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d247996741 | Neural network classifiers can largely rely on simple spurious features, such as backgrounds, to make predictions. However, even in these cases, we show that they still often learn core features associated with the desired attributes of the data, contrary to recent findings. Inspired by this insight, we demonstrate that simple last layer retraining can match or outperform state-of-the-art approaches on spurious correlation benchmarks, but with profoundly lower complexity and computational expenses. Moreover, we show that last layer retraining on large ImageNet-trained models can also significantly reduce reliance on background and texture information, improving robustness to covariate shift, after only minutes of training on a single GPU. * Equal contribution. | Published as a conference paper at ICLR 2023 LAST LAYER RE-TRAINING IS SUFFICIENT FOR ROBUSTNESS TO SPURIOUS CORRELATIONS |
d212768111 | This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations. To this end, we propose a multi-modal learning framework that links the inference stage and generation stage. First, the inference networks are trained to match the speaker identity between the two different modalities. Then the trained inference networks cooperate with the generation network by giving conditional information about the voice. The proposed method exploits the recent development of GANs techniques and generates the human face directly from the speech waveform making our system fully end-to-end. We analyze the extent to which the network can naturally disentangle two latent factors that contribute to the generation of a face imageone that comes directly from a speech signal and the other that is not related to itand explore whether the network can learn to generate natural human face image distribution by modeling these factors. Experimental results show that the proposed network can not only match the relationship between the human face and speech, but can also generate the high-quality human face sample conditioned on its speech. Finally, the correlation between the generated face and the corresponding speech is quantitatively measured to analyze the relationship between the two modalities. | Published as a conference paper at ICLR 2020 FROM INFERENCE TO GENERATION: END-TO-END FULLY SELF-SUPERVISED GENERATION OF HUMAN FACE FROM SPEECH |
d252683631 | This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demonstrate that, in certain cases, VI algorithms are equivalent to special cases of GFlowNets in the sense of equality of expected gradients of their learning objectives. We then point out the differences between the two families and show how these differences emerge experimentally. Notably, GFlowNets, which borrow ideas from reinforcement learning, are more amenable than VI to off-policy training without the cost of high gradient variance induced by importance sampling. We argue that this property of GFlowNets can provide advantages for capturing diversity in multimodal target distributions. | Published as a conference paper at ICLR 2023 GFLOWNETS AND VARIATIONAL INFERENCE |
d16894878 | In this paper, we develop a framework for information theoretic learning based on infinitely divisible matrices. We formulate an entropy-like functional on positive definite matrices based on Renyi's axiomatic definition of entropy and examine some key properties of this functional that lead to the concept of infinite divisibility. The proposed formulation avoids the plug in estimation of density and brings along the representation power of reproducing kernel Hilbert spaces. As an application example, we derive a supervised metric learning algorithm using a matrix based analogue to conditional entropy achieving results comparable with the state of the art. | Information Theoretic Learning with Infinitely Divisible Kernels |
d6138149 | Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high resolution images can explain the same downsampled image. Most current single image SR methods use empirical risk minimisation, often with a pixel-wise mean squared error (MSE) loss. However, the outputs from such methods tend to be blurry, over-smoothed and generally appear implausible. A more desirable approach would employ Maximum a Posteriori (MAP) inference, preferring solutions that always have a high probability under the image prior, and thus appear more plausible. Direct MAP estimation for SR is nontrivial, as it requires us to build a model for the image prior from samples. Here we introduce new methods for amortised MAP inference whereby we calculate the MAP estimate directly using a convolutional neural network. We first introduce a novel neural network architecture that performs a projection to the affine subspace of valid SR solutions ensuring that the high resolution output of the network is always consistent with the low resolution input. Using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models. We propose three methods to solve this optimisation problem: (1) Generative Adversarial Networks (GAN) (2) denoiser-guided SR which backpropagates gradient-estimates from denoising to train the network, and (3) a baseline method using a maximum-likelihoodtrained image prior. Our experiments show that the GAN based approach performs best on real image data. Lastly, we establish a connection between GANs and amortised variational inference as in e. g. variational autoencoders. | Published as a conference paper at ICLR 2017 AMORTISED MAP INFERENCE FOR IMAGE SUPER-RESOLUTION |
d219558972 | Designing an incentive compatible auction that maximizes expected revenue is a central problem in Auction Design. While theoretical approaches to the problem have hit some limits, a recent research direction initiated byDuetting et al. (2019)consists in building neural network architectures to find optimal auctions. We propose two conceptual deviations from their approach which result in enhanced performance. First, we use recent results in theoretical auction design to introduce a time-independent Lagrangian. This not only circumvents the need for an expensive hyper-parameter search (as in prior work), but also provides a single metric to compare the performance of two auctions (absent from prior work). Second,the optimization procedure in previous work uses an inner maximization loop to compute optimal misreports. We amortize this process through the introduction of an additional neural network. We demonstrate the effectiveness of our approach by learning competitive or strictly improved auctions compared to prior work. Both results together further imply a novel formulation of Auction Design as a two-player game with stationary utility functions. | Published as a conference paper at ICLR 2021 AUCTION LEARNING AS A TWO-PLAYER GAME |
d235377213 | Evaluating the worst-case performance of a reinforcement learning (RL) agent under the strongest/optimal adversarial perturbations on state observations (within some constraints) is crucial for understanding the robustness of RL agents. However, finding the optimal adversary is challenging, in terms of both whether we can find the optimal attack and how efficiently we can find it. Existing works on adversarial RL either use heuristics-based methods that may not find the strongest adversary, or directly train an RL-based adversary by treating the agent as a part of the environment, which can find the optimal adversary but may become intractable in a large state space. This paper introduces a novel attacking method to find the optimal attacks through collaboration between a designed function named "actor" and an RL-based learner named "director". The actor crafts state perturbations for a given policy perturbation direction, and the director learns to propose the best policy perturbation directions. Our proposed algorithm, PA-AD, is theoretically optimal and significantly more efficient than prior RL-based works in environments with large state spaces. Empirical results show that our proposed PA-AD universally outperforms state-of-the-art attacking methods in various Atari and MuJoCo environments. By applying PA-AD to adversarial training, we achieve state-of-the-art empirical robustness in multiple tasks under strong adversaries. The codebase is released at https://github.com/umd-huang-lab/paad adv rl. | WHO IS THE STRONGEST ENEMY? TOWARDS OPTI- MAL AND EFFICIENT EVASION ATTACKS IN DEEP RL |
d235828753 | Modeling complex phenomena typically involves the use of both discrete and continuous variables. Such a setting applies across a wide range of problems, from identifying trends in time-series data to performing effective compositional scene understanding in images. Here, we propose Hybrid Memoised Wake-Sleep (HMWS), an algorithm for effective inference in such hybrid discrete-continuous models. Prior approaches to learning suffer as they need to perform repeated expensive inner-loop discrete inference. We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importancesampling based approximate inference and marginalization. We evaluate HMWS in the GP-kernel learning and 3D scene understanding domains, and show that it outperforms current state-of-the-art inference methods. | HYBRID MEMOISED WAKE-SLEEP: APPROXIMATE IN- FERENCE AT THE DISCRETE-CONTINUOUS INTERFACE |
d247618647 | Most convergence guarantees for stochastic gradient descent with momentum (SGDm) rely on iid sampling. Yet, SGDm is often used outside this regime, in settings with temporally correlated input samples such as continual learning and reinforcement learning. Existing work has shown that SGDm with a decaying stepsize can converge under Markovian temporal correlation. In this work, we show that SGDm under covariate shift with a fixed step-size can be unstable and diverge. In particular, we show SGDm under covariate shift is a parametric oscillator, and so can suffer from a phenomenon known as resonance. We approximate the learning system as a time varying system of ordinary differential equations, and leverage existing theory to characterize the system's divergence/convergence as resonant/nonresonant modes. The theoretical result is limited to the linear setting with periodic covariate shift, so we empirically supplement this result to show that resonance phenomena persist even under non-periodic covariate shift, nonlinear dynamics with neural networks, and optimizers other than SGDm.Published as a conference paper at ICLR 2022 learning (Xiong et al., 2020). To our knowledge, however, there has been little work on providing convergence rates or guarantees for SGDm under non-iid sampling. In(Doan et al., 2020a), a progress bound is provided for SGDm under Markovian sampling based on mixing time-the time required for a distribution's convergence toward its stationary distribution-along with a convergence rate guarantee under decaying step-sizes. In this work, we assume a fixed step-size, since it is a common choice in the online setting, especially when the practitioner is unsure of mixing rate or stationarity. Sanjeev Arora, Nadav Cohen, Wei Hu, and Yuping Luo. Implicit regularization in deep matrix factorization. Neural Information Processing Systems, 2019. , and Ashia C Wilson. On symplectic optimization. arXiv preprint arXiv:1802.03653, 2018. Petra Csomós and István Faragó. Error analysis of the numerical solution of split differential equations. Mathematical and Computer Modelling, 2008. Sándor Csörgő and László Hatvani. Stability properties of solutions of linear second order differential equations with random coefficients. Journal of Differential Equations, 2010. Jelena Diakonikolas and Lorenzo Orecchia. The approximate duality gap technique: A unified theory of first-order methods. SIAM Journal on Optimization, 2019. Thinh T Doan, Lam M Nguyen, Nhan H Pham, and Justin Romberg. Convergence rates of accelerated markov gradient descent with applications in reinforcement learning. arXiv preprint arXiv:2002.02873, 2020a. | Published as a conference paper at ICLR 2022 RESONANCE IN WEIGHT SPACE: COVARIATE SHIFT CAN DRIVE DIVERGENCE OF SGD WITH MOMENTUM |
d1870512 | In this paper, we explore different ways to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks. By carefully analyzing and understanding the architecture of an RNN, however, we find three points of an RNN which may be made deeper; (1) input-to-hidden function, (2) hidden-tohidden transition and (3) hidden-to-output function. Based on this observation, we propose two novel architectures of a deep RNN which are orthogonal to an earlier attempt of stacking multiple recurrent layers to build a deep RNN(Schmidhuber, 1992;El Hihi and Bengio, 1996). We provide an alternative interpretation of these deep RNNs using a novel framework based on neural operators. The proposed deep RNNs are empirically evaluated on the tasks of polyphonic music prediction and language modeling. The experimental result supports our claim that the proposed deep RNNs benefit from the depth and outperform the conventional, shallow RNNs. | How to Construct Deep Recurrent Neural Networks |
d237592805 | The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery. Deep generative models and combinatorial optimization methods achieve initial success but still struggle with directly modeling discrete chemical structures and often heavily rely on brute-force enumeration. The challenge comes from the discrete and non-differentiable nature of molecule structures. To address this, we propose differentiable scaffolding tree (DST) that utilizes a learned knowledge network to convert discrete chemical structures to locally differentiable ones. DST enables a gradient-based optimization on a chemical graph structure by back-propagating the derivatives from the target properties through a graph neural network (GNN). Our empirical studies show the gradient-based molecular optimizations are both effective and sample efficient. Furthermore, the learned graph parameters can also provide an explanation that helps domain experts understand the model output. | Differentiable Scaffolding Tree for Molecular Optimization |
d247158749 | Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations that preserve labels. This is relatively straightforward for images, but much more challenging for graphs. In this work, we propose GraphAug, a novel automated data augmentation method aiming at computing label-invariant augmentations for graph classification. Instead of using uniform transformations as in existing studies, GraphAug uses an automated augmentation model to avoid compromising critical label-related information of the graph, thereby producing label-invariant augmentations at most times. To ensure label-invariance, we develop a training method based on reinforcement learning to maximize an estimated label-invariance probability. Experiments show that GraphAug outperforms previous graph augmentation methods on various graph classification tasks. | Published as a conference paper at ICLR 2023 AUTOMATED DATA AUGMENTATIONS FOR GRAPH CLASSIFICATION |
d6949717 | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within the pooling region. The approach is hyper-parameter free and can be combined with other regularization approaches, such as dropout and data augmentation. We achieve state-of-the-art performance on four image datasets, relative to other approaches that do not utilize data augmentation.1 | Stochastic Pooling for Regularization of Deep Convolutional Neural Networks |
d214283684 | Research on exploration in reinforcement learning, as applied to Atari 2600 gameplaying, has emphasized tackling difficult exploration problems such as MON-TEZUMA'S REVENGE(Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human average performance on such domains. In this paper we reassess popular bonus-based exploration methods within a common evaluation framework. We combine Rainbow (Hessel et al., 2018) with different exploration bonuses and evaluate its performance on MONTEZUMA'S REVENGE, Bellemare et al.'s set of hard of exploration games with sparse rewards, and the whole Atari 2600 suite. We find that while exploration bonuses lead to higher score on MONTEZUMA'S REVENGE they do not provide meaningful gains over the simpler -greedy scheme. In fact, we find that methods that perform best on that game often underperform -greedy on easy exploration Atari 2600 games. We find that our conclusions remain valid even when hyperparameters are tuned for these easy-exploration games. Finally, we find that none of the methods surveyed benefit from additional training samples (1 billion frames, versus Rainbow's 200 million) on Bellemare et al.'s hard exploration games. Our results suggest that recent gains in MONTEZUMA'S REVENGE may be better attributed to architecture change, rather than better exploration schemes; and that the real pace of progress in exploration research for Atari 2600 games may have been obfuscated by good results on a single domain. , et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529, 2015. | ON BONUS-BASED EXPLORATION METHODS IN THE ARCADE LEARNING ENVIRONMENT |
d257206115 | Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. However, the online model updating of TTA may be unstable and this is often a key obstacle preventing existing TTA methods from being deployed in the real world. Specifically, TTA may fail to improve or even harm the model performance when test data have: 1) mixed distribution shifts, 2) small batch sizes, and 3) online imbalanced label distribution shifts, which are quite common in practice. In this paper, we investigate the unstable reasons and find that the batch norm layer is a crucial factor hindering TTA stability. Conversely, TTA can perform more stably with batch-agnostic norm layers, i.e., group or layer norm. However, we observe that TTA with group and layer norms does not always succeed and still suffers many failure cases. By digging into the failure cases, we find that certain noisy test samples with large gradients may disturb the model adaption and result in collapsed trivial solutions, i.e., assigning the same class label for all samples. To address the above collapse issue, we propose a sharpness-aware and reliable entropy minimization method, called SAR, for further stabilizing TTA from two aspects: 1) remove partial noisy samples with large gradients, 2) encourage model weights to go to a flat minimum so that the model is robust to the remaining noisy samples. Promising results demonstrate that SAR performs more stably over prior methods and is computationally efficient under the above wild test scenarios. The source code is available at https://github.com/mr-eggplant/SAR. | Published as a conference paper at ICLR 2023 TOWARDS STABLE TEST-TIME ADAPTATION IN DYNAMIC WILD WORLD |
d249625698 | This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gradient algorithm.Our contribution is demonstrating the virtues of magnetic mirror descent as both an equilibrium solver and as an approach to reinforcement learning in two-player zero-sum games.These virtues include: 1) Being the first quantal response equilibria solver to achieve linear convergence for extensive-form games with first order feedback; 2) Being the first standard reinforcement learning algorithm to achieve empirically competitive results with CFR in tabular settings; 3) Achieving favorable performance in 3x3 Dark Hex and Phantom Tic-Tac-Toe as a self-play deep reinforcement learning algorithm.˚Equal contribution | A UNIFIED APPROACH TO REINFORCEMENT LEARN-ING, QUANTAL RESPONSE EQUILIBRIA, AND TWO-PLAYER ZERO-SUM GAMES |
d232233726 | We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems. SIR is based on two core ideas: task reduction and self-imitation. Task reduction tackles a hard-to-solve task by actively reducing it to an easier task whose solution is known by the RL agent. Once the original hard task is successfully solved by task reduction, the agent naturally obtains a self-generated solution trajectory to imitate. By continuously collecting and imitating such demonstrations, the agent is able to progressively expand the solved subspace in the entire task space. Experiment results show that SIR can significantly accelerate and improve learning on a variety of challenging sparse-reward continuous-control problems with compositional structures. Code and videos are available at httpsPublished as a conference paper at ICLR 2021 (a) An easy task, which can be completed by a straightforward push of the blue cubic box.(b) A hard task, where a straightforward push fails since the elongated box blocks the door.(c) Task reduction: solve a hard task by reducing it to an easier one. , et al. Language models are few-shot learners. arXiv preprint arXiv:-warshall reinforcement learning learning from past experiences to reach new goals. CoRR, abs/1809.09318, 2018. Tenenbaum. Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. In Advances in neural information processing systems, pp. 3675-3683, 2016.Yann LeCun, Yoshua Bengio, et al. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995, 1995. | Published as a conference paper at ICLR 2021 SOLVING COMPOSITIONAL REINFORCEMENT LEARN- ING PROBLEMS VIA TASK REDUCTION |
d247451199 | Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints. These constraints determine the class of numerical methods that can be applied: computationally expensive but accurate numerical routines are replaced by fast and inaccurate methods, trading inference time for solution accuracy. This paper provides techniques to improve the quality of optimized control policies given a fixed computational budget. We achieve the above via a hypersolvers (Poli et al., 2020a) approach, which hybridizes a differential equation solver and a neural network. The performance is evaluated in direct and receding-horizon optimal control tasks in both low and high dimensions, where the proposed approach shows consistent Pareto improvements in solution accuracy and control performance. | Published as a conference paper at ICLR 2022 NEURAL SOLVERS FOR FAST AND ACCURATE NUMER- ICAL OPTIMAL CONTROL |
d246634966 | We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We show that an agent, after being trained exclusively on the SE, is able to solve the corresponding real environment. While an SE acts as a full proxy to a real environment by learning about its state dynamics and rewards, an RN is a partial proxy that learns to augment or replace rewards. We use bi-level optimization to evolve SEs and RNs: the inner loop trains the RL agent, and the outer loop trains the parameters of the SE / RN via an evolution strategy. We evaluate our proposed new concept on a broad range of RL algorithms and classic control environments. In a one-to-one comparison, learning an SE proxy requires more interactions with the real environment than training agents only on the real environment. However, once such an SE has been learned, we do not need any interactions with the real environment to train new agents. Moreover, the learned SE proxies allow us to train agents with fewer interactions while maintaining the original task performance. Our empirical results suggest that SEs achieve this result by learning informed representations that bias the agents towards relevant states. Moreover, we find that these proxies are robust against hyperparameter variation and can also transfer to unseen agents. | Published as a conference paper at ICLR 2022 LEARNING SYNTHETIC ENVIRONMENTS AND RE- WARD NETWORKS FOR REINFORCEMENT LEARNING |
d214693392 | Recently, there has been a lot of interest in using neural networks for solving partial differential equations. A number of neural network-based partial differential equation solvers have been formulated which provide performances equivalent, and in some cases even superior, to classical solvers. However, these neural solvers, in general, need to be retrained each time the initial conditions or the domain of the partial differential equation changes. In this work, we posit the problem of approximating the solution of a fixed partial differential equation for any arbitrary initial conditions as learning a conditional probability distribution. We demonstrate the utility of our method on Burger's Equation. | Published at the DeepDiffEq workshop under ICLR 2020 LEARNING TO SOLVE DIFFERENTIAL EQUATIONS ACROSS INITIAL CONDITIONS |
d222067132 | We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attentionkernels, Performers use a novel Fast Attention Via positive Orthogonal Random features approach (FAVOR+), which may be of independent interest for scalable kernel methods. FAVOR+ can also be used to efficiently model kernelizable attention mechanisms beyond softmax. This representational power is crucial to accurately compare softmax with other kernels for the first time on large-scale tasks, beyond the reach of regular Transformers, and investigate optimal attention-kernels. Performers are linear architectures fully compatible with regular Transformers and with strong theoretical guarantees: unbiased or nearly-unbiased estimation of the attention matrix, uniform convergence and low estimation variance. We tested Performers on a rich set of tasks stretching from pixel-prediction through text models to protein sequence modeling. We demonstrate competitive results with other examined efficient sparse and dense attention methods, showcasing effectiveness of the novel attention-learning paradigm leveraged by Performers. * Equal contribution. Correspondence to {kchoro,lcolwell}@google.com. Code for Transformer models on protein data can be found in github.com/google-research/ google-research/tree/master/protein_lm and Performer code can be found in github.com/ google-research/google-research/tree/master/performer. Google AI Blog: https:// ai.googleblog.com/2020/10/rethinking-attention-with-performers.html Published as a conference paper at ICLR 2021 layers(Child et al., 2019). Unfortunately, there is a lack of rigorous guarantees for the representation power produced by such methods, and sometimes the validity of sparsity patterns can only be verified empirically through trial and error by constructing special GPU operations (e.g. either writing C++ CUDA kernels (Child et al., 2019) or using TVMs(Beltagy et al., 2020)). Other techniques which aim to reduce Transformers' space complexity include reversible residual layers allowing one-time activation storage in training(Kitaev et al., 2020)and shared attention weights(Xiao et al., 2019). These constraints may impede application to long-sequence problems, where approximations of the attention mechanism are not sufficient. Approximations based on truncated back-propagation are also unable to capture long-distance correlations since the gradients are only propagated inside a localized window. Other methods propose biased estimation of regular attention but only in the non-causal setting and with large mean squared error .In response, we introduce the first Transformer architectures, Performers, capable of provably accurate and practical estimation of regular (softmax) full-rank attention, but of only linear space and time complexity and not relying on any priors such as sparsity or low-rankness. Performers use the Fast Attention Via positive Orthogonal Random features (FAVOR+) mechanism, leveraging new methods for approximating softmax and Gaussian kernels, which we propose. We believe these methods are of independent interest, contributing to the theory of scalable kernel methods. Consequently, Performers are the first linear architectures fully compatible (via small amounts of fine-tuning) with regular Transformers, providing strong theoretical guarantees: unbiased or nearly-unbiased estimation of the attention matrix, uniform convergence and lower variance of the approximation.FAVOR+ can be also applied to efficiently model other kernelizable attention mechanisms beyond softmax. This representational power is crucial to accurately compare softmax with other kernels for the first time on large-scale tasks, that are beyond the reach of regular Transformers, and find for them optimal attention-kernels. FAVOR+ can also be applied beyond the Transformer scope as a more scalable replacement for regular attention, which itself has a wide variety of uses in computer vision(Fu et al., 2019), reinforcement learning (Zambaldi et al., 2019), training with softmax cross entropy loss, and even combinatorial optimization (Vinyals et al., 2015). | Published as a conference paper at ICLR 2021 RETHINKING ATTENTION WITH PERFORMERS |
d1926319 | Several recent publications have proposed methods for mapping images into continuous semantic embedding spaces. In some cases the embedding space is trained jointly with the image transformation. In other cases the semantic embedding space is established by an independent natural language processing task, and then the image transformation into that space is learned in a second stage. Proponents of these image embedding systems have stressed their advantages over the traditional n-way classification framing of image understanding, particularly in terms of the promise for zero-shot learning -the ability to correctly annotate images of previously unseen object categories. In this paper, we propose a simple method for constructing an image embedding system from any existing n-way image classifier and a semantic word embedding model, which contains the n class labels in its vocabulary. Our method maps images into the semantic embedding space via convex combination of the class label embedding vectors, and requires no additional training. We show that this simple and direct method confers many of the advantages associated with more complex image embedding schemes, and indeed outperforms state of the art methods on the ImageNet zero-shot learning task. | Zero-Shot Learning by Convex Combination of Semantic Embeddings |
d210164926 | Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversarial examples via a first-order method like projected gradient decent (PGD). In this paper, we make the surprising discovery that it is possible to train empirically robust models using a much weaker and cheaper adversary, an approach that was previously believed to be ineffective, rendering the method no more costly than standard training in practice. Specifically, we show that adversarial training with the fast gradient sign method (FGSM), when combined with random initialization, is as effective as PGD-based training but has significantly lower cost. Furthermore we show that FGSM adversarial training can be further accelerated by using standard techniques for efficient training of deep networks, allowing us to learn a robust CIFAR10 classifier with 45% robust accuracy to PGD attacks with = 8/255 in 6 minutes, and a robust ImageNet classifier with 43% robust accuracy at = 2/255 in 12 hours, in comparison to past work based on "free" adversarial training which took 10 and 50 hours to reach the same respective thresholds. Finally, we identify a failure mode referred to as "catastrophic overfitting" which may have caused previous attempts to use FGSM adversarial training to fail. All code for reproducing the experiments in this paper as well as pretrained model weights are at https://github.com/locuslab/fast_adversarial. * Equal contribution. arXiv:2001.03994v1 [cs.LG] 12 Jan 2020Published as a conference paper at ICLR 2020 that tries to to reduce the complexity of generating an adversarial example, which forms the bulk of the additional computation in adversarial trainingShafahi et al., 2019). While these works present reasonable improvements to the runtime of adversarial training, they are still significantly slower than standard training, which has been greatly accelerated due to competitions for optimizing both the speed and cost of training(Coleman et al., 2017).In this work, we argue that adversarial training, in fact, is not as hard as has been suggested by this past line of work. In particular, we revisit one of the the first proposed methods for adversarial training, using the Fast Gradient Sign Method (FGSM) to add adversarial examples to the training process(Goodfellow et al., 2014). Although this approach has long been dismissed as ineffective, we show that by simply introducing random initialization points, FGSM-based training is as effective as projected gradient descent based training while being an order of magnitude more efficient. Moreover, FGSM adversarial training (and to a lesser extent, other adversarial training methods) can be drastically accelerated using standard techniques for efficient training of deep networks, including e.g. cyclic learning rates(Smith & Topin, 2018), mixed-precision training (Micikevicius et al., 2017, and other similar techniques. The method has extremely few free parameters to tune, and can be easily adapted to most training procedures. We further identify a failure mode that we call "catastrophic overfitting", which may have caused previous attempts at FGSM adversarial training to fail against PGD-based attacks.The end result is that, with these approaches, we are able to train (empirically) robust classifiers far faster than in previous work. Specifically, we train an ∞ robust CIFAR10 model to 45% accuracy at = 8/255 (the same level attained in previous work) in 6 minutes; previous papers reported times of 80 hours for PGD-based training(Madry et al., 2017)and 10 hours for the more recent "free" adversarial training method(Shafahi et al., 2019). Similarly, we train an ∞ robust ImageNet classifier to 43% top-1 accuracy at = 2/255 (again matching previous results) in 12 hours of training (compared to 50 hours in the best reported previous work that we are aware of (Shafahi et al., 2019)). Both of these times roughly match the comparable time for quickly training a standard non-robust model to reasonable accuracy. We extensively evaluate these results against strong PGDbased attacks, and show that they obtain the same empirical performance as the slower, PGD-based training. Thus, we argue that despite the conventional wisdom, adversarially robust training is not actually more challenging than standard training of deep networks, and can be accomplished with the notoriously weak FGSM attack. | Published as a conference paper at ICLR 2020 FAST IS BETTER THAN FREE: REVISITING ADVERSARIAL TRAINING |
d249191720 | Existing dialogue modeling methods have achieved promising performance on various dialogue tasks with the aid of Transformer and the large-scale pre-trained language models. However, some recent studies revealed that the context representations produced by these methods suffer the problem of anisotropy. In this paper, we find that the generated representations are also not conversational, losing the conversation structure information during the context modeling stage. To this end, we identify two properties in dialogue modeling, i.e., locality and isotropy, and present a simple method for dialogue representation calibration, namely SimDRC, to build isotropic and conversational feature spaces. Experimental results show that our approach significantly outperforms current stateof-the-art models on three open-domain dialogue tasks with eight benchmarks across both automatic and human evaluation metrics. More in-depth analyses further confirm the effectiveness of our proposed approach. We release the code at | Published as a conference paper at ICLR 2023 LEARNING LOCALITY AND ISOTROPY IN DIALOGUE MODELING |
d2918187 | In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent. This set is learned by maximizing the number of different states an agent can reliably reach, as measured by the mutual information between the set of options and option termination states. To this end, we instantiate two policy gradient based algorithms, one that creates an explicit embedding space of options and one that represents options implicitly. The algorithms also provide an explicit measure of empowerment in a given state that can be used by an empowerment maximizing agent. The algorithm scales well with function approximation and we demonstrate the applicability of the algorithm on a range of tasks. | VARIATIONAL INTRINSIC CONTROL |
d231718721 | Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a 'texture bias': given an image with both texture and shape cues (e.g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture. However, these previous studies conduct experiments on the final classification output of the network, and fail to robustly evaluate the bias contained (i) in the latent representations, and (ii) on a per-pixel level. In this paper, we design a series of experiments that overcome these issues. We do this with the goal of better understanding what type of shape information contained in the network is discriminative, where shape information is encoded, as well as when the network learns about object shape during training. We show that a network learns the majority of overall shape information at the first few epochs of training and that this information is largely encoded in the last few layers of a CNN. Finally, we show that the encoding of shape does not imply the encoding of localized per-pixel semantic information. The experimental results and findings provide a more accurate understanding of the behaviour of current CNNs, thus helping to inform future design choices. | Published as a conference paper at ICLR 2021 SHAPE OR TEXTURE: UNDERSTANDING DISCRIMINATIVE FEATURES IN CNNS |
d231749857 | Controllable semantic image editing enables a user to change entire image attributes with few clicks, e.g., gradually making a summer scene look like it was taken in winter. Classic approaches for this task use a Generative Adversarial Net (GAN) to learn a latent space and suitable latent-space transformations. However, current approaches often suffer from attribute edits which are entangled, global image identity changes, and diminished photo-realism. To address these concerns, we learn multiple attribute transformations simultaneously, we integrate attribute regression into the training of transformation functions, apply a content loss and an adversarial loss that encourage the maintenance of image identity and photo-realism. We propose quantitative evaluation strategies for measuring controllable editing performance, unlike prior work which primarily focuses on qualitative evaluation. Our model permits better control for both single-and multiple-attribute editing, while also preserving image identity and realism during transformation. We provide empirical results for both real and synthetic images, highlighting that our model achieves state-of-the-art performance for targeted image manipulation. | Published as a conference paper at ICLR 2021 ENJOY YOUR EDITING: CONTROLLABLE GANS FOR IMAGE EDITING VIA LATENT SPACE NAVIGATION |
d244270768 | Being able to predict the mental states of others is a key factor to effective social interaction. It is also crucial for distributed multi-agent systems, where agents are required to communicate and cooperate. In this paper, we introduce such an important social-cognitive skill, i.e. Theory of Mind (ToM), to build socially intelligent agents who are able to communicate and cooperate effectively to accomplish challenging tasks. With ToM, each agent is capable of inferring the mental states and intentions of others according to its (local) observation. Based on the inferred states, the agents decide "when" and with "whom" to share their intentions. With the information observed, inferred, and received, the agents decide their sub-goals and reach a consensus among the team. In the end, the low-level executors independently take primitive actions to accomplish the sub-goals. We demonstrate the idea in two typical target-oriented multi-agent tasks: cooperative navigation and multisensor target coverage. The experiments show that the proposed model not only outperforms the state-of-the-art methods on reward and communication efficiency, but also shows good generalization across different scales of the environment. | Published as a conference paper at ICLR 2022 TOM2C: TARGET-ORIENTED MULTI-AGENT COMMU- NICATION AND COOPERATION WITH THEORY OF MIND |
d252846263 | Stochastic gradient descent-ascent (SGDA) is one of the main workhorses for solving finite-sum minimax optimization problems. Most practical implementations of SGDA randomly reshuffle components and sequentially use them (i.e., without-replacement sampling); however, there are few theoretical results on this approach for minimax algorithms, especially outside the easier-to-analyze (strongly-)monotone setups. To narrow this gap, we study the convergence bounds of SGDA with random reshuffling (SGDA-RR) for smooth nonconvexnonconcave objectives with Polyak-Łojasiewicz (PŁ) geometry. We analyze both simultaneous and alternating SGDA-RR for nonconvex-PŁ and primal-PŁ-PŁ objectives, and obtain convergence rates faster than with-replacement SGDA. Our rates extend to mini-batch SGDA-RR, recovering known rates for full-batch gradient descent-ascent (GDA). Lastly, we present a comprehensive lower bound for GDA with an arbitrary step-size ratio, which matches the full-batch upper bound for the primal-PŁ-PŁ case.Here, α > 0 and β > 0 are the step sizes and ∇ j denotes the gradient with respect to j-th argument for f i(t) (j = 1, 2). As shown in the update equations above, there are two widely used versions of SGDA: simultaneous SGDA (simSGDA), and alternating SGDA (altSGDA).In such stochastic gradient methods, there are two main categories of sampling schemes for the component indices i(t). One way is to sample i(t) independently (in time) and uniformly at random arXiv:2210.05995v2 [math.OC] | Published as a conference paper at ICLR 2023 SGDA WITH SHUFFLING: FASTER CONVERGENCE FOR NONCONVEX-PŁ MINIMAX OPTIMIZATION |
d257255342 | Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approximated surrogates for the fairness metrics in the objective as regularization terms. Although each of the learning schemes has its own strength in terms of applicability or performance, respectively, it is difficult for any method in the either category to be considered as a gold standard since their successful performances are typically limited to specific cases. To that end, we propose a principled method, dubbed as FairDRO, which unifies the two learning schemes by incorporating a well-justified group fairness metric into the training objective using a classwise distributionally robust optimization (DRO) framework. We then develop an iterative optimization algorithm that minimizes the resulting objective by automatically producing the correct re-weights for each group. Our experiments show that FairDRO is scalable and easily adaptable to diverse applications, and consistently achieves the state-of-the-art performance on several benchmark datasets in terms of the accuracy-fairness trade-off, compared to recent strong baselines.In this paper, we devise a new in-processing method, dubbed as Fairness-aware Distributionally Robust Optimization (FairDRO), which takes the advantages of both regularization and re-weighting based methods. The core of our method is to unify the two learning categories: namely, FairDRO incorporates a well-justified group fairness metric in the training objective as a regularizer, and optimizes the resulting objective function using a re-weighting based learning method. More specifically, we first present that a group fairness metric, Difference of Conditional Accuracy (DCA)(Berk et al., 2021), which is a natural extension of Equalized Opportunity(Hardt et al., 2016)to the multi-class, multi-group label settings, is equivalent (up to a constant) to the average (over the classes) of the roots of the variances of groupwise 0-1 losses. We then employ the Group DRO formulation, which uses the χ 2 -divergence ball including quasi-probabilities as the uncertainty set, for each class separately to convert the DCA (or variance) regularized group-balanced empirical risk minimization (ERM) to a more tractable minimax optimization. The inner maximizer in the converted optimization problem is then used as re-weights for the samples in each group, making a unified connection between the reweighting and regularization-based fairness-aware learning methods. Lastly, we develop an efficient iterative optimization algorithm, which automatically produces the correct (sometimes even negative) re-weights during the optimization process, in a more principled way than other re-weighting based methods. | RE-WEIGHTING BASED GROUP FAIRNESS REGULAR- IZATION VIA CLASSWISE ROBUST OPTIMIZATION |
d252668838 | Figure 1: Image translation results by DiffuseIT. Our model can generate high-quality translation outputs using both text and image conditions. More results can be found in the experiment section.ABSTRACTDiffusion-based image translation guided by semantic texts or a single target image has enabled flexible style transfer which is not limited to the specific domains. Unfortunately, due to the stochastic nature of diffusion models, it is often difficult to maintain the original content of the image during the reverse diffusion. To address this, here we present a novel diffusion-based unsupervised image translation method, dubbed as DiffuseIT, using disentangled style and content representation. Specifically, inspired by the slicing Vision Transformer (Tumanyan et al., 2022), we extract intermediate keys of multihead self attention layer from ViT model and used them as the content preservation loss. Then, an image guided style transfer is performed by matching the [CLS] classification token from the denoised samples and target image, whereas additional CLIP loss is used for the text-driven style transfer. To further accelerate the semantic change during the reverse diffusion, we also propose a novel semantic divergence loss and resampling strategy. Our experimental results show that the proposed method outperforms state-of-the-art baseline models in both text-guided and image-guided translation tasks. | DIFFUSION-BASED IMAGE TRANSLATION USING DIS- ENTANGLED STYLE AND CONTENT REPRESENTATION |
d256615188 | Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting in increased costs and patient discomfort, and decreased throughput. Thus, there is great interest in denoising MRI scans, especially for the subtype of diffusion MRI scans that are severely SNR-limited. While most prior MRI denoising methods are supervised in nature, acquiring supervised training datasets for the multitude of anatomies, MRI scanners, and scan parameters proves impractical. Here, we propose Denoising Diffusion Models for Denoising Diffusion MRI (DDM 2 ), a self-supervised denoising method for MRI denoising using diffusion denoising generative models. Our three-stage framework integrates statistic-based denoising theory into diffusion models and performs denoising through conditional generation. During inference, we represent input noisy measurements as a sample from an intermediate posterior distribution within the diffusion Markov chain. We conduct experiments on 4 real-world in-vivo diffusion MRI datasets and show that our DDM 2 demonstrates superior denoising performances ascertained with clinically-relevant visual qualitative and quantitative metrics. Our source codes are available at: https://github.com/StanfordMIMI/DDM2. | Published as a conference paper at ICLR 2023 DDM 2 : SELF-SUPERVISED DIFFUSION MRI DENOIS- ING WITH GENERATIVE DIFFUSION MODELS |
d232185104 | Pretrained text encoders, such as BERT, have been applied increasingly in various natural language processing (NLP) tasks, and have recently demonstrated significant performance gains. However, recent studies have demonstrated the existence of social bias in these pretrained NLP models. Although prior works have made progress on word-level debiasing, improved sentence-level fairness of pretrained encoders still lacks exploration. In this paper, we proposed the first neural debiasing method for a pretrained sentence encoder, which transforms the pretrained encoder outputs into debiased representations via a fair filter (FairFil) network. To learn the FairFil, we introduce a contrastive learning framework that not only minimizes the correlation between filtered embeddings and bias words but also preserves rich semantic information of the original sentences. On real-world datasets, our FairFil effectively reduces the bias degree of pretrained text encoders, while continuously showing desirable performance on downstream tasks. Moreover, our post hoc method does not require any retraining of the text encoders, further enlarging FairFil's application space. * Equal contribution. | Published as a conference paper at ICLR 2021 FAIRFIL: CONTRASTIVE NEURAL DEBIASING METHOD FOR PRETRAINED TEXT ENCODERS |
d246442150 | In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank characteristics of the subspace spanned by gradients across epochs (i.e., the gradient-space) in centralized model training, and observe that this gradient-space often consists of a few leading principal components accounting for an overwhelming majority (95 − 99%) of the explained variance. Motivated by this, we propose the "Look-back Gradient Multiplier" (LBGM) algorithm, which exploits this low-rank property to enable gradient recycling between model update rounds of federated learning, reducing transmissions of large parameters to single scalars for aggregation. We analytically characterize the convergence behavior of LBGM, revealing the nature of the trade-off between communication savings and model performance. Our subsequent experimental results demonstrate the improvement LBGM obtains in communication overhead compared to conventional federated learning on several datasets and deep learning models. Additionally, we show that LBGM is a general plug-and-play algorithm that can be used standalone or stacked on top of existing sparsification techniques for distributed model training.arXiv:2202.00280v1 [cs.LG] 1 Feb 2022Published as a conference paper at ICLR 2022 N-PCA and FL. In an "ideal" FL framework, if both the server and the workers/devices have the PGDs, then the newly generated gradients can be transmitted by sharing their projections on the PGDs, i.e., the PGD multipliers (PGM). PGMs and PGDs can together be used to reconstruct the device generated gradients at the server, dramatically reducing communication costs. However, this setting is impractical since: (i) it is infeasible to obtain the PGDs prior to the training, and (ii) PCA is computationally intensive. We thus look for an efficient online approximation of the PGDs. * The addition of a learning rate scheduler (e.g., cosine annealing scheduler (Loshchilov & Hutter, 2016)) has an effect on the PCA of the gradient-space. Careful investigation of this phenomenon is left to future work. † Refer to Algorithm 2 in Appendix D.1 for the detailed pseudocode.‡ 2 of 24 experiments(Fig. 52&53in Appendix E.3) show inconsistent gradient overlaps. However, our algorithm discussed in Sec. 3 still performs well on those datasets and models (seeFig. 60in Appendix E.3). | Published as a conference paper at ICLR 2022 RECYCLING MODEL UPDATES IN FEDERATED LEARNING: ARE GRADIENT SUBSPACES LOW-RANK? |
d233740397 | We present an unsupervised approach that converts the input speech of any individual into audiovisual streams of potentially-infinitely many output speakers. Our approach builds on simple autoencoders that project out-of-sample data onto the distribution of the training set. We use Exemplar Autoencoders to learn the voice, stylistic prosody, and visual appearance of a specific target exemplar speech. In contrast to existing methods, the proposed approach can be easily extended to an arbitrarily large number of speakers and styles using only 3 minutes of target audio-video data, without requiring any training data for the input speaker. To do so, we learn audiovisual bottleneck representations that capture the structured linguistic content of speech. We outperform prior approaches on both audio and video synthesis, and provide extensive qualitative analysis on our project page | Published as a conference paper at ICLR 2021 UNSUPERVISED AUDIOVISUAL SYNTHESIS VIA EXEMPLAR AUTOENCODERS |
d257232596 | Neighbor embedding methods t-SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. Motivated from entirely different viewpoints, their loss functions appear to be unrelated. In practice, they yield strongly differing embeddings and can suggest conflicting interpretations of the same data. The fundamental reasons for this and, more generally, the exact relationship between t-SNE and UMAP have remained unclear. In this work, we uncover their conceptual connection via a new insight into contrastive learning methods. Noisecontrastive estimation can be used to optimize t-SNE, while UMAP relies on negative sampling, another contrastive method. We find the precise relationship between these two contrastive methods and provide a mathematical characterization of the distortion introduced by negative sampling. Visually, this distortion results in UMAP generating more compact embeddings with tighter clusters compared to t-SNE. We exploit this new conceptual connection to propose and implement a generalization of negative sampling, allowing us to interpolate between (and even extrapolate beyond) t-SNE and UMAP and their respective embeddings. Moving along this spectrum of embeddings leads to a trade-off between discrete / local and continuous / global structures, mitigating the risk of over-interpreting ostensible features of any single embedding. We provide a PyTorch implementation. | Published as a conference paper at ICLR 2023 FROM t-SNE TO UMAP WITH CONTRASTIVE LEARNING |
d235436105 | Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs (KGs). It subsumes both one-hop link prediction as well as other more complex types of logical queries. However, existing algorithms operate only on classical, triple-based graphs, whereas modern KGs often employ a hyper-relational modeling paradigm. In this paradigm, typed edges may have several key-value pairs known as qualifiers that provide fine-grained context for facts. In queries, this context modifies the meaning of relations, and usually reduces the answer set. Hyper-relational queries are often observed in real-world KG applications, and existing approaches for approximate query answering (QA) cannot make use of qualifier pairs. In this work, we bridge this gap and extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries. Building upon recent advancements in Graph Neural Networks and query embedding techniques, we study how to embed and answer hyper-relational conjunctive queries. Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve QA on a diverse set of query patterns. | Published as a conference paper at ICLR 2022 QUERY EMBEDDING ON HYPER-RELATIONAL KNOWLEDGE GRAPHS |
d246431097 | The growing public concerns on data privacy in face recognition can be greatly addressed by the federated learning (FL) paradigm. However, conventional FL methods perform poorly due to the uniqueness of the task: broadcasting class centers among clients is crucial for recognition performances but leads to privacy leakage. To resolve the privacy-utility paradox, this work proposes PrivacyFace, a framework largely improves the federated learning face recognition via communicating auxiliary and privacy-agnostic information among clients. PrivacyFace mainly consists of two components: First, a practical Differentially Private Local Clustering (DPLC) mechanism is proposed to distill sanitized clusters from local class centers. Second, a consensus-aware recognition loss subsequently encourages global consensuses among clients, which ergo results in more discriminative features. The proposed framework is mathematically proved to be differentially private, introducing a lightweight overhead as well as yielding prominent performance boosts (e.g., +9.63% and +10.26% for TAR@FAR=1e-4 on IJB-B and IJB-C respectively). Extensive experiments and ablation studies on a large-scale dataset have demonstrated the efficacy and practicability of our method. | IMPROVING FEDERATED LEARNING FACE RECOGNI- TION VIA PRIVACY-AGNOSTIC CLUSTERS |
d244714983 | We study a class of algorithms for solving bilevel optimization problems in both stochastic and deterministic settings when the inner-level objective is strongly convex. Specifically, we consider algorithms based on inexact implicit differentiation and we exploit a warm-start strategy to amortize the estimation of the exact gradient. We then introduce a unified theoretical framework inspired by the study of singularly perturbed systems (Habets, 1974) to analyze such amortized algorithms. By using this framework, our analysis shows these algorithms to match the computational complexity of oracle methods that have access to an unbiased estimate of the gradient, thus outperforming many existing results for bilevel optimization. We illustrate these findings on synthetic experiments and demonstrate the efficiency of these algorithms on hyper-parameter optimization experiments involving several thousands of variables. | Published as a conference paper at ICLR 2022 AMORTIZED IMPLICIT DIFFERENTIATION FOR STOCHASTIC BILEVEL OPTIMIZATION |
d3431470 | We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recently proposed few-shot learning models. Besides providing improved numerical performance, our framework is easily extended to variants of few-shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based models to operate well on 'relational' tasks. | FEW-SHOT LEARNING WITH GRAPH NEURAL NET- WORKS |
d46932612 | Our understanding of reinforcement learning (RL) has been shaped by theoretical and empirical results that were obtained decades ago using tabular representations and linear function approximators. These results suggest that RL methods that use temporal differencing (TD) are superior to direct Monte Carlo estimation (MC). How do these results hold up in deep RL, which deals with perceptually complex environments and deep nonlinear models? In this paper, we re-examine the role of TD in modern deep RL, using specially designed environments that control for specific factors that affect performance, such as reward sparsity, reward delay, and the perceptual complexity of the task. When comparing TD with infinite-horizon MC, we are able to reproduce classic results in modern settings. Yet we also find that finite-horizon MC is not inferior to TD, even when rewards are sparse or delayed. This makes MC a viable alternative to TD in deep RL. | Published as a conference paper at ICLR 2018 TD OR NOT TD: ANALYZING THE ROLE OF TEMPORAL DIFFERENCING IN DEEP REINFORCEMENT LEARNING |
d5959482 | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities. arXiv:1301.3781v3 [cs.CL] 7 Sep 2013 1 The test set is available at www.fit.vutbr.cz/˜imikolov/rnnlm/word-test.v1.txt 2 | Efficient Estimation of Word Representations in Vector Space |
d251719318 | Representation learning often plays a critical role in avoiding the curse of dimensionality in reinforcement learning. A representative class of algorithms exploits spectral decomposition of the stochastic transition dynamics to construct representations that enjoy strong theoretical properties in idealized settings. However, current spectral methods suffer from limited applicability because they are constructed for state-only aggregation and are derived from a policy-dependent transition kernel, without considering the issue of exploration. To address these issues, we propose an alternative spectral method, Spectral Decomposition Representation (SPEDER), that extracts a state-action abstraction from the dynamics without inducing spurious dependence on the data collection policy, while also balancing the explorationversus-exploitation trade-off during learning. A theoretical analysis establishes the sample efficiency of the proposed algorithm in both the online and offline settings. In addition, an experimental investigation demonstrates superior performance over current state-of-the-art algorithms across several RL benchmarks. * Equal Contribution.These definitions imply the following recursive relationships:V π P,r (s) = E π Q π P,r (s, a) , Q π P,r (s, a) = r(s, a) + γE P V π P,r (s ) . We additionally define the state visitation distribution induced by a policy π as d πWhen |S| and |A| are finite, there exist sample-efficient algorithms that find the optimal policy by maintaining an estimate of P or Q π P,r (Azar et al., 2017; Jin et al., 2018). However, such methods cannot be scaled up when |S| and |A| are extremely large or infinite. In such cases, function approximation is needed to exploit the structure of the MDP while avoiding explicit dependence on |S| and |A|. The low rank MDP is one of the most prominent structures that allows for simple yet effective function approximation in MDPs, which is based on the following spectral structural assumption on P and r: Assumption 1 (Low Rank MDP, (Jin et al., 2020;Agarwal et al., 2020)). An MDP M is a low rank MDP if there exists a low rank spectral decomposition of the transition kernel P (s |s, a), such that P (s |s, a) = φ(s, a), µ(s ) , r(s, a) = φ(s, a), θ r ,2 Published as a conference paper at ICLR 2023 with two spectral maps φ : S × A → R d and µ : S → R d , and a vector θ r ∈ R d . The φ and µ also satisfy the following normalization conditions:The low rank MDP allows for a linear representation of Q π P,r for any arbitrary policy π, since Q π P,r (s, a) = r(s, a) + γ V π P,r (s)P (s |s, a)ds = φ(s, a), θ r + γ V π P,r (s )µ(s )ds .(3) Hence, we can provably perform computationally-efficient planning and sample-efficient exploration in a low-rank MDP given φ(s, a), as shown in (Jin et al., 2020). However, φ(s, a) is generally unknown to the reinforcement learning algorithm, and must be learned via representation learning to leverage the structure of low rank MDPs. 2.. Deep reinforcement learning in a handful of trials using probabilistic dynamics models. Advances in neural information processing systems, 31, 2018. Peter Dayan. Improving generalization for temporal difference learning: The successor representation. Neural Computation, 5(4):613-624, 1993. Thomas G Dietterich et al. The maxq method for hierarchical reinforcement learning. , et al. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905, 2018. Tenenbaum. Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. Advances in neural information processing systems, 29, 2016a. Tejas D Kulkarni, Ardavan Saeedi, Simanta Gautam, and Samuel J Gershman. Deep successor reinforcement learning. arXiv preprint arXiv:1606.02396, 2016b. Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, and Pieter Abbeel. Model-ensemble trust-region policy optimization. arXiv preprint arXiv:1802.10592, 2018. | Published as a conference paper at ICLR 2023 SPECTRAL DECOMPOSITION REPRESENTATION FOR REINFORCEMENT LEARNING |
d1662233 | Representation learning systems typically rely on massive amounts of labeled data in order to be trained to high accuracy. Recently, high-dimensional parametric models like neural networks have succeeded in building rich representations using either compressive, reconstructive or supervised criteria. However, the semantic structure inherent in observations is oftentimes lost in the process. Human perception excels at understanding semantics but cannot always be expressed in terms of labels. Thus, oracles or human-in-the-loop systems, for example crowdsourcing, are often employed to generate similarity constraints using an implicit similarity function encoded in human perception. In this work we propose to combine generative unsupervised feature learning with a probabilistic treatment of oracle information like triplets in order to transfer implicit privileged oracle knowledge into explicit nonlinear Bayesian latent factor models of the observations. We use a fast variational algorithm to learn the joint model and demonstrate applicability to a well-known image dataset. We show how implicit triplet information can provide rich information to learn representations that outperform previous metric learning approaches as well as generative models without this side-information in a variety of predictive tasks. In addition, we illustrate that the proposed approach compartmentalizes the latent spaces semantically which allows interpretation of the latent variables. | BAYESIAN REPRESENTATION LEARNING WITH ORACLE CONSTRAINTS |
d211096885 | While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a random variable that can be estimated from multiple angles. In this generalized framework, referred to as RealnessGAN 1 , the discriminator outputs a distribution as the measure of realness. While RealnessGAN shares similar theoretical guarantees with the standard GAN, it provides more insights on adversarial learning. Compared to multiple baselines, RealnessGAN provides stronger guidance for the generator, achieving improvements on both synthetic and real-world datasets. Moreover, it enables the basic DCGAN(Radford et al., 2015)architecture to generate realistic images at 1024*1024 resolution when trained from scratch. | Published as a conference paper at ICLR 2020 REAL OR NOT REAL, THAT IS THE QUESTION |
d203594002 | Learning to align multiple datasets is an important problem with many applications, and it is especially useful when we need to integrate multiple experiments or correct for confounding. Optimal transport (OT) is a principled approach to align datasets, but a key challenge in applying OT is that we need to specify a transport cost function that accurately captures how the two datasets are related. Reliable cost functions are typically not available and practitioners often resort to using hand-crafted or Euclidean cost even if it may not be appropriate. In this work, we investigate how to learn the cost function using a small amount of side information which is often available. The side information we consider captures subset correspondence-i.e. certain subsets of points in the two data sets are known to be related. For example, we may have some images labeled as cars in both datasets; or we may have a common annotated cell type in single-cell data from two batches. We develop an end-to-end optimizer (OT-SI) that differentiates through the Sinkhorn algorithm and effectively learns the suitable cost function from side information. On systematic experiments in images, marriage-matching and single-cell RNA-seq, our method substantially outperform state-of-the-art benchmarks. . On the scaling of multidimensional matrices. Linear Algebra and its applications, 114:717-735, 1989. Alfred Galichon and Bernard Salanié. Matching with trade-offs: Revealed preferences over competing characteristics. 2010. Aude Genevay, Gabriel Peyré, and Marco Cuturi. Learning generative models with sinkhorn divergences. arXiv preprint arXiv:1706.00292, 2017. Pascal Germain, Amaury Habrard, François Laviolette, and Emilie Morvant. A pac-bayesian approach for domain adaptation with specialization to linear classifiers. In International conference on machine learning, pp. 738-746, 2013. Edouard Grave, Armand Joulin, and Quentin Berthet. Unsupervised alignment of embeddings with wasserstein procrustes. | Published as a conference paper at ICLR 2020 LEARNING TRANSPORT COST FROM SUBSET CORRE- SPONDENCE |
d16734174 | We develop a representation suitable for the unconstrained recognition of words in natural images, where unconstrained means that there is no fixed lexicon and words have unknown length.To this end we propose a convolutional neural network (CNN) based architecture which incorporates a Conditional Random Field (CRF) graphical model, taking the whole word image as a single input. The unaries of the CRF are provided by a CNN that predicts characters at each position of the output, while higher order terms are provided by another CNN that detects the presence of N-grams. We show that this entire model (CRF, character predictor, N-gram predictor) can be jointly optimised by back-propagating the structured output loss, essentially requiring the system to perform multi-task learning, and training requires only synthetically generated data. The resulting model is a more accurate system on standard real-world text recognition benchmarks than character prediction alone, setting a benchmark for systems that have not been trained on a particular lexicon. In addition, our model achieves state-of-the-art accuracy in lexicon-constrained scenarios, without being specifically modelled for constrained recognition. To test the generalisation of our model, we also perform experiments with random alpha-numeric strings to evaluate the method when no visual language model is applicable. * Current affiliation Google DeepMind. + Current affiliation University of Oxford and Google DeepMind. | DEEP STRUCTURED OUTPUT LEARNING FOR UNCONSTRAINED TEXT RECOGNITION |
d49745096 | Disjoint Mapping Network for Cross-modal Matching of Voices and Faces Contrastive Loss Triplet Loss Multi-task Classification Multiple Covariates Supervision | |
d225062556 | The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes access to an unlabelled test sample. This sample may be used to estimate the test label distribution, and to then train a suitably re-weighted classifier. While approaches using this idea have proven effective, their scope is limited as it is not always feasible to access the target domain; further, they require repeated retraining if the model is to be deployed in multiple test environments. Can one instead learn a single classifier that is robust to arbitrary label shifts from a broad family? In this paper, we answer this question by proposing a model that minimises an objective based on distributionally robust optimisation (DRO). We then design and analyse a gradient descent-proximal mirror ascent algorithm tailored for large-scale problems to optimise the proposed objective. Finally, through experiments on CIFAR-100 and ImageNet, we show that our technique can significantly improve performance over a number of baselines in settings where label shift is present. | Published as a conference paper at ICLR 2021 COPING WITH LABEL SHIFT VIA DISTRIBUTIONALLY ROBUST OPTIMISATION |
d88522390 | This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions. To that end, we propose a stochastic dual approach of regularized OT, and show empirically that it scales better than a recent related approach when the amount of samples is very large. Second, we estimate a Monge map as a deep neural network learned by approximating the barycentric projection of the previously-obtained OT plan. This parameterization allows generalization of the mapping outside the support of the input measure. We prove two theoretical stability results of regularized OT which show that our estimations converge to the OT plan and Monge map between the underlying continuous measures. We showcase our proposed approach on two applications: domain adaptation and generative modeling. | LARGE-SCALE OPTIMAL TRANSPORT AND MAPPING ESTIMATION |
d213695995 | Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box recommender systems. In particular, we propose to interpret feature interactions from a source recommender model and explicitly encode these interactions in a target recommender model, where both source and target models are black-boxes. By not assuming the structure of the recommender system, our approach can be used in general settings. In our experiments, we focus on a prominent use of machine learning recommendation: ad-click prediction. We found that our interaction interpretations are both informative and predictive, e.g., significantly outperforming existing recommender models. What's more, the same approach to interpret interactions can provide new insights into domains even beyond recommendation, such as text and image classification. | Published as a conference paper at ICLR 2020 FEATURE INTERACTION INTERPRETABILITY: A CASE FOR EXPLAINING AD-RECOMMENDATION SYSTEMS VIA NEURAL INTERACTION DETECTION |
d256389347 | Federated learning is a distributed paradigm that allows multiple parties to collaboratively train deep models without exchanging the raw data. However, the data distribution among clients is naturally non-i.i.d., which leads to severe degradation of the learnt model. The primary goal of this paper is to develop a robust federated learning algorithm to address feature shift in clients' samples, which can be caused by various factors, e.g., acquisition differences in medical imaging. To reach this goal, we propose FEDFA to tackle federated learning from a distinct perspective of federated feature augmentation. FEDFA is based on a major insight that each client's data distribution can be characterized by statistics (i.e., mean and standard deviation) of latent features; and it is likely to manipulate these local statistics globally, i.e., based on information in the entire federation, to let clients have a better sense of the underlying distribution and therefore alleviate local data bias. Based on this insight, we propose to augment each local feature statistic probabilistically based on a normal distribution, whose mean is the original statistic and variance quantifies the augmentation scope. Key to our approach is the determination of a meaningful Gaussian variance, which is accomplished by taking into account not only biased data of each individual client, but also underlying feature statistics characterized by all participating clients. We offer both theoretical and empirical justifications to verify the effectiveness of FEDFA. Our code is available at https://github.com/tfzhou/FedFA. . Adapting visual category models to new domains. In ECCV, 2010. | Published as a conference paper at ICLR 2023 FEDFA: FEDERATED FEATURE AUGMENTATION |
d14089312 | The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks. We present an acceleration method for CNNs, where we prune filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole filters in the network together with their connecting feature maps, the computation costs are reduced significantly. In contrast to pruning weights, this approach does not result in sparse connectivity patterns. Hence, it does not need the support of sparse convolution libraries and can work with existing efficient BLAS libraries for dense matrix multiplications. We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34% and ResNet-110 by up to 38% on CIFAR10 while regaining close to the original accuracy by retraining the networks. | Published as a conference paper at ICLR 2017 PRUNING FILTERS FOR EFFICIENT CONVNETS |
d61857474 | We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, single-path formulation of the nCRP, allowing a document to more easily express thematic borrowings as a random effect. We demonstrate our algorithm on 1.8 million documents from The New York Times. 1 | A Nested HDP for Hierarchical Topic Models |
d257233135 | Self-supervised learning has significantly improved the performance of many NLP tasks. However, how can self-supervised learning discover useful representations, and why is it better than traditional approaches such as probabilistic models are still largely unknown. In this paper, we focus on the context of topic modeling and highlight a key advantage of self-supervised learning -when applied to data generated by topic models, self-supervised learning can be oblivious to the specific model, and hence is less susceptible to model misspecification. In particular, we prove that commonly used self-supervised objectives based on reconstruction or contrastive samples can both recover useful posterior information for general topic models. Empirically, we show that the same objectives can perform on par with posterior inference using the correct model, while outperforming posterior inference using misspecified models. : Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.Nicolas Gillis and StephenA Vavasis. Fast and robust recursive algorithmsfor separable nonnegative matrix factorization. IEEE transactions on pattern analysis and machine intelligence, 36(4): 698-714, 2013. et al. Bootstrap your own latent: A new approach to self-supervised learning. arXiv preprint arXivguarantees for self-supervised deep learning with spectral contrastive loss. arXiv preprint arXiv:2106.04156, 2021. Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In . Predicting what you already know helps: Provable self-supervised learning. arXiv preprint arXiv:2008.01064, 2020. Wei Li and Andrew McCallum. Pachinko allocation: Dag-structured mixture models of topic correlations. In | UNDERSTANDING THE ROBUSTNESS OF SELF- SUPERVISED LEARNING THROUGH TOPIC MODELING |
d195346786 | Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the planner. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al., 2017) on multiple Atari games, with deeper trees often outperforming shallower ones. We also present a qualitative analysis that sheds light on the trees learned by TreeQN. † These authors contributed equally to this work. | TREEQN AND ATREEC: DIFFERENTIABLE TREE PLANNING FOR DEEP REINFORCEMENT LEARNING |
d238856707 | Many-to-one maps are ubiquitous in machine learning, from the image recognition model that assigns a multitude of distinct images to the concept of "cat" to the time series forecasting model which assigns a range of distinct time-series to a single scalar regression value. While the primary use of such models is naturally to associate correct output to each input, in many problems it is also useful to be able to explore, understand, and sample from a model's fibers, which are the set of input values x such that f (x) = y, for fixed y in the output space. In this paper we show that popular generative architectures are ill-suited to such tasks. Motivated by this we introduce a novel generative architecture, a Bundle Network, based on the concept of a fiber bundle from (differential) topology. BundleNets exploit the idea of a local trivialization wherein a space can be locally decomposed into a product space that cleanly encodes the many-to-one nature of the map. By enforcing this decomposition in BundleNets and by utilizing state-of-the-art invertible components, investigating a network's fibers becomes natural.Published as a conference paper at ICLR 2022 is the ground truth pairing between input space X and output space Y , this amounts to calculating the inverse image π −1 (y), or fiber of π at y.Our goal in the present work is to (1) formalize this problem and (2) describe a deep learning framework which readily enables this kind of analysis. We take as our inspiration the notion of a fiber bundle from topology(Seifert, 1933;Whitney, 1935). Consider the projection map on X = Y × Z, π : Y × Z → Y , which sends π(y, z) = y. For any y ∈ Y , the inverse image π −1 (y) is easily calculated as {y} × Z ∼ = Z. Interpreted in terms of the machine learning task, Y is the component of X that we want to predict and Z encodes the remaining variation occurring among all x that map to y. Unfortunately, in our nonlinear world data distributions can rarely be decomposed into a product space on a global level like this. Instead, it is more realistic to hope that for each sufficiently small neighborhood U of Y , we can find a data distribution preserving homeomorphism U × Z ∼ | Published as a conference paper at ICLR 2022 BUNDLE NETWORKS: FIBER BUNDLES, LOCAL TRIV- IALIZATIONS, AND A GENERATIVE APPROACH TO EX- PLORING MANY-TO-ONE MAPS |
d15389304 | Convolutional neural networks perform well on object recognition because of a number of recent advances: rectified linear units (ReLUs), data augmentation, dropout, and large labelled datasets. Unsupervised data has been proposed as another way to improve performance. Unfortunately, unsupervised pre-training is not used by state-of-the-art methods leading to the following question: Is unsupervised pre-training still useful given recent advances? If so, when? We answer this in three parts: we 1) develop a unsupervised method that incorporates ReLUs and recent unsupervised regularization techniques, 2) analyze the benefits of unsupervised pre-training compared to data augmentation and dropout on CIFAR-10 while varying the ratio of unsupervised to supervised samples, 3) verify our findings on STL-10. We discover unsupervised pre-training, as expected, helps when the ratio of unsupervised to supervised samples is high, and surprisingly, hurts when the ratio is low. We also use unsupervised pre-training with additional color augmentation to achieve near state-of-the-art performance on STL-10. | AN ANALYSIS OF UNSUPERVISED PRE-TRAINING IN LIGHT OF RECENT ADVANCES |
d3555598 | Computing universal distributed representations of sentences is a fundamental task in natural language processing. We propose a method to learn such representations by encoding the suffixes of word sequences in a sentence and training on the Stanford Natural Language Inference (SNLI) dataset. We demonstrate the effectiveness of our approach by evaluating it on the SentEval benchmark, improving on existing approaches on several transfer tasks. | Workshop track -ICLR 2018 SUFISENT -UNIVERSAL SENTENCE REPRESENTA- TIONS USING SUFFIX ENCODINGS |
d249847976 | Modern deep learning systems are increasingly deployed in situations such as personalization and federated learning where it is necessary to support i) learning on small amounts of data, and ii) communication efficient distributed training protocols. In this work, we develop FiLM Transfer (FIT) which fulfills these requirements in the image classification setting by combining ideas from transfer learning (fixed pretrained backbones and fine-tuned FiLM adapter layers) and metalearning (automatically configured Naive Bayes classifiers and episodic training) to yield parameter efficient models with superior classification accuracy at low-shot. The resulting parameter efficiency is key for enabling few-shot learning, inexpensive model updates for personalization, and communication efficient federated learning. We experiment with FIT on a wide range of downstream datasets and show that it achieves better classification accuracy than the leading Big Transfer (BiT) algorithm at low-shot and achieves state-of-the art accuracy on the challenging VTAB-1k benchmark, with fewer than 1% of the updateable parameters. Finally, we demonstrate the parameter efficiency and superior accuracy of FIT in distributed low-shot applications including model personalization and federated learning where model update size is an important performance metric. | Published as a conference paper at ICLR 2023 FIT: PARAMETER EFFICIENT FEW-SHOT TRANSFER LEARNING FOR PERSONALIZED AND FEDERATED IMAGE CLASSIFICATION |
d174802916 | Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-the-art video generation models often attempt to address these issues by combining sometimes complex, usually video-specific neural network architectures, latent variable models, adversarial training and a range of other methods. Despite their often high complexity, these approaches still fall short of generating high quality video continuations outside of narrow domains and often struggle with fidelity. In contrast, we show that conceptually simple autoregressive video generation models based on a three-dimensional self-attention mechanism achieve competitive results across multiple metrics on popular benchmark datasets, for which they produce continuations of high fidelity and realism. We also present results from training our models on Kinetics, a large scale action recognition dataset comprised of YouTube videos exhibiting phenomena such as camera movement, complex object interactions and diverse human movement. While modeling these phenomena consistently remains elusive, we hope that our results, which include occasional realistic continuations encourage further research on comparatively complex, large scale datasets such as Kinetics.Published as a conference paper at ICLR 2020We obtain strong results on popular benchmarks (Section 4.2, Appendix A) and produce high fidelity video continuations on the BAIR robot pushing dataset(Ebert et al., 2017)exhibiting plausible object interactions. Furthermore, our model achieves an almost 50% reduction in perplexity compared to prior work on autoregressive models on another robot pushing dataset.Finally, we apply our models to down-sampled videos from the Kinetics-600 dataset(Section 4.3). While modeling the full range of Kinetics-600 videos still poses a major challenge, we see encouraging video continuations for a more limited subset, namely cooking videos. These feature camera movement, complex object interactions and still cover diverse subjects.We hope that these initial results will encourage future video generation work to evaluate models on more challenging datasets such as Kinetics. | Published as a conference paper at ICLR 2020 SCALING AUTOREGRESSIVE VIDEO MODELS |
d31598573 | Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to employ hierarchical policies that decide when to use a previously learned policy and when to learn a new skill. This enables agents to continually acquire new skills during different stages of training. Each learned task corresponds to a human language description. Because agents can only access previously learned skills through these descriptions, the agent can always provide a human-interpretable description of its choices. In order to help the agent learn the complex temporal dependencies necessary for the hierarchical policy, we provide it with a stochastic temporal grammar that modulates when to rely on previously learned skills and when to execute new skills. We validate our approach on Minecraft games designed to explicitly test the ability to reuse previously learned skills while simultaneously learning new skills. | Under review as a conference paper at ICLR 2018 HIERARCHICAL AND INTERPRETABLE SKILL ACQUI- SITION IN MULTI-TASK REINFORCEMENT LEARNING |
d231798234 | Robustness against word substitutions has a well-defined and widely acceptable form, i.e., using semantically similar words as substitutions, and thus it is considered as a fundamental stepping-stone towards broader robustness in natural language processing. Previous defense methods capture word substitutions in vector space by using either l 2 -ball or hyper-rectangle, which results in perturbation sets that are not inclusive enough or unnecessarily large, and thus impedes mimicry of worst cases for robust training. In this paper, we introduce a novel Adversarial Sparse Convex Combination (ASCC) method. We model the word substitution attack space as a convex hull and leverages a regularization term to enforce perturbation towards an actual substitution, thus aligning our modeling better with the discrete textual space. Based on the ASCC method, we further propose ASCC-defense, which leverages ASCC to generate worst-case perturbations and incorporates adversarial training towards robustness. Experiments show that ASCC-defense outperforms the current state-of-the-arts in terms of robustness on two prevailing NLP tasks, i.e., sentiment analysis and natural language inference, concerning several attacks across multiple model architectures. Besides, we also envision a new class of defense towards robustness in NLP, where our robustly trained word vectors can be plugged into a normally trained model and enforce its robustness without applying any other defense techniques. 1 | Published as a conference paper at ICLR 2021 TOWARDS ROBUSTNESS AGAINST NATURAL LANGUAGE WORD SUBSTITUTIONS |
d257038997 | Knowledge distillation has been shown to be a powerful model compression approach to facilitate the deployment of pre-trained language models in practice. This paper focuses on task-agnostic distillation. It produces a compact pre-trained model that can be easily fine-tuned on various tasks with small computational costs and memory footprints. Despite the practical benefits, task-agnostic distillation is challenging. Since the teacher model has a significantly larger capacity and stronger representation power than the student model, it is very difficult for the student to produce predictions that match the teacher's over a massive amount of open-domain training data. Such a large prediction discrepancy often diminishes the benefits of knowledge distillation. To address this challenge, we propose Homotopic Distillation (HomoDistil), a novel task-agnostic distillation approach equipped with iterative pruning. Specifically, we initialize the student model from the teacher model, and iteratively prune the student's neurons until the target width is reached. Such an approach maintains a small discrepancy between the teacher's and student's predictions throughout the distillation process, which ensures the effectiveness of knowledge transfer. Extensive experiments demonstrate that Ho-moDistil achieves significant improvements on existing baselines 1 . | HOMODISTIL: HOMOTOPIC TASK-AGNOSTIC DISTIL- LATION OF PRE-TRAINED TRANSFORMERS |
d231719419 | Hopfield networks (HNs) and Restricted Boltzmann Machines (RBMs) are two important models at the interface of statistical physics, machine learning, and neuroscience. Recently, there has been interest in the relationship between HNs and RBMs, due to their similarity under the statistical mechanics formalism. An exact mapping between HNs and RBMs has been previously noted for the special case of orthogonal ("uncorrelated") encoded patterns. We present here an exact mapping in the general case of correlated pattern HNs, which are more broadly applicable to existing datasets. Specifically, we show that any HN with N binary variables and p < N arbitrary binary patterns can be transformed into an RBM with N binary visible variables and p gaussian hidden variables. We outline the conditions under which the reverse mapping exists, and conduct experiments on the MNIST dataset which suggest the mapping provides a useful initialization to the RBM weights. We discuss extensions, the potential importance of this correspondence for the training of RBMs, and for understanding the performance of deep architectures which utilize RBMs. | Published as a conference paper at ICLR 2021 ON THE MAPPING BETWEEN HOPFIELD NETWORKS AND RESTRICTED BOLTZMANN MACHINES |
d257232672 | We create a reusable Transformer, BrainBERT, for intracranial field potential recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition, this Transformer enables classifying complex concepts, i.e., decoding neural data, with higher accuracy and with much less data by being pretrained in an unsupervised manner on a large corpus of unannotated neural recordings. Our approach generalizes to new subjects with electrodes in new positions and to unrelated tasks showing that the representations robustly disentangle the neural signal. Just like in NLP where one can study language by investigating what a language model learns, this approach enables investigating the brain by studying what a model of the brain learns. As a first step along this path, we demonstrate a new analysis of the intrinsic dimensionality of the computations in different areas of the brain. To construct BrainBERT, we combine super-resolution spectrograms of neural data with an approach designed for generating contextual representations of audio by masking. In the future, far more concepts will be decodable from neural recordings by using representation learning, potentially unlocking the brain like language models unlocked language.Experiments that contributed to this work were approved by IRB. All subjects consented to participate. All electrode locations were exclusively dictated by clinical considerations.REPRODUCIBILITY STATEMENTCode to train models and reproduce the results was submitted as part of the supplementary materials and can be accessed here: https://github.com/czlwang/BrainBERT. | Published as a conference paper at ICLR 2023 BRAINBERT: SELF-SUPERVISED REPRESENTATION LEARNING FOR INTRACRANIAL RECORDINGS |
d204806091 | Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution. In single particle cryo-EM, the central problem is to reconstruct the 3D structure of a macromolecule from 10 4−7 noisy and randomly oriented 2D projection images. However, the imaged protein complexes may exhibit structural variability, which complicates reconstruction and is typically addressed using discrete clustering approaches that fail to capture the full range of protein dynamics. Here, we introduce a novel method for cryo-EM reconstruction that extends naturally to modeling continuous generative factors of structural heterogeneity. This method encodes structures in Fourier space using coordinate-based deep neural networks, and trains these networks from unlabeled 2D cryo-EM images by combining exact inference over image orientation with variational inference for structural heterogeneity. We demonstrate that the proposed method, termed cryoDRGN, can perform ab initio reconstruction of 3D protein complexes from simulated and real 2D cryo-EM image data. To our knowledge, cryoDRGN is the first neural networkbased approach for cryo-EM reconstruction and the first end-to-end method for directly reconstructing continuous ensembles of protein structures from cryo-EM images. * Corresponding authors arXiv:1909.05215v3 [q-bio.QM] | Published as a conference paper at ICLR 2020 RECONSTRUCTING CONTINUOUS DISTRIBUTIONS OF 3D PROTEIN STRUCTURE FROM CRYO-EM IMAGES |
d222125159 | Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction for avoiding biases from noisy energy estimators has limited the potential of the acceleration. To address this issue, we study the variance reduction for noisy energy estimators, which promotes much more effective swaps. Theoretically, we provide a non-asymptotic analysis on the exponential acceleration for the underlying continuous-time Markov jump process; moreover, we consider a generalized Girsanov theorem which includes the change of Poisson measure to overcome the crude discretization based on the Gröwall's inequality and yields a much tighter error in the 2-Wasserstein (W 2 ) distance. Numerically, we conduct extensive experiments and obtain the state-of-the-art results in optimization and uncertainty estimates for synthetic experiments and image data. | ACCELERATING CONVERGENCE OF REPLICA EX- CHANGE STOCHASTIC GRADIENT MCMC VIA VARI- ANCE REDUCTION |
d257496462 | Oversmoothing is a common phenomenon in a wide range of Graph Neural Networks (GNNs) and Transformers, where performance worsens as the number of layers increases. Instead of characterizing oversmoothing from the view of complete collapse in which representations converge to a single point, we dive into a more general perspective of dimensional collapse in which representations lie in a narrow cone. Accordingly, inspired by the effectiveness of contrastive learning in preventing dimensional collapse, we propose a novel normalization layer called ContraNorm. Intuitively, ContraNorm implicitly shatters representations in the embedding space, leading to a more uniform distribution and a slighter dimensional collapse. On the theoretical analysis, we prove that ContraNorm can alleviate both complete collapse and dimensional collapse under certain conditions. Our proposed normalization layer can be easily integrated into GNNs and Transformers with negligible parameter overhead. Experiments on various real-world datasets demonstrate the effectiveness of our proposed ContraNorm. Our implementation is available at https://github.com/PKU-ML/ContraNorm. | CONTRANORM: A CONTRASTIVE LEARNING PER- SPECTIVE ON OVERSMOOTHING AND BEYOND |
d246705922 | Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there is a performance gap between quantized and fullprecision models. To reduce it, existing quantization approaches require highprecision INT32 or full-precision multiplication during inference for scaling or dequantization. This introduces a noticeable cost in terms of memory, speed, and required energy. To tackle these issues, we present F8Net, a novel quantization framework consisting of only fixed-point 8-bit multiplication. To derive our method, we first discuss the advantages of fixed-point multiplication with different formats of fixed-point numbers and study the statistical behavior of the associated fixedpoint numbers. Second, based on the statistical and algorithmic analysis, we apply different fixed-point formats for weights and activations of different layers. We introduce a novel algorithm to automatically determine the right format for each layer during training. Third, we analyze a previous quantization algorithmparameterized clipping activation (PACT)-and reformulate it using fixed-point arithmetic. Finally, we unify the recently proposed method for quantization finetuning and our fixed-point approach to show the potential of our method. We verify F8Net on ImageNet for MobileNet V1/V2 and ResNet18/50. Our approach achieves comparable and better performance, when compared not only to existing quantization techniques with INT32 multiplication or floating-point arithmetic, but also to the full-precision counterparts, achieving state-of-the-art performance. | Published as a conference paper at ICLR 2022 F8NET: FIXED-POINT 8-BIT ONLY MULTIPLICATION FOR NETWORK QUANTIZATION |
d245005831 | Numerous applications of machine learning involve representing probability distributions over high-dimensional data. We propose autoregressive quantile flows, a flexible class of normalizing flow models trained using a novel objective based on proper scoring rules. Our objective does not require calculating computationally expensive determinants of Jacobians during training and supports new types of neural architectures, such as neural autoregressive flows from which it is easy to sample. We leverage these models in quantile flow regression, an approach that parameterizes predictive conditional distributions with flows, resulting in improved probabilistic predictions on tasks such as time series forecasting and object detection. Our novel objective functions and neural flow parameterizations also yield improvements on popular generation and density estimation tasks, and represent a step beyond maximum likelihood learning of flows.Published as a conference paper at ICLR 2022 distribution. The QFR approach enables neural networks to output highly expressive probabilistic predictions that make very little assumptions on the form of the predicted variable and that improve uncertainty estimates in probabilistic and Bayesian models. In the one-dimensional case, our approach yields quantile function regression and cumulative distribution function regression, two simple, general, and principled approaches for flexible probabilistic forecasting in regression.In addition, we demonstrate the benefits of AQFs on probabilistic modeling tasks that include density estimation and autoregressive generation. Across our sets of experiments, we observe improved performance, and we demonstrate properties of quantile flows that traditional flow models do not possess (e.g., sampling with flexible neural parameterizations).Contributions. In summary, this work (1) introduces new objectives for flow models that simplify the computation of determinants of Jacobians, which in turn greatly simplifies the implementation of flow models and extends the class of models that can be used to parameterize flows. We also (2) define autoregressive quantile flows based on this objective, and highlight new architectures supported by this framework. Finally, (3) we deploy AQFs as part of quantile flow regression, and show that this approach improves upon existing methods for predictive uncertainty estimation. | Published as a conference paper at ICLR 2022 AUTOREGRESSIVE QUANTILE FLOWS FOR PREDICTIVE UNCERTAINTY ESTIMATION |
d247958122 | The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes. To resolve this, recent works focus on developing weaker forms of supervision-e.g., hyperparameters discovered with a small number of group-labeled samples with spurious attribute annotation-but none of the methods retain comparable performance to methods using full supervision on the spurious attribute. In this paper, instead of searching for weaker supervisions, we ask: Given access to a fixed number of group-labeled samples, what is the best achievable worst-group loss if we "fully exploit" them? To this end, we propose a pseudo-attribute-based algorithm, coined Spread Spurious Attribute (SSA), for improving the worst-group accuracy. In particular, we leverage samples both with and without spurious attribute annotations to train a model predicting the spurious attribute, then use the pseudo-attribute predicted by the trained model as a supervision on the spurious attribute to train a new robust model having minimal worst-group loss. Our experiments on various benchmark datasets show that our algorithm consistently outperforms the baseline methods using the same number of group-labeled samples. We also demonstrate that the proposed SSA can achieve comparable performances to methods using full (100%) spurious attribute supervision, by using a much smaller number of group-labeled samples-from 0.6% and up to 1.5%, depending on the dataset. * Work done at KAIST arXiv:2204.02070v1 [cs.LG] 5 Apr 2022Published as a conference paper at ICLR 2022 Acknowledging this difficulty, recent works propose worst-group loss minimization algorithms that require a smaller number of group-labeled training samples (i.e., training samples with spurious attribute annotations)(Nam et al., 2020;Liu et al., 2021). At a high level, these works share a similar strategy(Fig. 1, Left): The methods first use a specialized mechanism to identify minority group samples among group-unlabeled training samples, and train a model in a way that puts more emphasis on identified-as-minority samples, e.g., by upweighting. A small set of group-labeled samples are used to tune hyperparameters of this procedure; asLiu et al. (2021)shows, the performance of trained models are very sensitive to these hyperparameters, indicating the high dependency of such algorithms on the availability of the group-labeled samples. Although these methods achieve higher worst-group accuracy than completely annotation-free approaches, e.g.,Sohoni et al. (2020), they fail to perform comparably to the algorithms which use full annotations on spurious attributes, e.g.,Sagawa et al. (2020). This performance gap gives rise to the following question: Can we closely achieve the full-annotation performance using a partially annotated dataset, if we use group-labeled samples more actively than hyperparameter-tuning?Contribution. This paper proposes a worst-group loss minimization algorithm, coined Spread Spurious Attribute (SSA). At a high level, SSA consists of two phases-pseudo-labeling and robust training-that are designed to make a full use out of the group-labeled samples(Fig. 1, Right): | SPREAD SPURIOUS ATTRIBUTE: IMPROVING WORST-GROUP ACCURACY WITH SPURI- OUS ATTRIBUTE ESTIMATION |
d226048947 | Partial differential equations (PDEs) are used to describe a variety of physical phenomena.Often these equations do not have analytical solutions and numerical approximations are used instead.One of the common methods to solve PDEs is the finite element method.Computing derivative information of the solution with respect to the input parameters is important in many tasks in scientific computing.We extend JAX automatic differentiation library with an interface to Firedrake finite element library.High-level symbolic representation of PDEs allows bypassing differentiating through low-level possibly many iterations of the underlying nonlinear solvers.Differentiating through Firedrake solvers is done using tangent-linear and adjoint equations.This enables the efficient composition of finite element solvers with arbitrary differentiable programs.The code is available at github.com/IvanYashchuk/jax-firedrake. | |
d204906218 | In this work, we propose a new method to integrate two recent lines of work: unsupervised induction of shallow semantics (e.g., semantic roles) and factorization of relations in text and knowledge bases.Our model consists of two components:(1) an encoding component: a semantic role labeling model which predicts roles given a rich set of syntactic and lexical features; (2) a reconstruction component: a tensor factorization model which relies on roles to predict argument fillers.When the components are estimated jointly to minimize errors in argument reconstruction, the induced roles largely correspond to roles defined in annotated resources.Our method performs on par with most accurate role induction methods on English, even though, unlike these previous approaches, we do not incorporate any prior linguistic knowledge about the language. | Under review as a workshop contribution at ICLR 2015 INDUCING SEMANTIC REPRESENTATION FROM TEXT BY JOINTLY PREDICTING AND FACTORIZING RELA-TIONS |
d231592851 | Predictive uncertainty estimation is an essential next step for the reliable deployment of deep object detectors in safety-critical tasks. In this work, we focus on estimating predictive distributions for bounding box regression output with variance networks. We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean. We propose to use the energy score as a non-local proper scoring rule and find that when used for training, the energy score leads to better calibrated and lower entropy predictive distributions than NLL. We also address the widespread use of non-proper scoring metrics for evaluating predictive distributions from deep object detectors by proposing an alternate evaluation approach founded on proper scoring rules. Using the proposed evaluation tools, we show that although variance networks can be used to produce high quality predictive distributions, adhoc approaches used by seminal object detectors for choosing regression targets during training do not provide wide enough data support for reliable variance learning. We hope that our work helps shift evaluation in probabilistic object detection to better align with predictive uncertainty evaluation in other machine learning domains. Code for all models, evaluation, and datasets is available at: https://github.com/asharakeh/probdet.git. distance-sensitive proper scoring rule based on energy statistics(Székely & Rizzo, 2013), as an alternative for training variance networks. We show that predictive distributions learnt with the energy score are lower entropy, better calibrated, and of higher quality when evaluated using proper scoring rules.Pitfalls of EvaluationWe address the widespread use of non-proper scoring rules for evaluating probabilistic object detectors by providing evaluation tools based on well established proper scoring rules(Gneiting & Raftery, 2007)that are only minimized if the estimated predictive distribution is equal to the true target distribution, for both classification and regression. Using the proposed tools, we benchmark probabilistic extensions of three common object detection architectures on in-distribution, shifted, and out-of-distribution data. Our results show that variance networks can differentiate between in-distribution, shifted, and out-of-distribution data using their predictive entropy. We find that ad-hoc approaches used by seminal object detectors for choosing their regression targets during training do not provide a wide enough data support for reliable learning in variance networks. Finally, we provide clear recommendations in Sec. 5 to avoid the pitfalls described above. | Published as a conference paper at ICLR 2021 ESTIMATING AND EVALUATING REGRESSION PREDIC- TIVE UNCERTAINTY IN DEEP OBJECT DETECTORS |
d213987212 | Mode connectivity provides novel geometric insights on analyzing loss landscapes and enables building high-accuracy pathways between well-trained neural networks. In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel methods for improving this robustness. Our experiments cover various types of adversarial attacks applied to different network architectures and datasets. When network models are tampered with backdoor or error-injection attacks, our results demonstrate that the path connection learned using limited amount of bonafide data can effectively mitigate adversarial effects while maintaining the original accuracy on clean data. Therefore, mode connectivity provides users with the power to repair backdoored or error-injected models. We also use mode connectivity to investigate the loss landscapes of regular and robust models against evasion attacks. Experiments show that there exists a barrier in adversarial robustness loss on the path connecting regular and adversarially-trained models. A high correlation is observed between the adversarial robustness loss and the largest eigenvalue of the input Hessian matrix, for which theoretical justifications are provided. Our results suggest that mode connectivity offers a holistic tool and practical means for evaluating and improving adversarial robustness 1 . | Published as a conference paper at ICLR 2020 BRIDGING MODE CONNECTIVITY IN LOSS LANDSCAPES AND ADVERSARIAL ROBUSTNESS |
d22014305 | Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router and a set of one or more function blocks. A function block may be any neural network -for example a fully-connected or a convolutional layer. Given an input the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively, terminating when a fixed recursion depth is reached. In this way the routing network dynamically composes different function blocks for each input. We employ a collaborative multi-agent reinforcement learning (MARL) approach to jointly train the router and function blocks. We evaluate our model against cross-stitch networks and shared-layer baselines on multi-task settings of the MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a significant improvement in accuracy, with sharper convergence. In addition, routing networks have nearly constant per-task training cost while cross-stitch networks scale linearly with the number of tasks. On CIFAR-100 (20 tasks) we obtain cross-stitch performance levels with an 85% reduction in training time. | ROUTING NETWORKS: ADAPTIVE SELECTION OF NON-LINEAR FUNCTIONS FOR MULTI-TASK LEARN- ING |
d221083147 | Although widely adopted, existing approaches for fine-tuning pre-trained language models have been shown to be unstable across hyper-parameter settings, motivating recent work on trust region methods. In this paper, we present a simplified and efficient method rooted in trust region theory that replaces previously used adversarial objectives with parametric noise (sampling from either a normal or uniform distribution), thereby discouraging representation change during fine-tuning when possible without hurting performance. We also introduce a new analysis to motivate the use of trust region methods more generally, by studying representational collapse; the degradation of generalizable representations from pre-trained models as they are fine-tuned for a specific end task. Extensive experiments show that our fine-tuning method matches or exceeds the performance of previous trust region methods on a range of understanding and generation tasks (including DailyMail/CNN, Gigaword, Reddit TIFU, and the GLUE benchmark), while also being much faster. We also show that it is less prone to representation collapse; the pre-trained models maintain more generalizable representations every time they are fine-tuned. | BETTER FINE-TUNING BY REDUCING REPRESENTA- TIONAL COLLAPSE |
d245117574 | Building embodied intelligent agents that can interact with 3D indoor environments has received increasing research attention in recent years. While most works focus on single-object or agent-object visual functionality and affordances, our work proposes to study a new kind of visual relationship that is also important to perceive and model -inter-object functional relationships (e.g., a switch on the wall turns on or off the light, a remote control operates the TV). Humans often spend little or no effort to infer these relationships, even when entering a new room, by using our strong prior knowledge (e.g., we know that buttons control electrical devices) or using only a few exploratory interactions in cases of uncertainty (e.g., multiple switches and lights in the same room). In this paper, we take the first step in building AI system learning inter-object functional relationships in 3D indoor environments with key technical contributions of modeling prior knowledge by training over large-scale scenes and designing interactive policies for effectively exploring the training scenes and quickly adapting to novel test scenes. We create a new benchmark based on the AI2Thor and PartNet datasets and perform extensive experiments that prove the effectiveness of our proposed method. Results show that our model successfully learns priors and fast-interactive-adaptation strategies for exploring inter-object functional relationships in complex 3D scenes. Several ablation studies further validate the usefulness of each proposed module. * Equal contribution. | IFR-EXPLORE: LEARNING INTER-OBJECT FUNC- TIONAL RELATIONSHIPS IN 3D INDOOR SCENES |
d239998683 | k-means clustering is a well-studied problem due to its wide applicability. Unfortunately, there exist strong theoretical limits on the performance of any algorithm for the k-means problem on worst-case inputs. To overcome this barrier, we consider a scenario where "advice" is provided to help perform clustering. Specifically, we consider the k-means problem augmented with a predictor that, given any point, returns its cluster label in an approximately optimal clustering up to some, possibly adversarial, error. We present an algorithm whose performance improves along with the accuracy of the predictor, even though naïvely following the accurate predictor can still lead to a high clustering cost. Thus if the predictor is sufficiently accurate, we can retrieve a close to optimal clustering with nearly optimal runtime, breaking known computational barriers for algorithms that do not have access to such advice. We evaluate our algorithms on real datasets and show significant improvements in the quality of clustering. * | Learning-Augmented k-means Clustering |
d54438210 | Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures.However, the prohibitive computational demand of conventional NAS algorithms (e.g. 10 4 GPU hours) makes it difficult to directly search the architectures on large-scale tasks (e.g.ImageNet).Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly w.r.t.candidate set size).As a result, they need to utilize proxy tasks, such as training on a smaller dataset, or learning with only a few blocks, or training just for a few epochs.These architectures optimized on proxy tasks are not guaranteed to be optimal on the target task.In this paper, we present ProxylessNAS that can directly learn the architectures for large-scale target tasks and target hardware platforms.We address the high memory consumption issue of differentiable NAS and reduce the computational cost (GPU hours and GPU memory) to the same level of regular training while still allowing a large candidate set.Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of directness and specialization.On CIFAR-10, our model achieves 2.08% test error with only 5.7M parameters, better than the previous state-of-the-art architecture AmoebaNet-B, while using 6× fewer parameters.On ImageNet, our model achieves 3.1% better top-1 accuracy than MobileNetV2, while being 1.2× faster with measured GPU latency.We also apply ProxylessNAS to specialize neural architectures for hardware with direct hardware metrics (e.g.latency) and provide insights for efficient CNN architecture design. 1 | |
d233025422 | Many real-world applications such as robotics provide hard constraints on power and compute that limit the viable model complexity of Reinforcement Learning (RL) agents. Similarly, in many distributed RL settings, acting is done on unaccelerated hardware such as CPUs, which likewise restricts model size to prevent intractable experiment run times. These "actor-latency" constrained settings present a major obstruction to the scaling up of model complexity that has recently been extremely successful in supervised learning. To be able to utilize large model capacity while still operating within the limits imposed by the system during acting, we develop an "Actor-Learner Distillation" (ALD) procedure that leverages a continual form of distillation that transfers learning progress from a large capacity learner model to a small capacity actor model. As a case study, we develop this procedure in the context of partially-observable environments, where transformer models have had large improvements over LSTMs recently, at the cost of significantly higher computational complexity. With transformer models as the learner and LSTMs as the actor, we demonstrate in several challenging memory environments that using Actor-Learner Distillation recovers the clear sample-efficiency gains of the transformer learner model while maintaining the fast inference and reduced total training time of the LSTM actor model. | Published as a conference paper at ICLR 2021 EFFICIENT TRANSFORMERS IN REINFORCEMENT LEARNING USING ACTOR-LEARNER DISTILLATION |
d247315339 | In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small subset of past data is stored and replayed alongside new data, has emerged as a simple and effective learning strategy. In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones. We shed new light on this question by showing that applying ER causes the newly added classes' representations to overlap significantly with the previous classes, leading to highly disruptive parameter updates. Based on this empirical analysis, we propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes. We show that using an asymmetric update rule pushes new classes to adapt to the older ones (rather than the reverse), which is more effective especially at task boundaries, where much of the forgetting typically occurs. Empirical results show significant gains over strong baselines on standard continual learning benchmarks 1 | Published as a conference paper at ICLR 2022 NEW INSIGHTS ON REDUCING ABRUPT REPRESENTA- TION CHANGE IN ONLINE CONTINUAL LEARNING |
d252846076 | MuZero Unplugged presents a promising approach for offline policy learning from logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned model and leverages Reanalyze algorithm to learn purely from offline data. For good performance, MCTS requires accurate learned models and a large number of simulations, thus costing huge computing time. This paper investigates a few hypotheses where MuZero Unplugged may not work well under the offline RL settings, including 1) learning with limited data coverage; 2) learning from offline data of stochastic environments; 3) improperly parameterized models given the offline data; 4) with a low compute budget. We propose to use a regularized one-step look-ahead approach to tackle the above issues. Instead of planning with the expensive MCTS, we use the learned model to construct an advantage estimation based on a one-step rollout. Policy improvements are towards the direction that maximizes the estimated advantage with regularization of the dataset. We conduct extensive empirical studies with BSuite environments to verify the hypotheses and then run our algorithm on the RL Unplugged Atari benchmark. Experimental results show that our proposed approach achieves stable performance even with an inaccurate learned model. On the large-scale Atari benchmark, the proposed method outperforms MuZero Unplugged by 43%. Most significantly, it uses only 5.6% wall-clock time (i.e., 1 hour) compared to MuZero Unplugged (i.e., 17.8 hours) to achieve a 150% IQM normalized score with the same hardware and software stacks. Our implementation is open-sourced at https://github.com/ | Published as a conference paper at ICLR 2023 EFFICIENT OFFLINE POLICY OPTIMIZATION WITH A LEARNED MODEL |
d253098754 | Weather and climate simulations produce petabytes of high-resolution data that are later analyzed by researchers in order to understand climate change or severe weather. We propose a new method of compressing this multidimensional weather and climate data: a coordinate-based neural network is trained to overfit the data, and the resulting parameters are taken as a compact representation of the original grid-based data. While compression ratios range from 300× to more than 3,000×, our method outperforms the state-of-the-art compressor SZ3 in terms of weighted RMSE, MAE. It can faithfully preserve important large scale atmosphere structures and does not introduce significant artifacts. When using the resulting neural network as a 790× compressed dataloader to train the WeatherBench forecasting model, its RMSE increases by less than 2%. The three orders of magnitude compression democratizes access to high-resolution climate data and enables numerous new research directions. | Published as a conference paper at ICLR 2023 COMPRESSING MULTIDIMENSIONAL WEATHER AND CLIMATE DATA INTO NEURAL NETWORKS |
d210839718 | Batch Normalization (BN) is one of the most widely used techniques in Deep Learning field. But its performance can awfully degrade with insufficient batch size. This weakness limits the usage of BN on many computer vision tasks like detection or segmentation, where batch size is usually small due to the constraint of memory consumption. Therefore many modified normalization techniques have been proposed, which either fail to restore the performance of BN completely, or have to introduce additional nonlinear operations in inference procedure and increase huge consumption. In this paper, we reveal that there are two extra batch statistics involved in backward propagation of BN, on which has never been well discussed before. The extra batch statistics associated with gradients also can severely affect the training of deep neural network. Based on our analysis, we propose a novel normalization method, named Moving Average Batch Normalization (MABN). MABN can completely restore the performance of vanilla BN in small batch cases, without introducing any additional nonlinear operations in inference procedure. We prove the benefits of MABN by both theoretical analysis and experiments. Our experiments demonstrate the effectiveness of MABN in multiple computer vision tasks including ImageNet and COCO. The code has been released in https://github.com/megvii-model/MABN. | TOWARDS STABILIZING BATCH STATISTICS IN BACK- WARD PROPAGATION OF BATCH NORMALIZATION |
d253098208 | Sampling from a target measure whose density is only known up to a normalization constant is a fundamental problem in computational statistics and machine learning. In this paper, we present a new optimization-based method for sampling called mollified interaction energy descent (MIED). MIED minimizes a new class of energies on probability measures called mollified interaction energies (MIEs). These energies rely on mollifier functions-smooth approximations of the Dirac delta originated from PDE theory. We show that as the mollifier approaches the Dirac delta, the MIE converges to the chi-square divergence with respect to the target measure and the minimizers of MIE converge to the target measure. Optimizing this energy with proper discretization yields a practical firstorder particle-based algorithm for sampling in both unconstrained and constrained domains. We show experimentally that for unconstrained sampling problems, our algorithm performs on par with existing particle-based algorithms like SVGD, while for constrained sampling problems our method readily incorporates constrained optimization techniques to handle more flexible constraints with strong performance compared to alternatives.We present an optimization-based method called mollified interaction energy descent (MIED) that minimizes mollified interaction energies (MIEs) for both unconstrained and constrained sampling. An MIE takes the form of a double integral of the quotient of a mollifier-smooth approximation of δ 0 , the Dirac delta at the origin-over the target density properly scaled. Intuitively, minimizing an MIE balances two types of forces: attractive forces that drive the current measure towards the target density, and repulsive forces from the mollifier that prevents collapsing. We show that as the mollifier converges to δ 0 , the MIE converges to the χ 2 divergence to the target measure up to an additive constant(Theorem 3.3). Moreover, the MIE Γ-converges to χ 2 divergence (Theorem 3.6), so that minimizers of MIEs converge to the target measure, providing a theoretical basis for sampling by minimizing MIE.Kwangjun Ahn and Sinho Chewi. Efficient constrained sampling via the mirror-langevin algorithm. . Towards a theory of non-log-concave sampling: first-order stationarity guarantees for langevin monte carlo. | Published as a conference paper at ICLR 2023 SAMPLING WITH MOLLIFIED INTERACTION ENERGY DESCENT |
d257804924 | Image steganography is the process of concealing secret information in images through imperceptible changes. Recent work has formulated this task as a classic constrained optimization problem. In this paper, we argue that image steganography is inherently performed on the (elusive) manifold of natural images, and propose an iterative neural network trained to perform the optimization steps. In contrast to classical optimization methods like L-BFGS or projected gradient descent, we train the neural network to also stay close to the manifold of natural images throughout the optimization. We show that our learned neural optimization is faster and more reliable than classical optimization approaches. In comparison to previous state-of-the-art encoder-decoder based steganography methods, it reduces the recovery error rate by multiple orders of magnitude and achieves zero error up to 3 bits per pixel (bpp) without the need for error-correcting codes. * Equal contribution.Published as a conference paper at ICLR 2023 each pixel is optimized in isolation, and pixel-level constraints only ensure that the steganographic image stays close to the input image according to an algebraic norm, rather than along the natural image manifold. Although similar manifold-unaware approaches are successfully deployed for adversarial attacks, steganography aims to precisely control millions of binary decoder outputs, instead of a single class prediction; the resulting optimization problem is thus harder and prone to producing unnatural-looking images.Steganographic ImagePerturbationMessage Decoding ErrorError: 50% Error: 0.46% | Published as a conference paper at ICLR 2023 LEARNING ITERATIVE NEURAL OPTIMIZERS FOR IM- AGE STEGANOGRAPHY |
d252595921 | Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP). However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data. To fill the gap, we present Tabular Math Word Problems (TABMWP), a new dataset containing 38,431 open-domain grade-level problems that require mathematical reasoning on both textual and tabular data. Each question in TABMWP is aligned with a tabular context, which is presented as an image, semi-structured text, and a structured table. There are two types of questions: free-text and multichoice, and each problem is annotated with gold solutions to reveal the multi-step reasoning process. We evaluate different pre-trained models on TABMWP, including the GPT-3 model in a few-shot setting. As earlier studies suggest, since few-shot GPT-3 relies on the selection of in-context examples, its performance is unstable and can degrade to near chance. The unstable issue is more severe when handling complex problems like TABMWP. To mitigate this, we further propose a novel approach, PROMPTPG, which utilizes policy gradient to learn to select in-context examples from a small amount of training data and then constructs the corresponding prompt for the test example. Experimental results show that our method outperforms the best baseline by 5.31% on the accuracy metric and reduces the prediction variance significantly compared to random selection, which verifies its effectiveness in selecting in-context examples. 1 | Published as a conference paper at ICLR 2023 DYNAMIC PROMPT LEARNING VIA POLICY GRADIENT FOR SEMI-STRUCTURED MATHEMATICAL REASONING |
d257378217 | Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstream tasks of interest. However, different downstream tasks require different invariances for their best performance, so the optimal choice of augmentations for SSL depends on the target task. In this paper, we aim to learn self-supervised features that generalize well across a variety of downstream tasks (e.g., object classification, detection and instance segmentation) without knowing any task information beforehand. We do so by Masked Augmentation Subspace Training (or MAST) to encode in the single feature space the priors from different data augmentations in a factorized way. Specifically, we disentangle the feature space into separate subspaces, each induced by a learnable mask that selects relevant feature dimensions to model invariance to a specific augmentation. We show the success of MAST in jointly capturing generalizable priors from different augmentations, using both unique and shared features across the subspaces. We further show that MAST benefits from uncertainty modeling to reweight ambiguous samples from strong augmentations that may cause similarity mismatch in each subspace. Experiments demonstrate that MAST consistently improves generalization on various downstream tasks, while being task-agnostic and efficient during SSL. We also provide interesting insights about how different augmentations are related and how uncertainty reflects learning difficulty. | MAST: MASKED AUGMENTATION SUBSPACE TRAIN- ING FOR GENERALIZABLE SELF-SUPERVISED PRIORS |
d249889641 | In this paper we show how to achieve state-of-the-art certified adversarial robustness to 2 -norm bounded perturbations by relying exclusively on off-the-shelf pretrained models. To do so, we instantiate the denoised smoothing approach of Salman et al. (2020) by combining a pretrained denoising diffusion probabilistic model and a standard high-accuracy classifier. This allows us to certify 71% accuracy on ImageNet under adversarial perturbations constrained to be within an 2 norm of ε = 0.5, an improvement of 14 percentage points over the prior certified SoTA using any approach, or an improvement of 30 percentage points over denoised smoothing. We obtain these results using only pretrained diffusion models and image classifiers, without requiring any fine tuning or retraining of model parameters. * Joint first authors | Published as a conference paper at ICLR 2023 |
d252683970 | A large class of hyperbolic and advection-dominated PDEs can have solutions with discontinuities. This paper investigates, both theoretically and empirically, the operator learning of PDEs with discontinuous solutions. We rigorously prove, in terms of lower approximation bounds, that methods which entail a linear reconstruction step (e.g. DeepONet or PCA-Net) fail to efficiently approximate the solution operator of such PDEs. In contrast, we show that certain methods employing a nonlinear reconstruction mechanism can overcome these fundamental lower bounds and approximate the underlying operator efficiently. The latter class includes Fourier Neural Operators and a novel extension of DeepONet termed shift-DeepONet. Our theoretical findings are confirmed by empirical results for advection equation, inviscid Burgers' equation and compressible Euler equations of aerodynamics. | NONLINEAR RECONSTRUCTION FOR OPERATOR LEARNING OF PDES WITH DISCONTINUITIES A PREPRINT |
d211126472 | Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up predictions is to assume independence between interacting time series. However, modeling statistical dependencies can improve accuracy and enable analysis of interaction effects. Deep learning methods are well suited for this problem, but multi-variate models often assume a simple parametric distribution and do not scale to high dimensions. In this work we model the multi-variate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow. This combination retains the power of autoregressive models, such as good performance in extrapolation into the future, with the flexibility of flows as a general purpose high-dimensional distribution model, while remaining computationally tractable. We show that it improves over the state-of-the-art for standard metrics on many real-world data sets with several thousand interacting time-series. | Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows |
d247450683 | State-of-the-art maximum entropy models for texture synthesis are built from statistics relying on image representations defined by convolutional neural networks (CNN). Such representations capture rich structures in texture images, outperforming wavelet-based representations in this regard. However, conversely to neural networks, wavelets offer meaningful representations, as they are known to detect structures at multiple scales (e.g. edges) in images. In this work, we propose a family of statistics built upon non-linear wavelet based representations, that can be viewed as a particular instance of a one-layer CNN, using a generalized rectifier non-linearity. These statistics significantly improve the visual quality of previous classical wavelet-based models, and allow one to produce syntheses of similar quality to state-of-the-art models, on both gray-scale and color textures. We further provide insights on memorization effects in these models. . Phase harmonic correlations and convolutional neural networks. Information and Inference: A Journal of the IMA, 9(3):721-747, 2020. Jorge Nocedal. Updating quasi-newton matrices with limited storage. Mathematics of computation, 35(151):773-782, 1980.Javier Portilla and Eero P Simoncelli. A parametric texture model based on joint statistics of complex wavelet coefficients. survey of exemplar-based texture synthesis. | Published as a conference paper at ICLR 2022 GENERALIZED RECTIFIER WAVELET COVARIANCE MODELS FOR TEXTURE SYNTHESIS |
d165163737 | Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwise adaptive learning rates trains RESNET on ImageNet in a few minutes. However, LARS performs poorly for attention models like BERT, indicating that its performance gains are not consistent across tasks. In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches. Using this strategy, we develop a new layerwise adaptive large batch optimization technique called LAMB; we then provide convergence analysis of LAMB as well as LARS, showing convergence to a stationary point in general nonconvex settings. Our empirical results demonstrate the superior performance of LAMB across various tasks such as BERT and RESNET-50 training with very little hyperparameter tuning. In particular, for BERT training, our optimizer enables use of very large batch sizes of 32868 without any degradation of performance. By increasing the batch size to the memory limit of a TPUv3 Pod, BERT training time can be reduced from 3 days to just 76 minutes (Table 1). The LAMB implementation is available online 1 . arXiv:1904.00962v5 [cs.LG] 3 Jan 2020 Yoshua Bengio. Practical recommendations for gradient-based training of deep architectures. In Neural networks: . signsgd: compressed optimisation for non-convex problems. CoRR, abs/1802.04434, 2018. Valeriu Codreanu, Damian Podareanu, and Vikram Saletore. Scale out for large minibatch sgd: Residual network training on imagenet-1k with improved accuracy and reduced time to train. arXiv preprint arXiv:1711.04291, 2017. : Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. Timothy Dozat. Incorporating nesterov momentum into adam. 2016. Saeed Ghadimi and Guanghui Lan. Stochastic first-and zeroth-order methods for nonconvex stochastic programming. -batch stochastic approximation methods for nonconvex stochastic composite optimization. Mathematical Programming, 155(1-2):267-305, 2014.Elad Hoffer, Itay Hubara, and Daniel Soudry. Train longer, generalize better: closing the generalization gap in large batch training of neural networks. arXiv preprint arXiv:1705.08741, 2017. | Published as a conference paper at ICLR 2020 LARGE BATCH OPTIMIZATION FOR DEEP LEARNING: TRAINING BERT IN 76 MINUTES |
d245144350 | A wide variety of deep generative models has been developed in the past decade. Yet, these models often struggle with simultaneously addressing three key requirements including: high sample quality, mode coverage, and fast sampling. We call the challenge imposed by these requirements the generative learning trilemma, as the existing models often trade some of them for others. Particularly, denoising diffusion models have shown impressive sample quality and diversity, but their expensive sampling does not yet allow them to be applied in many real-world applications. In this paper, we argue that slow sampling in these models is fundamentally attributed to the Gaussian assumption in the denoising step which is justified only for small step sizes. To enable denoising with large steps, and hence, to reduce the total number of denoising steps, we propose to model the denoising distribution using a complex multimodal distribution. We introduce denoising diffusion generative adversarial networks (denoising diffusion GANs) that model each denoising step using a multimodal conditional GAN. Through extensive evaluations, we show that denoising diffusion GANs obtain sample quality and diversity competitive with original diffusion models while being 2000× faster on the CIFAR-10 dataset. Compared to traditional GANs, our model exhibits better mode coverage and sample diversity. To the best of our knowledge, denoising diffusion GAN is the first model that reduces sampling cost in diffusion models to an extent that allows them to be applied to real-world applications inexpensively. Project page and code: https://nvlabs.github.io/denoising-diffusion-gan. * Work done during an internship at NVIDIA. | Published as a conference paper at ICLR 2022 TACKLING THE GENERATIVE LEARNING TRILEMMA WITH DENOISING DIFFUSION GANS |
d9665723 | Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of Im-ageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties. In this work we present a fast and simple data-dependent initialization procedure, that sets the weights of a network such that all units in the network train at roughly the same rate, avoiding vanishing or exploding gradients. Our initialization matches the current state-of-the-art unsupervised or self-supervised pre-training methods on standard computer vision tasks, such as image classification and object detection, while reducing the pre-training time by three orders of magnitude. When combined with pre-training methods, our initialization significantly outperforms prior work, narrowing the gap between supervised and unsupervised pre-training.Published as a conference paper at ICLR 2016 same amount. While these studies did indeed set the learning rate to be the same for all layers, somewhat counterintuitively this does not actually enforce that all layers learn at the same rate. To see this, say we have a network where there are two convolution layers separated by a ReLU. Multiplying the weights and bias term of the first layer by a scalar α > 0, and then dividing the weights (but not bias) of the next (higher) layer by the same constant α will result in a network which computes exactly the same function. However, note that the gradients of the two layers are not the same: they will be divided by α for the first layer, and multiplied by α for the second. Worse, an update of a given magnitude will have a smaller effect on the lower layer than the higher layer, simply because the lower layer's norm is now larger. Using this kind of reparameterization, it is easy to make the gradients for certain layers vanish during fine-tuning, or even to make them explode, resulting in a network that is impossible to fine-tune despite representing exactly the same function. Conversely, this sort of re-parameterization gives us a tool we can use to calibrate layer-by-layer learning to improve fine-tuning performance, provided we have an appropriate principle for making such adjustments. | DATA-DEPENDENT INITIALIZATIONS OF CONVOLUTIONAL NEURAL NETWORKS |
d52897771 | Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly controlling the agents to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agents) which have their own minds (preferences, intentions, skills, etc.) and can not be dictated to perform tasks they do not wish to do. For achieving optimal coordination among these agents, we train a super agent (i.e., the manager) to manage them by first inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses so that they will agree to work together. The objective of the manager is maximizing the overall productivity as well as minimizing payments made to the workers for ad-hoc worker teaming. To train the manager, we propose Mind-aware Multi-agent Management Reinforcement Learning (M 3 RL), which consists of agent modeling and policy learning. We have evaluated our approach in two environments, Resource Collection and Crafting, to simulate multi-agent management problems with various task settings and multiple designs for the worker agents. The experimental results have validated the effectiveness of our approach in modeling worker agents' minds online, and in achieving optimal ad-hoc teaming with good generalization and fast adaptation. * Work done while interning at Facebook AI Research. | M 3 RL: MIND-AWARE MULTI-AGENT MANAGEMENT REINFORCEMENT LEARNING |
d202750335 | It has been widely recognized that adversarial examples can be easily crafted to fool deep networks, which mainly root from the locally unreasonable behavior nearby input examples. Applying mixup in training provides an effective mechanism to improve generalization performance and model robustness against adversarial perturbations, which introduces the globally linear behavior in-between training examples. However, in previous work, the mixup-trained models only passively defend adversarial attacks in inference by directly classifying the inputs, where the induced global linearity is not well exploited. Namely, since the locality of the adversarial perturbations, it would be more efficient to actively break the locality via the globality of the model predictions. Inspired by simple geometric intuition, we develop an inference principle, named mixup inference (MI), for mixup-trained models. MI mixups the input with other random clean samples, which can shrink and transfer the equivalent perturbation if the input is adversarial. Our experiments on CIFAR-10 and CIFAR-100 demonstrate that MI can further improve the adversarial robustness for the models trained by mixup and its variants. | Published as a conference paper at ICLR 2020 MIXUP INFERENCE: BETTER EXPLOITING MIXUP TO DEFEND ADVERSARIAL ATTACKS |
d67856088 | A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations useful for subsequent tasks will arise as a side effect. In this work, we propose instead to directly target later desired tasks by meta-learning an unsupervised learning rule which leads to representations useful for those tasks. Specifically, we target semi-supervised classification performance, and we metalearn an algorithm -an unsupervised weight update rule -that produces representations useful for this task. Additionally, we constrain our unsupervised update rule to a be a biologically-motivated, neuron-local function, which enables it to generalize to different neural network architectures, datasets, and data modalities. We show that the meta-learned update rule produces useful features and sometimes outperforms existing unsupervised learning techniques. We further show that the meta-learned unsupervised update rule generalizes to train networks with different widths, depths, and nonlinearities. It also generalizes to train on data with randomly permuted input dimensions and even generalizes from image datasets to a text task.Published as a conference paper at ICLR 2019 we learn a transferable learning rule which does not require access to labels and generalizes across both data domains and neural network architectures. Although we focus on the meta-objective of semi-supervised classification here, in principle a learning rule could be optimized to generate representations for any subsequent task.RELATED WORKUNSUPERVISED REPRESENTATION LEARNINGUnsupervised learning is a topic of broad and diverse interest. Here we briefly review several techniques that can lead to a useful latent representation of a dataset. In contrast to our work, each method imposes a manually defined training algorithm or loss function whereas we learn the algorithm that creates useful representations as determined by a meta-objective.Autoencoders (Hinton and Salakhutdinov, 2006) work by first compressing and optimizing reconstruction loss. Extensions have been made to de-noise data (Vincent et al., 2008; 2010), as well as compress information in an information theoretic way(Kingma and Welling, 2013). Le et al. (2011) further explored scaling up these unsupervised methods to large image datasets. Generative adversarial networks (Goodfellow et al., 2014) take another approach to unsupervised feature learning. Instead of a loss function, an explicit min-max optimization is defined to learn a generative model of a data distribution. Recent work has shown that this training procedure can learn unsupervised features useful for few shot learning (Radford et al., 2015; Donahue et al., 2016; Dumoulin et al., 2016).Other techniques rely on self-supervision where labels are easily generated to create a non-trivial 'supervised' loss. Domain knowledge of the input is often necessary to define these losses. Noroozi and Favaro(2016)use unscrambling jigsaw-like crops of an image. Techniques used by Misra et al. (2016) and Sermanet et al. (2017) rely on using temporal ordering from videos. Another approach to unsupervised learning relies on feature space design such as clustering. Coates and Ng (2012) showed that k-means can be used for feature learning. Xie et al. (2016) jointly learn features and cluster assignments. Bojanowski and Joulin (2017) develop a scalable technique to cluster by predicting noise. Other techniques such as Schmidhuber(1992),Hochreiter andSchmidhuber (1999), and Olshausen and Field (1997) define various desirable properties about the latent representation of the input, such as predictability, complexity of encoding mapping, independence, or sparsity, and optimize to achieve these properties.META LEARNINGMost meta-learning algorithms consist of two levels of learning, or 'loops' of computation: an inner loop, where some form of learning occurs (e.g. an optimization process), and an outer loop or metatraining loop, which optimizes some aspect of the inner loop, parameterized by meta-parameters. The performance of the inner loop computation for a given set of meta-parameters is quantified by a meta-objective. Meta-training is then the process of adjusting the meta-parameters so that the inner loop performs well on this meta-objective. Meta-learning approaches differ by the computation performed in the inner loop, the domain, the choice of meta-parameters, and the method of optimizing the outer loop.Some of the earliest work in meta-learning includes work by Schmidhuber (1987), which explores a variety of meta-learning and self-referential algorithms. Similarly to our algorithm, Bengio et al. (1990; 1992) propose to learn a neuron local learning rule, though their approach differs in task and problem formulation. Additionally, Runarsson and Jonsson (2000) meta-learn supervised learning rules which mix local and global network information. A number of papers propose meta-learning for few shot learning (Vinyals et al., 2016; Ravi and Larochelle, 2016; Mishra et al., 2017; Finn et al., 2017; Snell et al., 2017), though these do not take advantage of unlabeled data. Others make use of both labeled and unlabeld data (Ren et al., 2018). Hsu et al. (2018) uses a task created with no supervision to then train few-shot detectors. Garg (2018) use meta-learning for unsupervised learning, primarily in the context of clustering and with a small number of meta-parameters. Juergen Schmidhuber. On learning how to learn learning strategies. 1995.Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks.science, 313(5786): 504-507, 2006. | META-LEARNING UPDATE RULES FOR UNSUPER- VISED REPRESENTATION LEARNING |
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