robustness to errors in explicit state estimation, and c) learning can effectively leverage structural regularities of the real world, leading to more efficient behavior in previously unseen environments. This lead to their design of an end-to-end trained neural network-based policy that processed raw sensory observations to directly output actions that the agent should execute.While the use of learning for exploration is well-motivated, casting the exploration problem as an end-to-end learning problem has its own drawbacks. Learning about mapping, state-estimation, and path-planning purely from data in an end-to-end manner can be prohibitively expensive. Consequently, past end-to-end learning work for exploration from Chen et al. (2019) relies on the use of imitation learning and many millions of frames of experience, but still performs worse than classical methods that don't require any training at all. This motivates our work. In this paper, we investigate alternate formulations of employing learning for exploration that retains the advantages that learning has to offer, but doesn't suffer from the † Correspondence: | LEARNING TO EXPLORE USING ACTIVE NEURAL SLAM |
d11245315 | Dropout, a simple and effective way to train deep neural networks, has led to a number of impressive empirical successes and spawned many recent theoretical investigations. However, the gap between dropout's training and inference phases, introduced due to tractability considerations, has largely remained under-appreciated. In this work, we first formulate dropout as a tractable approximation of some latent variable model, leading to a clean view of parameter sharing and enabling further theoretical analysis. Then, we introduce (approximate) expectation-linear dropout neural networks, whose inference gap we are able to formally characterize. Algorithmically, we show that our proposed measure of the inference gap can be used to regularize the standard dropout training objective, resulting in an explicit control of the gap. Our method is as simple and efficient as standard dropout. We further prove the upper bounds on the loss in accuracy due to expectation-linearization, describe classes of input distributions that expectation-linearize easily. Experiments on three image classification benchmark datasets demonstrate that reducing the inference gap can indeed improve the performance consistently. | DROPOUT WITH EXPECTATION-LINEAR REGULARIZATION |
d238419331 | Current dense text retrieval models face two typical challenges. First, they adopt a siamese dual-encoder architecture to encode queries and documents independently for fast indexing and searching, while neglecting the finer-grained termwise interactions. This results in a sub-optimal recall performance. Second, their model training highly relies on a negative sampling technique to build up the negative documents in their contrastive losses. To address these challenges, we present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker. The two models are jointly optimized according to a minimax adversarial objective: the retriever learns to retrieve negative documents to cheat the ranker, while the ranker learns to rank a collection of candidates including both the ground-truth and the retrieved ones, as well as providing progressive direct feedback to the dual-encoder retriever. Through this adversarial game, the retriever gradually produces harder negative documents to train a better ranker, whereas the cross-encoder ranker provides progressive feedback to improve retriever. We evaluate AR2 on three benchmarks. Experimental results show that AR2 consistently and significantly outperforms existing dense retriever methods and achieves new state-of-the-art results on all of them. This includes the improvements on Natural Questions R@5 to 77.9% (+2.1%), TriviaQA R@5 to 78.2% (+1.4%), and MS-MARCO MRR@10 to 39.5% (+1.3%). Code and models are available at https://github.com/microsoft/AR2.Recent studies on contrastive learning (Xiong et al., 2021;Karpukhin et al., 2020)show that the iterative "hard-negative" sampling technique can significantly improve the performance compared with "random-negative" sampling approach, as it can pick more representative negative samples to * Work is done during internship at Microsoft Research Asia. . Training deep nets with sublinear memory cost. arXiv preprint arXiv:1604.06174, 2016.Zhuyun Dai and Jamie Callan. Deeper text understanding for ir with contextual neural language modeling. In SIGIR, 2019.Luyu Gao and Jamie Callan. Unsupervised corpus aware language model pre-training for dense passage retrieval. arXiv preprint arXiv:2108.05540, 2021a.Luyu Gao and Jamie Callan. Is your language model ready for dense representation fine-tuning? arXiv preprint arXiv:2104.08253, 2021b.Luyu Gao, Zhuyun Dai, and Jamie Callan. Modularized transfomer-based ranking framework. In EMNLP, 2020.Luyu Gao, Zhuyun Dai, and Jamie Callan. COIL: revisit exact lexical match in information retrieval with contextualized inverted list. In NAACL-HLT, 2021. | ADVERSARIAL RETRIEVER-RANKER FOR DENSE TEXT RETRIEVAL |
d119304640 | Figure 1. Our method extracts a character from an uncontrolled video and enables us to control its motion. The pose of the character, shown in the first row, is created by our Pose2Pose network in an autoregressive way, so that the motion matches the control signal illustrated by the joystick. The second row depicts the character's appearance, as generated by the Pose2Frame network, which also generates the masks shown in the third row. The final frame (last row) blends a given background and the generated frames, in accordance with these masks.AbstractWe are given a video of a person performing a certain activity, from which we extract a controllable model. The model generates novel image sequences of that person, according to arbitrary user-defined control signals, typically marking the displacement of the moving body. The generated video can have an arbitrary background, and effectively capture both the dynamics and appearance of the person.The method is based on two networks. The first network maps a current pose, and a single-instance control signal to the next pose. The second network maps the current pose, the new pose, and a given background, to an output frame. Both networks include multiple novelties that enable high-quality performance. This is demonstrated on multiple characters extracted from various videos of dancers and athletes. | Vid2Game: Controllable Characters Extracted from Real-World Videos |
d223953594 | In recent deep image compression neural networks, the entropy model plays a critical role in estimating the prior distribution of deep image encodings. Existing methods combine hyperprior with local context in the entropy estimation function. This greatly limits their performance due to the absence of a global vision. In this work, we propose a novel Global Reference Model for image compression to effectively leverage both the local and the global context information, leading to an enhanced compression rate. The proposed method scans decoded latents and then finds the most relevant latent to assist the distribution estimating of the current latent. A by-product of this work is the innovation of a mean-shifting GDN module that further improves the performance. Experimental results demonstrate that the proposed model outperforms the rate-distortion performance of most of the state-of-the-art methods in the industry. Code is available at https://github.com/damo-cv/imgcomp-reference. * Corresponding author Figure 1: Global spatial redundancy in the image. For standard codecs and previous learned codecs, non-local relevant patches (marked by yellow and blue) would consume equal bit rates. arXiv:2010.08321v3 [eess.IV] 5 Jan 2022 | LEARNING ACCURATE ENTROPY MODEL WITH GLOBAL REFERENCE FOR IMAGE COMPRESSION |
d252907682 | A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e.g., SimCLR, VICReg, SwAV, MSN). We show that in the formulation of all these methods is an overlooked prior to learn features that enable uniform clustering of the data. While this prior has led to remarkably semantic representations when pretraining on class-balanced data, such as ImageNet, we demonstrate that it can hamper performance when pretraining on class-imbalanced data. By moving away from conventional uniformity priors and instead preferring power-law distributed feature clusters, we show that one can improve the quality of the learned representations on real-world class-imbalanced datasets. To demonstrate this, we develop an extension of the Masked Siamese Networks (MSN) method to support the use of arbitrary features priors. . Self-labelling via simultaneous clustering and representation learning. arXiv preprint arXiv:1911.05371, 2019.Mahmoud Assran, Nicolas Ballas, Lluis Castrejon, and Michael Rabbat. Supervision accelerates pre-training in contrastive semi-supervised learning of visual representations. arXiv preprint arXiv-supervised learning of visual features by non-parametrically predicting view assignments with support samples. arXiv preprint arXiv:. Data2vec: A general framework for self-supervised learning in speech, vision and language. arXiv preprint arXiv:2202.03555, 2022.Randall Balestriero and Yann LeCun. Contrastive and non-contrastive self-supervised learning recover global and local spectral embedding methods. arXiv preprint arXiv:2205.11508, 2022. | THE HIDDEN UNIFORM CLUSTER PRIOR IN SELF-SUPERVISED LEARNING |
d600040 | Program translation is an important tool to migrate legacy code in one language into an ecosystem built in a different language. In this work, we are the first to employ deep neural networks toward tackling this problem. We observe that program translation is a modular procedure, in which a sub-tree of the source tree is translated into the corresponding target sub-tree at each step. To capture this intuition, we design a tree-to-tree neural network to translate a source tree into a target one. Meanwhile, we develop an attention mechanism for the tree-to-tree model, so that when the decoder expands one non-terminal in the target tree, the attention mechanism locates the corresponding sub-tree in the source tree to guide the expansion of the decoder. We evaluate the program translation capability of our tree-to-tree model against several state-of-the-art approaches. Compared against other neural translation models, we observe that our approach is consistently better than the baselines with a margin of up to 15 points. Further, our approach can improve the previous state-of-the-art program translation approaches by a margin of 20 points on the translation of real-world projects. | Tree-to-tree Neural Networks for Program Translation |
d219708801 | Traditional off-policy actor-critic Reinforcement Learning (RL) algorithms learn value functions of a single target policy. However, when value functions are updated to track the learned policy, they forget potentially useful information about old policies. We introduce a class of value functions called Parameter-Based Value Functions (PBVFs) whose inputs include the policy parameters. They can generalize across different policies. PBVFs can evaluate the performance of any policy given a state, a state-action pair, or a distribution over the RL agent's initial states. First we show how PBVFs yield novel off-policy policy gradient theorems. Then we derive off-policy actor-critic algorithms based on PBVFs trained by Monte Carlo or Temporal Difference methods. We show how learned PBVFs can zeroshot learn new policies that outperform any policy seen during training. Finally our algorithms are evaluated on a selection of discrete and continuous control tasks using shallow policies and deep neural networks. Their performance is comparable to state-of-the-art methods. arXiv:2006.09226v4 [cs.LG] 13 Aug 2021 Published as a conference paper at ICLR 2021 1 With tabular policies, dropping this term still results in a convergent algorithm (Degris et al., 2012). 2 In the original formulation of Silver et al. (2014) d π b∞ (s) is replaced by d π b (s). | PARAMETER-BASED VALUE FUNCTIONS |
d259887064 | Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised setting, when most training labels are unavailable. We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process. We use training consistency as a surrogate function and propose a consistency ranking loss for confidence estimation. On both image classification and segmentation tasks, our method achieves state-of-the-art performances in confidence estimation. Furthermore, we show the benefit of the proposed method through a downstream active learning task.Published as a conference paper at ICLR 2023 dence learning. For data without labels, our idea is to use the consistency of the predictions through the training process. An initial investigation suggests that consistency of predictions tends to be correlated with sample confidence on both labeled and unlabeled data.Having established training consistency as an approximation of confidence, the next challenge is that the consistency can only be evaluated on data available during training. To this end, we propose to re-calibrate model's prediction by aligning it with the consistency. In particular, we propose a novel Consistency Ranking Loss to regulate the model's output after the softmax layer so it has a similar ranking of model confidence output as the ranking of the consistency. After the re-calibration, we expect the model's output correctly accounts for its confidence on test samples. We both theoretically and empirically validate the effectiveness of the proposed Consistency Ranking Loss. Specifically, we show the superiority of our method on real applications, such as image classification and medical image segmentation, under semi-supervised settings. We also demonstrate the benefit of our method through active learning tasks. | CONFIDENCE ESTIMATION USING UNLABELED DATA |
d256697328 | This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to explain the task correctly and misguide the agent in unseen environments due to the intrinsic covariate shift. Leveraging both expert data and lower-quality diverse data, we devise a principled algorithm (namely CLARE) that solves offline IRL efficiently via integrating "conservatism" into a learned reward function and utilizing an estimated dynamics model. Our theoretical analysis provides an upper bound on the return gap between the learned policy and the expert policy, based on which we characterize the impact of covariate shift by examining subtle two-tier tradeoffs between the "exploitation" (on both expert and diverse data) and "exploration" (on the estimated dynamics model). We show that CLARE can provably alleviate the reward extrapolation error by striking the right "exploitation-exploration" balance therein. Extensive experiments corroborate the significant performance gains of CLARE over existing state-of-the-art algorithms on MuJoCo continuous control tasks (especially with a small offline dataset), and the learned reward is highly instructive for further learning (source code). . Model-based offline metareinforcement learning with regularization. In Proc. ICLR, 2021.Andrew Y Ng, Stuart J Russell, et al. Algorithms for inverse reinforcement learning. In | CLARE: CONSERVATIVE MODEL-BASED REWARD LEARNING FOR OFFLINE INVERSE REINFORCEMENT LEARNING |
d257078858 | Intelligent systems deployed in the real world suffer from catastrophic forgetting when exposed to a sequence of tasks. Humans, on the other hand, acquire, consolidate, and transfer knowledge between tasks that rarely interfere with the consolidated knowledge. Accompanied by self-regulated neurogenesis, continual learning in the brain is governed by a rich set of neurophysiological processes that harbor different types of knowledge, which are then integrated by conscious processing. Thus, inspired by the Global Workspace Theory of conscious information access in the brain, we propose TAMiL, a continual learning method that entails task-attention modules to capture task-specific information from the common representation space. We employ simple, undercomplete autoencoders to create a communication bottleneck between the common representation space and the global workspace, allowing only the task-relevant information to the global workspace, thus greatly reducing task interference. Experimental results show that our method outperforms state-of-the-art rehearsal-based and dynamic sparse approaches and bridges the gap between fixed capacity and parameter isolation approaches while being scalable. We also show that our method effectively mitigates catastrophic forgetting while being well-calibrated with reduced taskrecency bias 1 .Published as a conference paper at ICLR 2023 common representation space of fixed capacity from which information is selected, maintained, and shared with the rest of the brain. When addressing the current task, the attention mechanism creates a communication bottleneck between the common representation space and the global workspace and admits only relevant information in the global workspace (Goyal & Bengio, 2020). Such a system enables efficient CL in humans with systematic generalization across tasks(Bengio, 2017).Several approaches have been proposed in the literature that mimic one or more neurophysiological processes in the brain to address catastrophic forgetting in DNN. Experience rehearsal (Ratcliff, 1990) is one of the most prominent approaches that mimics the association of past and present experiences in the brain. However, the performance of rehearsal-based approaches is poor under low buffer regimes, as it is commensurate with the buffer size(Bhat et al., 2022a). On the other hand, parameter isolation methods (Rusu et al., 2016) present an extreme case of neurogenesis in which a new subnetwork is initialized for each task, thus greatly reducing task interference. Nevertheless, these approaches exhibit poor reusability of parameters and are not scalable due to the addition of a large number of parameters per task. Therefore, the right combination of the aforementioned mechanisms governed by GWT could unlock effective CL in DNNs while simultaneously encouraging reusability and mitigating catastrophic forgetting. | TASK-AWARE INFORMATION ROUTING FROM COMMON REPRESENTATION SPACE IN LIFELONG LEARNING |
d245005650 | Despite overparameterization, deep networks trained via supervised learning are easy to optimize and exhibit excellent generalization. One hypothesis to explain this is that overparameterized deep networks enjoy the benefits of implicit regularization induced by stochastic gradient descent, which favors parsimonious solutions that generalize well on test inputs. It is reasonable to surmise that deep reinforcement learning (RL) methods could also benefit from this effect. In this paper, we discuss how the implicit regularization effect of SGD seen in supervised learning could in fact be harmful in the offline deep RL setting, leading to poor generalization and degenerate feature representations. Our theoretical analysis shows that when existing models of implicit regularization are applied to temporal difference learning, the resulting derived regularizer favors degenerate solutions with excessive "aliasing", in stark contrast to the supervised learning case. We back up these findings empirically, showing that feature representations learned by a deep network value function trained via bootstrapping can indeed become degenerate, aliasing the representations for state-action pairs that appear on either side of the Bellman backup. To address this issue, we derive the form of this implicit regularizer and, inspired by this derivation, propose a simple and effective explicit regularizer, called DR3, that counteracts the undesirable effects of this implicit regularizer. When combined with existing offline RL methods, DR3 substantially improves performance and stability, alleviating unlearning in Atari 2600 games, D4RL domains and robotic manipulation from images. | DR3: VALUE-BASED DEEP REINFORCEMENT LEARNING REQUIRES EXPLICIT REGULARIZATION |
d263311025 | Pretrained language models sometimes possess knowledge that we do not wish them to, including memorized personal information and knowledge that could be used to harm people. They can also output toxic or harmful text. To mitigate these safety and informational issues, we propose an attack-and-defense framework for studying the task of deleting sensitive information directly from model weights. We study direct edits to model weights because (1) this approach should guarantee that particular deleted information is never extracted by future prompt attacks, and (2) it should protect against whitebox attacks, which is necessary for making claims about safety/privacy in a setting where publicly available model weights could be used to elicit sensitive information. Our threat model assumes that an attack succeeds if the answer to a sensitive question is located among a set of B generated candidates, based on scenarios where the information would be insecure if the answer is among B candidates. Experimentally, we show that even state-of-the-art model editing methods such as ROME struggle to truly delete factual information from models like GPT-J, as our whitebox and blackbox attacks can recover "deleted" information from an edited model 38% of the time. These attacks leverage two key observations: (1) that traces of deleted information can be found in intermediate model hidden states, and (2) that applying an editing method for one question may not delete information across rephrased versions of the question. Finally, we provide new defense methods that protect against some extraction attacks, but we do not find a single universally effective defense method. Our results suggest that truly deleting sensitive information is a tractable but difficult problem, since even relatively low attack success rates have potentially severe implications for the deployment of language models in a world where individuals enjoy ownership of their personal data, a right to privacy, and safety from harmful model outputs. 1 * Equal contribution. 1 Our code is available at: https://github.com/Vaidehi99/InfoDeletionAttacks arXiv:2309.17410v1 [cs.CL] 29 Sep 2023• How can we "delete" specific sensitive information from language models when we do not want models to know or express this information?• How do we test whether that specific information was successfully deleted?Language ModelLanguage Model | CAN SENSITIVE INFORMATION BE DELETED FROM LLMS? OBJECTIVES FOR DEFENDING AGAINST EXTRACTION ATTACKS |
d258437253 | We introduce Robust Exploration via Clusteringbased Online Density Estimation (RECODE), a nonparametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space. By adapting classical clustering to the nonstationary setting of Deep RL, RECODE can efficiently track state visitation counts over thousands of episodes. We further propose a novel generalization of the inverse dynamics loss, which leverages masked transformer architectures for multi-step prediction; which in conjunction with RECODE achieves a new state-of-the-art in a suite of challenging 3D-exploration tasks in DM-HARD-8. RECODE also sets new state-of-theart in hard exploration Atari games, and is the first agent to reach the end screen in Pitfall! | Unlocking the Power of Representations in Long-term Novelty-based Exploration |
d264172845 | Invariance and equivariance to geometrical transformations have proven to be very useful inductive biases when training (convolutional) neural network models, especially in the low-data regime. Much work has focused on the case where the symmetry group employed is compact or abelian, or both. Recent work has explored enlarging the class of transformations used to the case of Lie groups, principally through the use of their Lie algebra, as well as the group exponential and logarithm maps. The applicability of such methods to larger transformation groups is limited by the fact that depending on the group of interest G, the exponential map may not be surjective. Further limitations are encountered when G is neither compact nor abelian. Using the structure and geometry of Lie groups and their homogeneous spaces, we present a framework by which it is possible to work with such groups primarily focusing on the Lie groups G = GL + (n, R) and G = SL(n, R), as well as their representation as affine transformations R n ⋊ G. Invariant integration as well as a global parametrization is realized by decomposing the 'larger' groups into subgroups and submanifolds which can be handled individually. Under this framework, we show how convolution kernels can be parametrized to build models equivariant with respect to affine transformations. We evaluate the robustness and out-of-distribution generalisation capability of our model on the standard affine-invariant benchmark classification task, where we outperform all previous equivariant models as well as all Capsule Network proposals. . Geometric means in a novel vector space structure on symmetric positive-definite matrices. SIAM journal on matrix analysis and applications, 29(1): 328-347, 2007. | LIE GROUP DECOMPOSITIONS FOR EQUIVARIANT NEURAL NETWORKS |
d260866107 | We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approach, named instruction backtranslation, starts with a language model finetuned on a small amount of seed data, and a given web corpus. The seed model is used to construct training examples by generating instruction prompts for web documents (self-augmentation), and then selecting high quality examples from among these candidates (self-curation). This data is then used to finetune a stronger model. Finetuning LLaMa on two iterations of our approach yields a model that outperforms all other LLaMa-based models on the Alpaca leaderboard not relying on distillation data, demonstrating highly effective self-alignment. | Self-Alignment with Instruction Backtranslation |
d53483457 | Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize that this scheme of writing is suboptimal in memory utilization and introduces redundant computation. To validate our hypothesis, we derive a theoretical bound on the amount of information stored in a RAM-like system and formulate an optimization problem that maximizes the bound. The proposed solution dubbed Uniform Writing is proved to be optimal under the assumption of equal timestep contributions. To relax this assumption, we introduce modifications to the original solution, resulting in a solution termed Cached Uniform Writing. This method aims to balance between maximizing memorization and forgetting via overwriting mechanisms. Through an extensive set of experiments, we empirically demonstrate the advantages of our solutions over other recurrent architectures, claiming the state-of-the-arts in various sequential modeling tasks.paper presents a new approach towards finding optimal operations for MANNs that serve the purpose of learning longer sequences with finite memory. | Learning to Remember More with Less Memorization |
d238634219 | Differentially Private (DP) learning has seen limited success for building large deep learning models of text, and straightforward attempts at applying Differentially Private Stochastic Gradient Descent (DP-SGD) to NLP tasks have resulted in large performance drops and high computational overhead. We show that this performance drop can be mitigated with (1) the use of large pretrained language models; (2) non-standard hyperparameters that suit DP optimization; and (3) finetuning objectives which are aligned with the pretraining procedure. With the above, we obtain NLP models that outperform state-of-the-art DP-trained models under the same privacy budget and strong non-private baselines-by directly fine-tuning pretrained models with DP optimization on moderately-sized corpora. To address the computational challenge of running DP-SGD with large Transformers, we propose a memory saving technique that allows clipping in DP-SGD to run without instantiating per-example gradients for any linear layer in the model. The technique enables privately training Transformers with almost the same memory cost as non-private training at a modest run-time overhead. Contrary to conventional wisdom that DP optimization fails at learning high-dimensional models (due to noise that scales with dimension) empirical results reveal that private learning with pretrained language models doesn't tend to suffer from dimension-dependent performance degradation. Code to reproduce results can be found at https: //github.com/lxuechen/private-transformers.arXiv:2110.05679v6 [cs.LG] 10 Nov 2022Published as a conference paper at ICLR 2022Published as a conference paper at ICLR 2022 Jekaterina Novikova, Ondřej Dušek, and Verena Rieser. The e2e dataset: New challenges for end-to-end generation. arXiv preprint arXiv:1706.09254, 2017.Nicolas Papernot and Thomas Steinke. Hyperparameter tuning with renyi differential privacy, 2021. . Training production language models without memorizing user data. arXiv preprint arXiv:2009.10031, 2020.Phillip Rogaway. The moral character of cryptographic work. Cryptology ePrint Archive, 2015. | LARGE LANGUAGE MODELS CAN BE STRONG DIFFERENTIALLY PRIVATE LEARNERS |
d264306111 | This paper reveals that large language models (LLMs), despite being trained solely on textual data, are surprisingly strong encoders for purely visual tasks in the absence of language.Even more intriguingly, this can be achieved by a simple yet previously overlooked strategy -employing a frozen transformer block from pre-trained LLMs as a constituent encoder layer to directly process visual tokens.Our work pushes the boundaries of leveraging LLMs for computer vision tasks, significantly departing from conventional practices that typically necessitate a multi-modal vision-language setup with associated language prompts, inputs, or outputs.We demonstrate that our approach consistently enhances performance across a diverse range of tasks, encompassing pure 2D and 3D visual recognition tasks (e.g., image and point cloud classification), temporal modeling tasks (e.g., action recognition), non-semantic tasks (e.g., motion forecasting), and multi-modal tasks (e.g., 2D/3D visual question answering and image-text retrieval).Such improvements are a general phenomenon, applicable to various types of LLMs (e.g., LLaMA and OPT) and different LLM transformer blocks.We additionally propose the information filtering hypothesis to explain the effectiveness of pre-trained LLMs in visual encoding -the pre-trained LLM transformer blocks discern informative visual tokens and further amplify their effect.This hypothesis is empirically supported by the observation that the feature activation, after training with LLM transformer blocks, exhibits a stronger focus on relevant regions.We hope that our work inspires new perspectives on utilizing LLMs and deepening our understanding of their underlying mechanisms.Code is available at https://github.com/ziqipang/LM4VisualEncoding. | FROZEN TRANSFORMERS IN LANGUAGE MODELS ARE EFFECTIVE VISUAL ENCODER LAYERS |
d257232328 | The success of machine learning relies heavily on massive amounts of data, which are usually generated and stored across a range of diverse and distributed data sources.Decentralized learning has thus been advocated and widely deployed to make efficient use of the distributed datasets, with an extensive focus on supervised learning (SL) problems.Unfortunately, the majority of real-world data are unlabeled and can be highly heterogeneous across sources.In this work, we carefully study decentralized learning with unlabeled data through the lens of self-supervised learning (SSL), specifically contrastive visual representation learning.We study the effectiveness of a range of contrastive learning algorithms under decentralized learning setting, on relatively large-scale datasets including ImageNet-100, MS-COCO, and a new real-world robotic warehouse dataset.Our experiments show that the decentralized SSL (Dec-SSL) approach is robust to the heterogeneity of decentralized datasets, and learns useful representation for object classification, detection, and segmentation tasks, even when combined with the simple and standard decentralized learning algorithm of Federated Averaging (FedAvg).This robustness makes it possible to significantly reduce communication and to reduce the participation ratio of data sources with only minimal drops in performance.Interestingly, using the same amount of data, the representation learned by Dec-SSL can not only perform on par with that learned by centralized SSL which requires communication and excessive data storage costs, but also sometimes outperform representations extracted from decentralized SL which requires extra knowledge about the data labels.Finally, we provide theoretical insights into understanding why data heterogeneity is less of a concern for Dec-SSL objectives, and introduce feature alignment and clustering techniques to develop a new Dec-SSL algorithm that further improves the performance, in the face of highly non-IID data.Our study presents positive evidence to embrace unlabeled data in decentralized learning, and we hope to provide new insights into whether and why decentralized SSL is effective and/or even advantageous. 1 | DOES LEARNING FROM DECENTRALIZED NON-IID UNLABELED DATA BENEFIT FROM SELF SUPERVISION? |
d235313931 | Policy-Space Response Oracles (PSRO) is a general algorithmic framework for learning policies in multiagent systems by interleaving empirical game analysis with deep reinforcement learning (Deep RL). At each iteration, Deep RL is invoked to train a best response to a mixture of opponent policies. The repeated application of Deep RL poses an expensive computational burden as we look to apply this algorithm to more complex domains. We introduce two variations of PSRO designed to reduce the amount of simulation required during Deep RL training. Both algorithms modify how PSRO adds new policies to the empirical game, based on learned responses to a single opponent policy. The first, Mixed-Oracles, transfers knowledge from previous iterations of Deep RL, requiring training only against the opponent's newest policy. The second, Mixed-Opponents, constructs a pure-strategy opponent by mixing existing strategy's action-value estimates, instead of their policies. Learning against a single policy mitigates variance in state outcomes that is induced by an unobserved distribution of opponents. We empirically demonstrate that these algorithms substantially reduce the amount of simulation during training required by PSRO, while producing equivalent or better solutions to the game. | ITERATIVE EMPIRICAL GAME SOLVING VIA SINGLE POLICY BEST RESPONSE |
d202889322 | Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting. However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entropy regularization to prevent policy collapse. As an alternative to policy gradient algorithms, we introduce V-MPO, an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) that performs policy iteration based on a learned statevalue function. We show that V-MPO surpasses previously reported scores for both the Atari-57 and DMLab-30 benchmark suites in the multi-task setting, and does so reliably without importance weighting, entropy regularization, or population-based tuning of hyperparameters. On individual DMLab and Atari levels, the proposed algorithm can achieve scores that are substantially higher than has previously been reported. V-MPO is also applicable to problems with high-dimensional, continuous action spaces, which we demonstrate in the context of learning to control simulated humanoids with 22 degrees of freedom from full state observations and 56 degrees of freedom from pixel observations, as well as example OpenAI Gym tasks where V-MPO achieves substantially higher asymptotic scores than previously reported. * Equal contribution 1 arXiv:1909.12238v1 [cs.AI] . Learning continuous control policies by stochastic value gradients. arXiv preprint, 2015. URL http: //arxiv.org/abs/1510.09142. ray Kavukcuoglu, and Thore Graepel. Human-level performance in 3d multiplayer games with population-based reinforcement learning. Science, 364:859-865, 2019. | V-MPO: ON-POLICY MAXIMUM A POSTERIORI POLICY OPTIMIZATION FOR DISCRETE AND CONTINUOUS CONTROL |
d209444850 | Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks. Against such attacks, adversarial training and its variants stand as the strongest defense to date. Previous studies have pointed out that robust models that have undergone adversarial training tend to produce more salient and interpretable Jacobian matrices than their non-robust counterparts. A natural question is whether a model trained with an objective to produce salient Jacobian can result in better robustness. This paper answers this question with affirmative empirical results. We propose Jacobian Adversarially Regularized Networks (JARN) as a method to optimize the saliency of a classifier's Jacobian by adversarially regularizing the model's Jacobian to resemble natural training images. Image classifiers trained with JARN show improved robust accuracy compared to standard models on the MNIST, SVHN and CIFAR-10 datasets, uncovering a new angle to boost robustness without using adversarial training examples. | JACOBIAN ADVERSARIALLY REGULARIZED NETWORKS FOR ROBUSTNESS |
d260091821 | Despite the remarkable success of diffusion models in image generation, slow sampling remains a persistent issue. To accelerate the sampling process, prior studies have reformulated diffusion sampling as an ODE/SDE and introduced higher-order numerical methods. However, these methods often produce divergence artifacts, especially with a low number of sampling steps, which limits the achievable acceleration. In this paper, we investigate the potential causes of these artifacts and suggest that the small stability regions of these methods could be the principal cause. To address this issue, we propose two novel techniques. The first technique involves the incorporation of Heavy Ball (HB) momentum, a wellknown technique for improving optimization, into existing diffusion numerical methods to expand their stability regions. We also prove that the resulting methods have first-order convergence. The second technique, called Generalized Heavy Ball (GHVB), constructs a new high-order method that offers a variable trade-off between accuracy and artifact suppression. Experimental results show that our techniques are highly effective in reducing artifacts and improving image quality, surpassing state-of-the-art diffusion solvers on both pixel-based and latent-based diffusion models for low-step sampling. Our research provides novel insights into the design of numerical methods for future diffusion work. | Diffusion Sampling with Momentum for Mitigating Divergence Artifacts |
d263829555 | We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control.Due to the highdimensionality of humanoid control as well as the inherent difficulties in reinforcement learning, prior methods have focused on learning skill embeddings for a narrow range of movement styles (e.g.locomotion, game characters) from specialized motion datasets.This limited scope hampers its applicability in complex tasks.Our work closes this gap, significantly increasing the coverage of motion representation space.To achieve this, we first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset.We then create our motion representation by distilling skills directly from the imitator.This is achieved using an encoder-decoder structure with a variational information bottleneck.Additionally, we jointly learn a prior conditioned on proprioception (humanoid's own pose and velocities) to improve model expressiveness and sampling efficiency for downstream tasks.Sampling from the prior, we can generate long, stable, and diverse human motions.Using this latent space for hierarchical RL, we show that our policies solve tasks using natural and realistic human behavior.We demonstrate the effectiveness of our motion representation by solving generative tasks (e.g.strike, terrain traversal) and motion tracking using VR controllers. | UNIVERSAL HUMANOID MOTION REPRESENTATIONS FOR PHYSICS-BASED CONTROL |
d249192258 | Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover its connection to the classic data visualization method, stochastic neighbor embedding (SNE)(Hinton & Roweis, 2002), whose goal is to preserve pairwise distances. From the perspective of preserving neighboring information, SSCL can be viewed as a special case of SNE with the input space pairwise similarities specified by data augmentation. The established correspondence facilitates deeper theoretical understanding of learned features of SSCL, as well as methodological guidelines for practical improvement. Specifically, through the lens of SNE, we provide novel analysis on domain-agnostic augmentations, implicit bias and robustness of learned features. To illustrate the practical advantage, we demonstrate that the modifications from SNE to t-SNE | YOUR CONTRASTIVE LEARNING IS SECRETLY DOING STOCHASTIC NEIGHBOR EMBEDDING |
d252668622 | Traditional analyses of gradient descent show that when the largest eigenvalue of the Hessian, also known as the sharpness S(θ), is bounded by 2/η, training is "stable" and the training loss decreases monotonically. Recent works, however, have observed that this assumption does not hold when training modern neural networks with full batch or large batch gradient descent. Most recently, Cohen et al.[8]observed two important phenomena. The first, dubbed progressive sharpening, is that the sharpness steadily increases throughout training until it reaches the instability cutoff 2/η. The second, dubbed edge of stability, is that the sharpness hovers at 2/η for the remainder of training while the loss continues decreasing, albeit non-monotonically.We demonstrate that, far from being chaotic, the dynamics of gradient descent at the edge of stability can be captured by a cubic Taylor expansion: as the iterates diverge in direction of the top eigenvector of the Hessian due to instability, the cubic term in the local Taylor expansion of the loss function causes the curvature to decrease until stability is restored. This property, which we call self-stabilization, is a general property of gradient descent and explains its behavior at the edge of stability. A key consequence of self-stabilization is that gradient descent at the edge of stability implicitly follows projected gradient descent (PGD) under the constraint S(θ) ≤ 2/η. Our analysis provides precise predictions for the loss, sharpness, and deviation from the PGD trajectory throughout training, which we verify both empirically in a number of standard settings and theoretically under mild conditions. Our analysis uncovers the mechanism for gradient descent's implicit bias towards stability. * Equal contribution 1 arXiv:2209.15594v2 [cs.LG] | Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability |
d67856213 | Efficient audio synthesis is an inherently difficult machine learning task, as human perception is sensitive to both global structure and fine-scale waveform coherence. Autoregressive models, such as WaveNet, model local structure but have slow iterative sampling and lack global latent structure. In contrast, Generative Adversarial Networks (GANs) have global latent conditioning and efficient parallel sampling, but struggle to generate locally-coherent audio waveforms. Herein, we demonstrate that GANs can in fact generate high-fidelity and locally-coherent audio by modeling log magnitudes and instantaneous frequencies with sufficient frequency resolution in the spectral domain. Through extensive empirical investigations on the NSynth dataset, we demonstrate that GANs are able to outperform strong WaveNet baselines on automated and human evaluation metrics, and efficiently generate audio several orders of magnitude faster than their autoregressive counterparts. 1 | GANSYNTH: ADVERSARIAL NEURAL AUDIO SYNTHESIS |
d252715491 | Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition.This paper focuses on temporal variation modeling, which is the common key problem of extensive analysis tasks.Previous methods attempt to accomplish this directly from the 1D time series, which is extremely challenging due to the intricate temporal patterns.Based on the observation of multi-periodicity in time series, we ravel out the complex temporal variations into the multiple intraperiod-and interperiod-variations.To tackle the limitations of 1D time series in representation capability, we extend the analysis of temporal variations into the 2D space by transforming the 1D time series into a set of 2D tensors based on multiple periods.This transformation can embed the intraperiod-and interperiod-variations into the columns and rows of the 2D tensors respectively, making the 2D-variations to be easily modeled by 2D kernels.Technically, we propose the TimesNet with TimesBlock as a task-general backbone for time series analysis.TimesBlock can discover the multi-periodicity adaptively and extract the complex temporal variations from transformed 2D tensors by a parameter-efficient inception block.Our proposed TimesNet achieves consistent state-of-the-art in five mainstream time series analysis tasks, including short-and long-term forecasting, imputation, classification, and anomaly detection. | TIMESNET: TEMPORAL 2D-VARIATION MODELING FOR GENERAL TIME SERIES ANALYSIS |
d263622302 | We rigorously study the joint evolution of training dynamics via stochastic gradient descent (SGD) and the spectra of empirical Hessian and gradient matrices. We prove that in two canonical classification tasks for multi-class high-dimensional mixtures and either 1 or 2-layer neural networks, the SGD trajectory rapidly aligns with emerging low-rank outlier eigenspaces of the Hessian and gradient matrices. Moreover, in multi-layer settings this alignment occurs per layer, with the final layer's outlier eigenspace evolving over the course of training, and exhibiting rank deficiency when the SGD converges to sub-optimal classifiers. This establishes some of the rich predictions that have arisen from extensive numerical studies in the last decade about the spectra of Hessian and information matrices over the course of training in overparametrized networks. | HIGH-DIMENSIONAL SGD ALIGNS WITH EMERGING OUTLIER EIGENSPACES |
d12933888 | A new model for video captioning is developed, using a deep three-dimensional Convolutional Neural Network (C3D) as an encoder for videos and a Recurrent Neural Network (RNN) as a decoder for captions. We consider both "hard" and "soft" attention mechanisms, to adaptively and sequentially focus on different layers of features (levels of feature "abstraction"), as well as local spatiotemporal regions of the feature maps at each layer. The proposed approach is evaluated on three benchmark datasets: YouTube2Text, M-VAD and MSR-VTT. Along with visualizing the results and how the model works, these experiments quantitatively demonstrate the effectiveness of the proposed adaptive spatiotemporal feature abstraction for translating videos to sentences with rich semantics. | ADAPTIVE FEATURE ABSTRACTION FOR TRANSLATING VIDEO TO LANGUAGE |
d56657912 | In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, IMAGENET-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Then we propose a new dataset called IMAGENET-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize. | BENCHMARKING NEURAL NETWORK ROBUSTNESS TO COMMON CORRUPTIONS AND PERTURBATIONS |
d202573010 | We prove the precise scaling, at finite depth and width, for the mean and variance of the neural tangent kernel (NTK) in a randomly initialized ReLU network. The standard deviation is exponential in the ratio of network depth to width. Thus, even in the limit of infinite overparameterization, the NTK is not deterministic if depth and width simultaneously tend to infinity. Moreover, we prove that for such deep and wide networks, the NTK has a non-trivial evolution during training by showing that the mean of its first SGD update is also exponential in the ratio of network depth to width. This is sharp contrast to the regime where depth is fixed and network width is very large. Our results suggest that, unlike relatively shallow and wide networks, deep and wide ReLU networks are capable of learning data-dependent features even in the so-called lazy training regime.(ii) The function space view where the loss L, which is a simple function of the network mappingx → N (x), is minimized over the manifold M N of all functions representable by the architecture of N using gradient descent with respect to a potentially complicated Riemannian metric K N on M N .A remarkable observation of Jacot et. al. in [14] is that K N simplifies dramatically when the network depth d is fixed and its width n tends to infinity. In this setting, by the universal approximation theorem [7, 13], the manifold M N fills out any (reasonable) ambient linear space of functions. The results in[14]then show that the kernel K N in this limit is frozen throughout training to the infinite width limit of its average E [K N ] at initialization, which depends on the depth and non-linearity of N but not on the dataset. This reduction of neural network SGD to kernel gradient descent for a fixed kernel can be viewed as two separate statements. First, at initialization, the distribution of K N converges in the infinite width limit to the delta function on the infinite width limit of its mean E [K N ]. Second, the infinite width limit of SGD dynamics in function space is kernel gradient descent for this limiting mean kernel for any fixed number of SGD iterations. This shows that as long as the loss L is well-behaved with respect to the network outputs N (x) and E [K N ] is non-degenerate in the subspace of function space given by values on inputs from the dataset, SGD for infinitely wide networks will converge with probability 1 to a minimum of the loss. Further, kernel method-based theorems show that even in this infinitely overparameterized regime neural networks will have non-vacuous guarantees on generalization[6].But replacing neural network training by gradient descent for a fixed kernel in function space is also not completely satisfactory for several reasons. First, it shows that no feature learning occurs during training for infinitely wide networks in the sense that the kernel E [K N ] (and hence its associated feature map) is data-independent. In fact, empirically, networks with finite but large width trained with initially large learning rates often outperform NTK predictions at infinite width. One interpretation is that, at finite width, K N evolves through training, learning data-dependent features not captured by the infinite width limit of its mean at initialization. In part for such reasons, it is important to study both empirically and theoretically finite width corrections to K N . Another interpretation is that the specific NTK scaling of weights at initialization[4,5,17,18,19,20]and the implicit small learning rate limit [16] obscure important aspects of SGD dynamics. Second, even in the infinite width limit, although K N is deterministic, it has no simple analytical formula for deep networks, since it is defined via a layer by layer recursion. In particular, the exact dependence, even in the infinite width limit, of K N on network depth is not well understood.Moreover, the joint statistical effects of depth and width on K N in finite size networks remain unclear, and the purpose of this article is to shed light on the simultaneous effects of depth and width on K N for finite but large widths n and any depth d. Our results apply to fully connected ReLU networks at initialization for which we will show:(1) In contrast to the regime in which the depth d is fixed but the width n is large, K N is not approximately deterministic at initialization so long as d/n is bounded away from 0. | FINITE DEPTH AND WIDTH CORRECTIONS TO THE NEURAL TANGENT KERNEL |
d264490431 | Score Distillation Sampling (SDS) has emerged as the de facto approach for textto-content generation in non-image domains.In this paper, we reexamine the SDS process and introduce a straightforward interpretation that demystifies the necessity for large Classifier-Free Guidance (CFG) scales, rooted in the distillation of an undesired noise term.Building upon our interpretation, we propose a novel Noise-Free Score Distillation (NFSD) process, which requires minimal modifications to the original SDS framework.Through this streamlined design, we achieve more effective distillation of pre-trained text-to-image diffusion models while using a nominal CFG scale.This strategic choice allows us to prevent the over-smoothing of results, ensuring that the generated data is both realistic and complies with the desired prompt.To demonstrate the efficacy of NFSD, we provide qualitative examples that compare NFSD and SDS, as well as several other methods. | NOISE-FREE SCORE DISTILLATION |
d6039192 | In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence -both semantic and syntactic -but might face difficulty remembering long-range dependencies. Intuitively, these long-range dependencies are of semantic nature. In contrast, latent topic models are able to capture the global semantic structure of a document but do not account for word ordering. The proposed TopicRNN model integrates the merits of RNNs and latent topic models: it captures local (syntactic) dependencies using an RNN and global (semantic) dependencies using latent topics. Unlike previous work on contextual RNN language modeling, our model is learned endto-end. Empirical results on word prediction show that TopicRNN outperforms existing contextual RNN baselines. In addition, TopicRNN can be used as an unsupervised feature extractor for documents. We do this for sentiment analysis on the IMDB movie review dataset and report an error rate of 6.28%. This is comparable to the state-of-the-art 5.91% resulting from a semi-supervised approach. Finally, TopicRNN also yields sensible topics, making it a useful alternative to document models such as latent Dirichlet allocation.Published as a conference paper at ICLR 2017 this, we often need to consider many preceding words to understand the global semantic meaning of the sentence or document. The ordering of the words usually matters much less in this case.Because they only consider a fixed-size context window of preceding words, traditional n-gram and neural probabilistic language models(Bengio et al., 2003)have difficulties in capturing global semantic information. To overcome this, RNN-based language models(Mikolov et al., 2010;use hidden states to "remember" the history of a word sequence. However, none of these approaches explicitly model the two main properties of language mentioned above, correct syntax and semantic coherence. Previous work by Chelba and Jelinek(2000)andGao et al. (2004)exploit syntactic or semantic parsers to capture long-range dependencies in language.In this paper, we propose TopicRNN, a RNN-based language model that is designed to directly capture long-range semantic dependencies via latent topics. These topics provide context to the RNN. Contextual RNNs have received a lot of attention(Mikolov and Zweig, 2012;Ji et al., 2015;Lin et al., 2015;Ji et al., 2016;Ghosh et al., 2016). However, the models closest to ours are the contextual RNN model proposed byMikolov and Zweig (2012)and its most recent extension to the long-short term memory (LSTM) architecture(Ghosh et al., 2016). These models use pre-trained topic model features as an additional input to the hidden states and/or the output of the RNN. In contrast, TopicRNN does not require pre-trained topic model features and can be learned in an end-to-end fashion. We introduce an automatic way for handling stop words that topic models usually have difficulty dealing with. Under a comparable model size set up, TopicRNN achieves better perplexity scores than the contextual RNN model of Mikolov and Zweig (2012) on the Penn TreeBank dataset 1 . Moreover, TopicRNN can be used as an unsupervised feature extractor for downstream applications. For example, we derive document features of the IMDB movie review dataset using TopicRNN for sentiment classification. We reported an error rate of 6.28%. This is close to the state-of-the-art 5.91%(Miyato et al., 2016)despite that we do not use the labels and adversarial training in the feature extraction stage. | TOPICRNN: A RECURRENT NEURAL NETWORK WITH LONG-RANGE SEMANTIC DEPENDENCY |
d258309378 | The symmetric generalized eigenvalue problem (SGEP) is a fundamental concept in numerical linear algebra. It captures the solution of many classical machine learning problems such as canonical correlation analysis, independent components analysis, partial least squares, linear discriminant analysis, principal components and others. Despite this, most general solvers are prohibitively expensive when dealing with streaming data sets (i.e., minibatches) and research has instead concentrated on finding efficient solutions to specific problem instances. In this work, we develop a game-theoretic formulation of the top-k SGEP whose Nash equilibrium is the set of generalized eigenvectors. We also present a parallelizable algorithm with guaranteed asymptotic convergence to the Nash. Current state-ofthe-art methods require O(d 2 k) runtime complexity per iteration which is prohibitively expensive when the number of dimensions (d) is large. We show how to modify this parallel approach to achieve O(dk) runtime complexity. Empirically we demonstrate that this resulting algorithm is able to solve a variety of SGEP problem instances including a large-scale analysis of neural network activations. * Asterisk denotes equal contribution. † Work done while at DeepMind. | THE SYMMETRIC GENERALIZED EIGENVALUE PROBLEM AS A NASH EQUILIBRIUM |
d235253907 | In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representationbased task distance between the source and target tasks. We show that TAWT is easy to implement, is computationally efficient, requires little hyperparameter tuning, and enjoys non-asymptotic learning-theoretic guarantees. The effectiveness of TAWT is corroborated through extensive experiments with BERT on four sequence tagging tasks in natural language processing (NLP), including part-ofspeech (PoS) tagging, chunking, predicate detection, and named entity recognition (NER). As a byproduct, the proposed representation-based task distance allows one to reason in a theoretically principled way about several critical aspects of cross-task learning, such as the choice of the source data and the impact of fine-tuning. 1Published as a conference paper at ICLR 2022 | WEIGHTED TRAINING FOR CROSS-TASK LEARNING |
d3904215 | We propose a simple extension to the ReLU-family of activation functions that allows them to shift the mean activation across a layer towards zero. Combined with proper weight initialization, this alleviates the need for normalization layers. We explore the training of deep vanilla recurrent neural networks (RNNs) with up to 144 layers, and show that bipolar activation functions help learning in this setting. On the Penn Treebank and Text8 language modeling tasks we obtain competitive results, improving on the best reported results for non-gated networks. 1 | Shifting Mean Activation Towards Zero with Bipolar Activation Functions |
d52878445 | Batch Normalization (BN) improves both convergence and generalization in training neural networks. This work understands these phenomena theoretically. We analyze BN by using a basic block of neural networks, consisting of a kernel layer, a BN layer, and a nonlinear activation function. This basic network helps us understand the impacts of BN in three aspects. First, by viewing BN as an implicit regularizer, BN can be decomposed into population normalization (PN) and gamma decay as an explicit regularization. Second, learning dynamics of BN and the regularization show that training converged with large maximum and effective learning rate. Third, generalization of BN is explored by using statistical mechanics. Experiments demonstrate that BN in convolutional neural networks share the same traits of regularization as the above analyses. * The first three authors contribute equally. Corresponding to pluo. | TOWARDS UNDERSTANDING REGULARIZATION IN BATCH NORMALIZATION |
d228063930 | Despite their recent successes, GAN models for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Historically, additionally employing the VGG-based perceptual loss has helped to overcome this issue, significantly improving the synthesis quality, but at the same time limiting the progress of GAN models for semantic image synthesis. In this work, we propose a novel, simplified GAN model, which needs only adversarial supervision to achieve high quality results. We re-design the discriminator as a semantic segmentation network, directly using the given semantic label maps as the ground truth for training. By providing stronger supervision to the discriminator as well as to the generator through spatially-and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity with better alignment to their input label maps, making the use of the perceptual loss superfluous. Moreover, we enable high-quality multi-modal image synthesis through global and local sampling of a 3D noise tensor injected into the generator, which allows complete or partial image change. We show that images synthesized by our model are more diverse and follow the color and texture distributions of real images more closely. We achieve an average improvement of 6 FID and 5 mIoU points over the state of the art across different datasets using only adversarial supervision. Semantic SPADE (Park et al., 2019) Our model (OASIS), sampled with different noise label map with VGG w/o VGG w/o VGG | YOU ONLY NEED ADVERSARIAL SUPERVISION FOR SEMANTIC IMAGE SYNTHESIS |
d7559933 | Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images. Even though it has recently been shown that GAN training converges, GAN models often end up in local Nash equilibria that are associated with mode collapse or otherwise fail to model the target distribution. We introduce Coulomb GANs, which pose the GAN learning problem as a potential field, where generated samples are attracted to training set samples but repel each other. The discriminator learns a potential field while the generator decreases the energy by moving its samples along the vector (force) field determined by the gradient of the potential field. Through decreasing the energy, the GAN model learns to generate samples according to the whole target distribution and does not only cover some of its modes. We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution. We show the efficacy of Coulomb GANs on LSUN bedrooms, celebA faces and CIFAR-10. For LSUN and celebA, Coulomb GANs set a new state of the art and produce a previously unseen variety of different samples. | Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields |
d202734090 | Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying causes. We propose Recurrent Independent Mechanisms (RIMs), a new recurrent architecture in which multiple groups of recurrent cells operate with nearly independent transition dynamics, communicate only sparingly through the bottleneck of attention, and are only updated at time steps where they are most relevant. We show that this leads to specialization amongst the RIMs, which in turn allows for dramatically improved generalization on tasks where some factors of variation differ systematically between training and evaluation. | RECURRENT INDEPENDENT MECHANISMS |
d252715808 | k-subset sampling is ubiquitous in machine learning, enabling regularization and interpretability through sparsity. The challenge lies in rendering k-subset sampling amenable to end-to-end learning. This has typically involved relaxing the reparameterized samples to allow for backpropagation, with the risk of introducing high bias and high variance. In this work, we fall back to discrete k-subset sampling on the forward pass. This is coupled with using the gradient with respect to the exact marginals, computed efficiently, as a proxy for the true gradient. We show that our gradient estimator, SIMPLE, exhibits lower bias and variance compared to state-of-the-art estimators, including the straight-through Gumbel estimator when k = 1. Empirical results show improved performance on learning to explain and sparse linear regression. We provide an algorithm for computing the exact ELBO for the k-subset distribution, obtaining significantly lower loss compared to SOTA. | SIMPLE: A GRADIENT ESTIMATOR FOR k-SUBSET SAMPLING |
d218581596 | This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of the popular instance-wise contrastive learning. PCL implicitly encodes semantic structures of the data into the learned embedding space, and prevents the network from solely relying on low-level cues for solving unsupervised learning tasks. Specifically, we introduce prototypes as latent variables to help find the maximumlikelihood estimation of the network parameters in an Expectation-Maximization framework. We iteratively perform E-step as finding the distribution of prototypes via clustering and M-step as optimizing the network via contrastive learning. We propose ProtoNCE loss, a generalized version of the InfoNCE loss for contrastive learning by encouraging representations to be closer to their assigned prototypes. PCL achieves state-of-the-art results on multiple unsupervised representation learning benchmarks, with >10% accuracy improvement in low-resource transfer tasks. Our code and pretrained models will be released.Preprint. Under review. | Prototypical Contrastive Learning of Unsupervised Representations |
d252762386 | Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications. In this paper, we introduce a novel analysis of the classical label propagation algorithm (LPA) (Zhu & Ghahramani, 2002) that moreover takes advantage of useful prior information, specifically probabilistic hypothesized labels on the unlabeled data. We provide an error bound that exploits both the local geometric properties of the underlying graph and the quality of the prior information. We also propose a framework to incorporate multiple sources of noisy information. In particular, we consider the setting of weak supervision, where our sources of information are weak labelers. We demonstrate the ability of our approach on multiple benchmark weakly supervised classification tasks, showing improvements upon existing semi-supervised and weakly supervised methods. | LABEL PROPAGATION WITH WEAK SUPERVISION |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.