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d256459300 | Intelligent biological systems are characterized by their embodiment in a complex environment and the intimate interplay between their nervous systems and the nonlinear mechanical properties of their bodies. This coordination, in which the dynamics of the motor system co-evolved to reduce the computational burden on the brain, is referred to as "mechanical intelligence" or "morphological computation". In this work, we seek to develop machine learning analogs of this process, in which we jointly learn the morphology of complex nonlinear elastic solids along with a deep neural network to control it. By using a specialized differentiable simulator of elastic mechanics coupled to conventional deep learning architectureswhich we refer to as neuromechanical autoencoders-we are able to learn to perform morphological computation via gradient descent. Key to our approach is the use of mechanical metamaterials-cellular solids, in particular-as the morphological substrate. Just as deep neural networks provide flexible and massivelyparametric function approximators for perceptual and control tasks, cellular solid metamaterials are promising as a rich and learnable space for approximating a variety of actuation tasks. In this work we take advantage of these complementary computational concepts to co-design materials and neural network controls to achieve nonintuitive mechanical behavior. We demonstrate in simulation how it is possible to achieve translation, rotation, and shape matching, as well as a "digital MNIST" task. We additionally manufacture and evaluate one of the designs to verify its real-world behavior. | Published as a conference paper at ICLR 2023 NEUROMECHANICAL AUTOENCODERS: LEARNING TO COUPLE ELASTIC AND NEURAL NETWORK NONLINEARITY |
d236134091 | Can artificial agents learn to assist others in achieving their goals without knowing what those goals are? Generic reinforcement learning agents could be trained to behave altruistically towards others by rewarding them for altruistic behaviour, i.e., rewarding them for benefiting other agents in a given situation. Such an approach assumes that other agents' goals are known so that the altruistic agent can cooperate in achieving those goals. However, explicit knowledge of other agents' goals is often difficult to acquire. In the case of human agents, their goals and preferences may be difficult to express fully; they might be ambiguous or even contradictory. Thus, it is beneficial to develop agents that do not depend on external supervision and learn altruistic behaviour in a task-agnostic manner. We propose to act altruistically towards other agents by giving them more choice and allowing them to achieve their goals better. Some concrete examples include opening a door for others or safeguarding them to pursue their objectives without interference. We formalize this concept and propose an altruistic agent that learns to increase the choices another agent has by preferring to maximize the number of states that the other agent can reach in its future. We evaluate our approach in three different multi-agent environments where another agent's success depends on altruistic behaviour. Finally, we show that our unsupervised agents can perform comparably to agents explicitly trained to work cooperatively, in some cases even outperforming them. | LEARNING ALTRUISTIC BEHAVIOURS IN REINFORCE- MENT LEARNING WITHOUT EXTERNAL REWARDS |
d247315206 | We present Sequential Neural Variational Inference (SNVI), an approach to perform Bayesian inference in models with intractable likelihoods. SNVI combines likelihood-estimation (or likelihood-ratio-estimation) with variational inference to achieve a scalable simulation-based inference approach. SNVI maintains the flexibility of likelihood(-ratio) estimation to allow arbitrary proposals for simulations, while simultaneously providing a functional estimate of the posterior distribution without requiring MCMC sampling. We present several variants of SNVI and demonstrate that they are substantially more computationally efficient than previous algorithms, without loss of accuracy on benchmark tasks. We apply SNVI to a neuroscience model of the pyloric network in the crab and demonstrate that it can infer the posterior distribution with one order of magnitude fewer simulations than previously reported. SNVI vastly reduces the computational cost of simulationbased inference while maintaining accuracy and flexibility, making it possible to tackle problems that were previously inaccessible. | Published as a conference paper at ICLR 2022 VARIATIONAL METHODS FOR SIMULATION-BASED INFERENCE |
d15876696 | We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discriminator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the discriminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images. | ENERGY-BASED GENERATIVE ADVERSARIAL NET- WORKS |
d224705257 | The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependence to the solution. Thus, they learn an entire family of PDEs, in contrast to classical methods which solve one instance of the equation. In this work, we formulate a new neural operator by parameterizing the integral kernel directly in Fourier space, allowing for an expressive and efficient architecture. We perform experiments on Burgers' equation, Darcy flow, and Navier-Stokes equation. The Fourier neural operator is the first ML-based method to successfully model turbulent flows with zero-shot super-resolution. It is up to three orders of magnitude faster compared to traditional PDE solvers. Additionally, it achieves superior accuracy compared to previous learning-based solvers under fixed resolution. | Published as a conference paper at ICLR 2021 FOURIER NEURAL OPERATOR FOR PARAMETRIC PARTIAL DIFFERENTIAL EQUATIONS |
d252762429 | Deep Reinforcement Learning (RL) has emerged as a powerful paradigm for training neural policies to solve complex control tasks. However, these policies tend to be overfit to the exact specifications of the task and environment they were trained on, and thus do not perform well when conditions deviate slightly or when composed hierarchically to solve even more complex tasks. Recent work has shown that training a mixture of policies, as opposed to a single one, that are driven to explore different regions of the state-action space can address this shortcoming by generating a diverse set of behaviors, referred to as skills, that can be collectively used to great effect in adaptation tasks or for hierarchical planning. This is typically realized by including a diversity term -often derived from information theory -in the objective function optimized by RL. However these approaches often require careful hyperparameter tuning to be effective. In this work, we demonstrate that less widely-used neuroevolution methods, specifically Quality Diversity (QD), are a competitive alternative to information-theory-augmented RL for skill discovery. Through an extensive empirical evaluation comparing eight state-of-the-art algorithms (four flagship algorithms from each line of work) on the basis of (i) metrics directly evaluating the skills' diversity, (ii) the skills' performance on adaptation tasks, and (iii) the skills' performance when used as primitives for hierarchical planning; QD methods are found to provide equal, and sometimes improved, performance whilst being less sensitive to hyperparameters and more scalable. As no single method is found to provide near-optimal performance across all environments, there is a rich scope for further research which we support by proposing future directions and providing optimized open-source implementations. res. Explore, discover and learn: Unsupervised discovery of state-covering skills. . Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents. In Advances in Neural Information Processing Systems, pp. 5027-5038, 2018.Antoine Cully. Autonomous skill discovery with quality-diversity and unsupervised descriptors. In , et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature, 602(7897):414-419, 2022.Gaurav Dixit, Everardo Gonzalez, and Kagan Tumer. Diversifying behaviors for learning in asymmetric multiagent systems. In | Published as a conference paper at ICLR 2023 NEUROEVOLUTION IS A COMPETITIVE ALTERNATIVE TO REINFORCEMENT LEARNING FOR SKILL DISCOVERY |
d9794990 | Neural network models have a reputation for being black boxes. We propose a new method to understand better the roles and dynamics of the intermediate layers. This has direct consequences on the design of such models and it enables the expert to be able to justify certain heuristics (such as the auxiliary heads in the Inception model). Our method uses linear classifiers, referred to as "probes", where a probe can only use the hidden units of a given intermediate layer as discriminating features. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. They allow the user to visualize the state of the model at multiple steps of training. We demonstrate how this can be used to develop a better intuition about a known model and to diagnose potential problems. * Yoshua Bengio is a senior CIFAR Fellow | Understanding intermediate layers using linear classifier probes |
d233714921 | The lottery ticket hypothesis questions the role of overparameterization in supervised deep learning. But how is the performance of winning lottery tickets affected by the distributional shift inherent to reinforcement learning problems? In this work, we address this question by comparing sparse agents who have to address the non-stationarity of the exploration-exploitation problem with supervised agents trained to imitate an expert. We show that feed-forward networks trained with behavioural cloning compared to reinforcement learning can be pruned to higher levels of sparsity without performance degradation. This suggests that in order to solve the RL problem agents require more degrees of freedom. Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in both learning paradigms can be attributed to the identified mask rather than the weight initialization. The input layer mask selectively prunes entire input dimensions that turn out to be irrelevant for the task at hand. At a moderate level of sparsity the mask identified by iterative magnitude pruning yields minimal task-relevant representations, i.e., an interpretable inductive bias. Finally, we propose a simple initialization rescaling which promotes the robust identification of sparse task representations in low-dimensional control tasks. | Published as a conference paper at ICLR 2022 ON LOTTERY TICKETS AND MINIMAL TASK REPRE- SENTATIONS IN DEEP REINFORCEMENT LEARNING |
d254247299 | Differentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the two axes are possible and provide affirmative answers leveraging two instantiations of group-wise clipping. To reduce the compute time overhead of private learning, we show that per-layer clipping, where the gradient of each neural network layer is clipped separately, allows clipping to be performed in conjunction with backpropagation in differentially private optimization. This results in private learning that is as memory-efficient and almost as fast per training update as non-private learning for many workflows of interest. While per-layer clipping with constant thresholds tends to underperform standard flat clipping, per-layer clipping with adaptive thresholds matches or outperforms flat clipping under given training epoch constraints, hence attaining similar or better task performance within less wall time. To explore the limits of scaling (pretrained) models in differentially private deep learning, we privately fine-tune the 175 billion-parameter GPT-3. We bypass scaling challenges associated with clipping gradients that are distributed across multiple devices with per-device clipping that clips the gradient of each model piece separately on its host device. Privately fine-tuning GPT-3 with perdevice clipping achieves a task performance at = 1 better than what is attainable by non-privately fine-tuning the largest GPT-2 on a summarization task. arXiv:2212.01539v1 [cs.LG] 3 Dec 2022 mechanism for large scale NLU models. Deep models under the gan: information leakage from collaborative deep learning. In , et al. Learning and evaluating a differentially private pre-trained language model. In | EXPLORING THE LIMITS OF DIFFERENTIALLY PRI- VATE DEEP LEARNING WITH GROUP-WISE CLIPPING |
d127986220 | One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising the model input (e.g., an image) to maximally activate specific neurons. However, this requires a careful selection of hyper-parameters to generate interpretable examples for each neuron of interest, and current methods rely on a manual, qualitative evaluation of each setting, which is prohibitively slow. We introduce a new metric that uses Fréchet Inception Distance (FID) to encourage similarity between model activations for real and generated data. This provides an efficient way to evaluate a set of generated examples for each setting of hyper-parameters. We also propose a novel GAN-based method for generating explanations that enables an efficient search through the input space and imposes a strong prior favouring realistic outputs. We apply our approach to a classification model trained to predict whether a music audio recording contains singing voice. Our results suggest that this proposed metric successfully selects hyper-parameters leading to interpretable examples, avoiding the need for manual evaluation. Moreover, we see that examples synthesised to maximise or minimise the predicted probability of singing voice presence exhibit vocal or non-vocal characteristics, respectively, suggesting that our approach is able to generate suitable explanations for understanding concepts learned by a neural network. | GAN-BASED GENERATION AND AUTOMATIC SELEC- TION OF EXPLANATIONS FOR NEURAL NETWORKS |
d235593394 | The study of provable adversarial robustness for deep neural networks (DNNs) has mainly focused on static supervised learning tasks such as image classification. However, DNNs have been used extensively in real-world adaptive tasks such as reinforcement learning (RL), making such systems vulnerable to adversarial attacks as well. Prior works in provable robustness in RL seek to certify the behaviour of the victim policy at every time-step against a non-adaptive adversary using methods developed for the static setting. But in the real world, an RL adversary can infer the defense strategy used by the victim agent by observing the states, actions, etc. from previous time-steps and adapt itself to produce stronger attacks in future steps (e.g., by focusing more on states critical to the agent's performance). We present an efficient procedure, designed specifically to defend against an adaptive RL adversary, that can directly certify the total reward without requiring the policy to be robust at each time-step. Focusing on randomized smoothing based defenses, our main theoretical contribution is to prove an adaptive version of the Neyman-Pearson Lemma -a key lemma for smoothingbased certificates -where the adversarial perturbation at a particular time can be a stochastic function of current and previous observations and states as well as previous actions. Building on this result, we propose policy smoothing where the agent adds a Gaussian noise to its observation at each time-step before passing it through the policy function. Our robustness certificates guarantee that the final total reward obtained by policy smoothing remains above a certain threshold, even though the actions at intermediate time-steps may change under the attack. We show that our certificates are tight by constructing a worst-case scenario that achieves the bounds derived in our analysis. Our experiments on various environments like Cartpole, Pong, Freeway and Mountain Car show that our method can yield meaningful robustness guarantees in practice. | Published as a conference paper at ICLR 2022 POLICY SMOOTHING FOR PROVABLY ROBUST REINFORCEMENT LEARNING |
d232307112 | We ask the following question: what training information is required to design an effective outlier/out-of-distribution (OOD) detector, i.e., detecting samples that lie far away from the training distribution? Since unlabeled data is easily accessible for many applications, the most compelling approach is to develop detectors based on only unlabeled in-distribution data. However, we observe that most existing detectors based on unlabeled data perform poorly, often equivalent to a random prediction. In contrast, existing state-of-the-art OOD detectors achieve impressive performance but require access to fine-grained data labels for supervised training. We propose SSD, an outlier detector based on only unlabeled in-distribution data. We use self-supervised representation learning followed by a Mahalanobis distance based detection in the feature space. We demonstrate that SSD outperforms most existing detectors based on unlabeled data by a large margin. Additionally, SSD even achieves performance on par, and sometimes even better, with supervised training based detectors. Finally, we expand our detection framework with two key extensions. First, we formulate few-shot OOD detection, in which the detector has access to only one to five samples from each class of the targeted OOD dataset. Second, we extend our framework to incorporate training data labels, if available. We find that our novel detection framework based on SSD displays enhanced performance with these extensions, and achieves state-of-the-art performance 1 . | SSD: A UNIFIED FRAMEWORK FOR SELF- SUPERVISED OUTLIER DETECTION |
d231847321 | We present the Colorization Transformer, a novel approach for diverse high fidelity image colorization based on self-attention. Given a grayscale image, the colorization proceeds in three steps. We first use a conditional autoregressive transformer to produce a low resolution coarse coloring of the grayscale image. Our architecture adopts conditional transformer layers to effectively condition grayscale input. Two subsequent fully parallel networks upsample the coarse colored low resolution image into a finely colored high resolution image. Sampling from the Colorization Transformer produces diverse colorings whose fidelity outperforms the previous state-of-the-art on colorising ImageNet based on FID results and based on a human evaluation in a Mechanical Turk test. Remarkably, in more than 60% of cases human evaluators prefer the highest rated among three generated colorings over the ground truth. The code and pre-trained checkpoints for Colorization Transformer are publicly available at this url. | Published as a conference paper at ICLR 2021 COLORIZATION TRANSFORMER |
d213905197 | We study the use of hypermodels to represent epistemic uncertainty and guide exploration. This generalizes and extends the use of ensembles to approximate Thompson sampling. The computational cost of training an ensemble grows with its size, and as such, prior work has typically been limited to ensembles with tens of elements. We show that alternative hypermodels can enjoy dramatic efficiency gains, enabling behavior that would otherwise require hundreds or thousands of elements, and even succeed in situations where ensemble methods fail to learn regardless of size. This allows more accurate approximation of Thompson sampling as well as use of more sophisticated exploration schemes. In particular, we consider an approximate form of information-directed sampling and demonstrate performance gains relative to Thompson sampling. As alternatives to ensembles, we consider linear and neural network hypermodels, also known as hypernetworks. We prove that, with neural network base models, a linear hypermodel can represent essentially any distribution over functions, and as such, hypernetworks are no more expressive. * DeepMind 1 Although later work suggests that this dropout approximation can be of poor quality(Osband, 2016;Hron et al., 2017).arXiv:2006.07464v1 [cs.LG] 12 Jun 2020Published as a conference paper at ICLR 2020 hypermodel design might be able to amortize computation across the entire distribution of base models, and in doing so, offer large gains in computational efficiency.We train our hypermodels to estimate a posterior distribution over base models conditioned on observed data, in a spirit similar to that of the Bayesian hypermodel literature(Krueger et al., 2017). Unlike typical variational approximations to Bayesian deep learning, this approach allows computationally efficient training with complex multimodal distributions. In this paper, we consider hypermodels trained through stochastic gradient descent on perturbed data (see Section 2.1 for a full description). Training procedures for hypermodels are an important area of research, and it may be possible to improve on this approach, but that is not the focus of this paper. Instead, we aim to understand whether more sophisticated hypermodel architectures can substantially improve exploration. To do this we consider bandit problems of varying degrees of complexity, and investigate the computational requirements to achieve low regret over a long horizon. | Published as a conference paper at ICLR 2020 HYPERMODELS FOR EXPLORATION |
d221739153 | We study the approximation properties and optimization dynamics of recurrent neural networks (RNNs) when applied to learn input-output relationships in temporal data. We consider the simple but representative setting of using continuoustime linear RNNs to learn from data generated by linear relationships. Mathematically, the latter can be understood as a sequence of linear functionals. We prove a universal approximation theorem of such linear functionals and characterize the approximation rate. Moreover, we perform a fine-grained dynamical analysis of training linear RNNs by gradient methods. A unifying theme uncovered is the non-trivial effect of memory, a notion that can be made precise in our framework, on both approximation and optimization: when there is long-term memory in the target, it takes a large number of neurons to approximate it. Moreover, the training process will suffer from slow downs. In particular, both of these effects become exponentially more pronounced with increasing memory -a phenomenon we call the "curse of memory". These analyses represent a basic step towards a concrete mathematical understanding of new phenomenons that may arise in learning temporal relationships using recurrent architectures. † Equal contribution ‡ Corresponding author arXiv:2009.07799v2 [cs.LG] 16 May 2021Published as a conference paper at ICLR 2021 here is the presence of temporal dynamics in terms of both the recurrent architectures in the model and the dynamical structures in the data. Hence, to understand the influence of dynamics on learning is of fundamental importance. As is often the case, the key effects of dynamics can already be revealed in the simplest linear setting. For this reason, we will focus our analysis on linear RNNs, i.e. those with linear activations. Further, we will employ a continuous-time analysis initially studied in the context of feed-forward architectures (E, 2017; Haber & Ruthotto, 2017; Li et al., 2017) and recently in recurrent settings (Ceni et al., 2019; Chang et al., 2019; Lim, 2020; Sherstinsky, 2018; Niu et al., 2019; Herrera et al., 2020; Rubanova et al., 2019) and idealize the RNN as a continuous-time dynamical system. This allows us to phrase the problems under investigation in convenient analytical settings that accentuates the effect of dynamics. In this case, the RNNs serve to approximate relationships represented by sequences of linear functionals. On first look the setting appears to be simple, but we show that it yields representative results that underlie key differences in the dynamical setting as opposed to static supervised learning problems. In fact, we show that memory, which can be made precise by the decay rates of the target linear functionals, can affect both approximation rates and optimization dynamics in a non-trivial way.Our main results are:Here, {h k } are the hidden/latent states and its evolution is governed by a recursive application of a feed-forward layer with activation σ, andŷ k is called the observation or readout. We omit the bias term here and only consider a linear readout or output layer. For each time step k, the mapping {x 0 , . . . , x k−1 } →ŷ k parameterizes a functionĤ k (·) through adjustable parameters (c, W, U ). Hence, for a particular choice of these parameters, a sequence of functions {Ĥ k } is constructed at the same time. To understand the working principles of RNNs, we need to characterize how {Ĥ k } approximates {H k }.The model(2)is not easy to analyze due to its discrete iterative nature. Hence, here we employ a continuous-time idealization that replaces the time-step index k by a continuous time parameter t. This allows us to employ a large variety of continuum analysis tools to gain insights to the learning problem. Let us now introduce this framework.Continuous-time formulation. Consider a sequence of inputs indexed by a real-valued variable t ∈ R instead of a discrete variable k considered previously. Concretely, we consider the input spaceDietrich Braess and Wolfgang Hackbusch. Approximation of 1/x by exponential sums in [1, ∞). IMA journal of numerical analysis, 25(4):685-697, 2005. . Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014. Tommy W. S. Chow and Xiao-Dong Li. Modeling of continuous time dynamical systems with input by recurrent neural networks. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 47(4):575-578, 2000. Eugen Diaconescu. The use of narx neural networks to predict chaotic time series. WSEAS Transactions on Computers archive, 3:182-191, 2008. . TopicRNN: A recurrent neural network with long-range semantic dependency. In 5th International Conference on Learning Representations, ICLR 2017, 2017. P. Doukhan, G. Oppenheim, and M. Taqqu. Theory and applications of long-range dependence. 2003.Kenji Doya. Universality of fully connected recurrent neural networks. | ON THE CURSE OF MEMORY IN RECURRENT NEU- RAL NETWORKS: APPROXIMATION AND OPTIMIZA- TION ANALYSIS |
d231709464 | Due to the need to store the intermediate activations for back-propagation, end-toend (E2E) training of deep networks usually suffers from high GPUs memory footprint. This paper aims to address this problem by revisiting the locally supervised learning, where a network is split into gradient-isolated modules and trained with local supervision. We experimentally show that simply training local modules with E2E loss tends to collapse task-relevant information at early layers, and hence hurts the performance of the full model. To avoid this issue, we propose an information propagation (InfoPro) loss, which encourages local modules to preserve as much useful information as possible, while progressively discard task-irrelevant information. As InfoPro loss is difficult to compute in its original form, we derive a feasible upper bound as a surrogate optimization objective, yielding a simple but effective algorithm. In fact, we show that the proposed method boils down to minimizing the combination of a reconstruction loss and a normal cross-entropy/contrastive term. Extensive empirical results on five datasets (i.e., CIFAR, SVHN, STL-10, ImageNet and Cityscapes) validate that InfoPro is capable of achieving competitive performance with less than 40% memory footprint compared to E2E training, while allowing using training data with higher-resolution or larger batch sizes under the same GPU memory constraint. Our method also enables training local modules asynchronously for potential training acceleration. | Published as a conference paper at ICLR 2021 REVISITING LOCALLY SUPERVISED LEARNING: AN ALTERNATIVE TO END-TO-END TRAINING |
d16868223 | Sentiment analysis is a common task in natural language processing that aims to detect polarity of a text document (typically a consumer review). In the simplest settings, we discriminate only between positive and negative sentiment, turning the task into a standard binary classification problem. We compare several machine learning approaches to this problem, and combine them to achieve a new state of the art. We show how to use for this task the standard generative language models, which are slightly complementary to the state of the art techniques. We achieve strong results on a well-known dataset of IMDB movie reviews. Our results are easily reproducible, as we publish also the code needed to repeat the experiments. This should simplify further advance of the state of the art, as other researchers can combine their techniques with ours with little effort. | ENSEMBLE OF GENERATIVE AND DISCRIMINATIVE TECHNIQUES FOR SENTIMENT ANALYSIS OF MOVIE REVIEWS |
d252668767 | The recent 3D medical ViTs (e.g., SwinUNETR) achieve the state-of-the-art performances on several 3D volumetric data benchmarks, including 3D medical image segmentation. Hierarchical transformers (e.g., Swin Transformers) reintroduced several ConvNet priors and further enhanced the practical viability of adapting volumetric segmentation in 3D medical datasets. The effectiveness of hybrid approaches is largely credited to the large receptive field for non-local selfattention and the large number of model parameters. We hypothesize that volumetric ConvNets can simulate the large receptive field behavior of these learning approaches with fewer model parameters using depth-wise convolution. In this work, we propose a lightweight volumetric ConvNet, termed 3D UX-Net, which adapts the hierarchical transformer using ConvNet modules for robust volumetric segmentation. Specifically, we revisit volumetric depth-wise convolutions with large kernel (LK) size (e.g. starting from 7 × 7 × 7) to enable the larger global receptive fields, inspired by Swin Transformer. We further substitute the multi-layer perceptron (MLP) in Swin Transformer blocks with pointwise depth convolutions and enhance model performances with fewer normalization and activation layers, thus reducing the number of model parameters. 3D UX-Net competes favorably with current SOTA transformers (e.g. SwinUNETR) using three challenging public datasets on volumetric brain and abdominal imaging: 1) MICCAI Challenge 2021 FLARE, 2) MICCAI Challenge 2021 FeTA, and 3) MICCAI Challenge 2022 AMOS. 3D UX-Net consistently outperforms Swin-UNETR with improvement from 0.929 to 0.938 Dice (FLARE2021) and 0.867 to 0.874 Dice (Feta2021). We further evaluate the transfer learning capability of 3D UX-Net with AMOS2022 and demonstrates another improvement of 2.27% Dice (from 0.880 to 0.900). The source code with our proposed model are available at https://github.com/MASILab/3DUX-Net. | 3D UX-NET: A LARGE KERNEL VOLUMETRIC CON- VNET MODERNIZING HIERARCHICAL TRANSFORMER FOR MEDICAL IMAGE SEGMENTATION |
d220056027 | Generative Adversarial Networks are notoriously challenging to train. The underlying minmax optimization is highly susceptible to the variance of the stochastic gradient and the rotational component of the associated game vector field. To tackle these challenges, we propose the Lookahead algorithm for minmax optimization, originally developed for single objective minimization only. The backtracking step of our Lookahead-minmax naturally handles the rotational game dynamics, a property which was identified to be key for enabling gradient ascent descent methods to converge on challenging examples often analyzed in the literature. Moreover, it implicitly handles high variance without using large mini-batches, known to be essential for reaching state of the art performance. Experimental results on MNIST, SVHN, CIFAR-10, and ImageNet demonstrate a clear advantage of combining Lookahead-minmax with Adam or extragradient, in terms of performance and improved stability, for negligible memory and computational cost. Using 30-fold fewer parameters and 16-fold smaller minibatches we outperform the reported performance of the class-dependent BigGAN on CIFAR-10 by obtaining FID of 12.19 without using the class labels, bringing state-of-the-art GAN training within reach of common computational resources. Our source code is available: https://github.com. Large scale GAN training for high fidelity natural image synthesis. In ICLR, 2019. Ronald E Bruck. On the weak convergence of an ergodic iteration for the solution of variational inequalities for monotone operators in hilbert space. with optimism. In ICLR, 2018. Constantinos Daskalakis, Stratis Skoulakis, and Manolis Zampetakis. The complexity of constrained min-max optimization. arXiv:2009.09623, 2020. Aaron Defazio and Léon Bottou. On the ineffectiveness of variance reduced optimization for deep learning. In NeurIPS, 2019. Popov Leonid Denisovich. A modification of the arrow-hurwicz method for search of saddle points. Mathematical Notes of the Academy of Sciences of the USSR, 28(5):845-848, 1980.Aymeric Dieuleveut, Alain Durmus, and Francis Bach. Bridging the gap between constant step size stochastic gradient descent and markov chains. arXiv:1707.06386, 2017. | Published as a conference paper at ICLR 2021 TAMING GANS WITH LOOKAHEAD-MINMAX |
d256390348 | Fully test-time adaptation aims at adapting a pre-trained model to the test stream during real-time inference, which is urgently required when the test distribution differs from the training distribution. Several efforts have been devoted to improving adaptation performance. However, we find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning. First, we reveal that the normalization statistics in test-time BN are completely affected by the currently received test samples, resulting in inaccurate estimates. Second, we show that during test-time adaptation, the parameter update is biased towards some dominant classes. In addition to the extensively studied test stream with independent and class-balanced samples, we further observe that the defects can be exacerbated in more complicated test environments, such as (time) dependent or class-imbalanced data. We observe that previous approaches work well in certain scenarios while show performance degradation in others due to their faults. In this paper, we provide a plug-in solution called DELTA for Degradation-freE fuLly Test-time Adaptation, which consists of two components: (i) Test-time Batch Renormalization (TBR), introduced to improve the estimated normalization statistics. (ii) Dynamic Online re-weighTing (DOT), designed to address the class bias within optimization. We investigate various test-time adaptation methods on three commonly used datasets with four scenarios, and a newly introduced real-world dataset. DELTA can help them deal with all scenarios simultaneously, leading to SOTA performance. * work done by Bowen Zhao (during internship) and Chen Chen at Tencent. . Progressive domain expansion network for single domain generalization. | Published as a conference paper at ICLR 2023 DELTA: DEGRADATION-FREE FULLY TEST-TIME ADAP- TATION * |
d14850799 | Recently, it has been observed that when representations are learnt in a way that encourages sparsity, improved performance is obtained on classification tasks. These methods involve combinations of activation functions, sampling steps and different kinds of penalties. To investigate the effectiveness of sparsity by itself, we propose the "ksparse autoencoder", which is an autoencoder with linear activation function, where in hidden layers only the k highest activities are kept. When applied to the MNIST and NORB datasets, we find that this method achieves better classification results than denoising autoencoders, networks trained with dropout, and RBMs. k -sparse autoencoders are simple to train and the encoding stage is very fast, making them well-suited to large problem sizes, where conventional sparse coding algorithms cannot be applied. | k -Sparse Autoencoders |
d257078994 | Edges in many real-world social/information networks are associated with rich text information (e.g., user-user communications or user-product reviews). However, mainstream network representation learning models focus on propagating and aggregating node attributes, lacking specific designs to utilize text semantics on edges. While there exist edge-aware graph neural networks, they directly initialize edge attributes as a feature vector, which cannot fully capture the contextualized text semantics of edges. In this paper, we propose Edgeformers 1 , a framework built upon graph-enhanced Transformers, to perform edge and node representation learning by modeling texts on edges in a contextualized way. Specifically, in edge representation learning, we inject network information into each Transformer layer when encoding edge texts; in node representation learning, we aggregate edge representations through an attention mechanism within each node's ego-graph. On five public datasets from three different domains, Edgeformers consistently outperform state-of-the-art baselines in edge classification and link prediction, demonstrating the efficacy in learning edge and node representations, respectively. | EDGEFORMERS: GRAPH-EMPOWERED TRANSFORM- ERS FOR REPRESENTATION LEARNING ON TEXTUAL- EDGE NETWORKS |
d231627872 | Neural networks are prone to learning shortcuts -they often model simple correlations, ignoring more complex ones that potentially generalize better. Prior works on image classification show that instead of learning a connection to object shape, deep classifiers tend to exploit spurious correlations with low-level texture or the background for solving the classification task. In this work, we take a step towards more robust and interpretable classifiers that explicitly expose the task's causal structure. Building on current advances in deep generative modeling, we propose to decompose the image generation process into independent causal mechanisms that we train without direct supervision. By exploiting appropriate inductive biases, these mechanisms disentangle object shape, object texture, and background; hence, they allow for generating counterfactual images. We demonstrate the ability of our model to generate such images on MNIST and ImageNet. Further, we show that the counterfactual images can improve out-of-distribution robustness with a marginal drop in performance on the original classification task, despite being synthetic. Lastly, our generative model can be trained efficiently on a single GPU, exploiting common pre-trained models as inductive biases. | Published as a conference paper at ICLR 2021 COUNTERFACTUAL GENERATIVE NETWORKS |
d239015970 | We address the problem of building agents whose goal is to learn to execute out-of distribution (OOD) multi-task instructions expressed in temporal logic (TL) by using deep reinforcement learning (DRL). Recent works provided evidence that the agent's neural architecture is a key feature when DRL agents are learning to solve OOD tasks in TL. Yet, the studies on this topic are still in their infancy. In this work, we propose a new deep learning configuration with inductive biases that lead agents to generate latent representations of their current goal, yielding a stronger generalization performance. We use these latent-goal networks within a neuro-symbolic framework that executes multi-task formally-defined instructions and contrast the performance of the proposed neural networks against employing different state-of-the-art (SOTA) architectures when generalizing to unseen instructions in OOD environments.Published as a conference paper at ICLR 2022 task-agnostic representation of the current state concatenated to a latent goal that is form by processing both observation of the agent and human instruction. As a motivation example, let us consider two scenarios: 1) a robot that is at the center of an empty room with a red square at the bottom right corner, 2) a robot at the same position in a room that is identical to the one in (1) except that the red square is now green. Intuitively, we can say that, if we give the instruction "go to the red square" in (1), the goal of the robot is the same as if we say "go to the green square" in (2), because the two tasks can be abstracted as "turn to face the bottom right of the room, then move straight". In a nutshell, computing the human instruction together with the current state of the environment -being at the center of the room, with the object asked by the human in the bottom right corner -allows to deduce that the optimal policy is the same in both scenarios. Our contributions are listed as follows:• We propose a new deep learning configuration (the latent-goal architecture) that helps agents to generalize better when solving multi-task instructions in OOD scenarios. • We are first to evaluate the performance of multiple state-of-the-art (SOTA) neural networks targeting generalization when following temporal logic instructions. • Through ablation studies, we find that networks generating independent pieces of information within their output are specially benefited from being used within latent-goal architectures.The remaining of the paper is structured as follows: Section 2 briefly introduces the key concepts needed to follow this work. Section 3 presents the formal language used to generate instructions and the neuro-symbolic framework that agents use in our experiments. Then, Section 4 details the proposed network configuration. Section 5 includes the experimental settings, empirical results and ablation studies. Last, Sections 6 and 7 discuss related works and conclusions, respectively. | Published as a conference paper at ICLR 2022 IN A NUTSHELL, THE HUMAN ASKED FOR THIS: LATENT GOALS FOR FOLLOWING TEMPORAL SPECIFICATIONS |
d258557266 | Sequence modeling has important applications in natural language processing and computer vision. Recently, the transformer-based models have shown strong performance on various sequence modeling tasks, which rely on attention to capture pairwise token relations, and position embedding to inject positional information.While showing good performance, the transformer models are inefficient to scale to long input sequences, mainly due to the quadratic space-time complexity of attention. To overcome this inefficiency, we propose to model sequences with a relative position encoded Toeplitz matrix and use a Toeplitz matrix-vector production trick to reduce the space-time complexity of the sequence modeling to log linear. A lightweight sub-network called relative position encoder is proposed to generate relative position coefficients with a fixed budget of parameters, enabling the proposed Toeplitz neural network to deal with varying sequence lengths. In addition, despite being trained on 512-token sequences, our model can extrapolate input sequence length up to 14K tokens in inference with consistent performance. Extensive experiments on autoregressive and bidirectional language modeling, image modeling, and the challenging Long-Range Arena benchmark show that our method achieves better performance than its competitors in most downstream tasks while being significantly faster. | Published as a conference paper at ICLR 2023 TOEPLITZ NEURAL NETWORK FOR SEQUENCE MODELING |
d249151916 | Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Often, the underlying process that drives the distribution shift is continuous in nature. We exploit this underlying continuity by developing predictor-corrector algorithms for time-varying stochastic optimization that anticipates changes in the underlying data generating process through a predictor-corrector term in the update rule. The key challenge is the estimation of the predictor-corrector term; a naive approach based on sample-average approximation may lead to non-convergence. We develop a general moving-average based method to estimate the predictorcorrector term and provide error bounds for the iterates, both in presence of pure and noisy access to the queries from the relevant derivatives of the loss function. Furthermore, we show (theoretically and empirically in several examples) that our method outperforms non-predictor corrector methods that do not anticipate changes in the data generating process. 1 | PREDICTOR-CORRECTOR ALGORITHMS FOR STOCHAS- TIC OPTIMIZATION UNDER GRADUAL DISTRIBUTION SHIFT |
d231602921 | We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking. Instead of tackling the problem by training task-specific discriminative classifiers, we frame it as a translation task between augmented natural languages, from which the task-relevant information can be easily extracted. Our approach can match or outperform task-specific models on all tasks, and in particular, achieves new state-of-the-art results on joint entity and relation extraction (CoNLL04, ADE, NYT, and ACE2005 datasets), relation classification (FewRel and TACRED), and semantic role labeling (CoNLL-2005 and CoNLL-2012). We accomplish this while using the same architecture and hyperparameters for all tasks and even when training a single model to solve all tasks at the same time (multi-task learning). Finally, we show that our framework can also significantly improve the performance in a low-resource regime, thanks to better use of label semantics.2. We apply our framework to (1) joint entity and relation extraction; (2) named entity recognition;(3) relation classification; (4) semantic role labeling; (5) coreference resolution; (6) event extraction; (7) dialogue state tracking (Sections 4 and 5). In all cases we achieve at least comparable results to the current state-of-the-art, and we achieve new state-of-the-art performance on joint entity and relation extraction (CoNLL04, ADE, NYT, and ACE2005 datasets), relation classification (FewRel and TACRED), and semantic role labeling (CoNLL-2005 and CoNLL-2012).3. We also train a single model simultaneously on all tasks (multi-task learning), obtaining comparable or better results as compared with single-task models (Section 5.1).4. We show that, thanks to the improved transfer of knowledge about label semantics, we can significantly improve the performance in the few-shot regime over previous approaches (Section 5.2).STRUCTURED PREDICTION TASKSJoint entity and relation extraction. Format and details for this task are provided in Section 3.Named entity recognition (NER). This is an entity-only particular case of the previous task.Relation classification. For this task, we are given an input sentence with head and tail entities and seek to classify the type of relation between them, choosing from a predefined set of relations. Since the head entity does not necessarily precede the tail entity in the input sentence, we add a phrase "The relationship between [ head ] and [ tail ] is" after the original input sentence. The output repeats this phrase, followed by the relation type. In the following example, the head and tail entities are "Carmen Melis" and "soprano" which have a voice type relation. Input: Born in Bologna, Orlandi was a student of the famous Italian [ soprano soprano soprano soprano soprano soprano soprano soprano soprano soprano soprano soprano soprano soprano soprano soprano soprano ] and voice teacher [ Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis Carmen Melis ] in Milan. The relationship between [ Carmen Melis | Published as a conference paper at ICLR 2021 STRUCTURED PREDICTION AS TRANSLATION BETWEEN AUGMENTED NATURAL LANGUAGES |
d257495980 | Recent state-of-the-art methods in imbalanced semi-supervised learning (SSL) rely on confidence-based pseudo-labeling with consistency regularization. To obtain high-quality pseudo-labels, a high confidence threshold is typically adopted. However, it has been shown that softmax-based confidence scores in deep networks can be arbitrarily high for samples far from the training data, and thus, the pseudo-labels for even high-confidence unlabeled samples may still be unreliable. In this work, we present a new perspective of pseudo-labeling for imbalanced SSL. Without relying on model confidence, we propose to measure whether an unlabeled sample is likely to be "in-distribution"; i.e., close to the current training data. To decide whether an unlabeled sample is "in-distribution" or "out-of-distribution", we adopt the energy score from out-of-distribution detection literature. As training progresses and more unlabeled samples become in-distribution and contribute to training, the combined labeled and pseudo-labeled data can better approximate the true class distribution to improve the model. Experiments demonstrate that our energy-based pseudo-labeling method, InPL, albeit conceptually simple, significantly outperforms confidence-based methods on imbalanced SSL benchmarks. For example, it produces around 3% absolute accuracy improvement on CIFAR10-LT. When combined with state-of-the-art long-tailed SSL methods, further improvements are attained. In particular, in one of the most challenging scenarios, InPL achieves a 6.9% accuracy improvement over the best competitor. | Published as a conference paper at ICLR 2023 INPL: PSEUDO-LABELING THE INLIERS FIRST FOR IM- BALANCED SEMI-SUPERVISED LEARNING |
d16496273 | We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches. | Efficient Learning of Domain-invariant Image Representations |
d248887235 | The great success of deep learning heavily relies on increasingly larger training data, which comes at a price of huge computational and infrastructural costs. This poses crucial questions that, do all training data contribute to model's performance? How much does each individual training sample or a sub-training-set affect the model's generalization, and how to construct the smallest subset from the entire training data as a proxy training set without significantly sacrificing the model's performance? To answer these, we propose dataset pruning, an optimization-based sample selection method that can (1) examine the influence of removing a particular set of training samples on model's generalization ability with theoretical guarantee, and (2) construct the smallest subset of training data that yields strictly constrained generalization gap. The empirically observed generalization gap of dataset pruning is substantially consistent with our theoretical expectations. Furthermore, the proposed method prunes 40% training examples on the CIFAR-10 dataset, halves the convergence time with only 1.3% test accuracy decrease, which is superior to previous score-based sample selection methods. | Published as a conference paper at ICLR 2023 DATASET PRUNING: REDUCING TRAINING DATA BY EXAMINING GENERALIZATION INFLUENCE |
d3530344 | Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates. This has been a notable problem in training deep RL agents to perform web-based tasks, such as booking flights or replying to emails, where a single mistake can ruin the entire sequence of actions. A common remedy is to "warmstart" the agent by pre-training it to mimic expert demonstrations, but this is prone to overfitting. Instead, we propose to constrain exploration using demonstrations. From each demonstration, we induce high-level "workflows" which constrain the allowable actions at each time step to be similar to those in the demonstration (e.g., "Step 1: click on a textbox; Step 2: enter some text"). Our exploration policy then learns to identify successful workflows and samples actions that satisfy these workflows. Workflows prune out bad exploration directions and accelerate the agent's ability to discover rewards. We use our approach to train a novel neural policy designed to handle the semi-structured nature of websites, and evaluate on a suite of web tasks, including the recent World of Bits benchmark. We achieve new state-of-the-art results, and show that workflow-guided exploration improves sample efficiency over behavioral cloning by more than 100x. * First three authors contributed equally | Published as a conference paper at ICLR 2018 REINFORCEMENT LEARNING ON WEB INTERFACES USING WORKFLOW-GUIDED EXPLORATION |
d257038220 | Transformer architectures have achieved great success in solving natural language tasks, which learn strong language representations from large-scale unlabeled texts. In this paper, we seek to go further beyond and explore a new logical inductive bias for better language representation learning. Logic reasoning is known as a formal methodology to reach answers from given knowledge and facts. Inspired by such a view, we develop a novel neural architecture named FOLNet (First-Order Logic Network), to encode this new inductive bias. We construct a set of neural logic operators as learnable Horn clauses, which are further forward-chained into a fully differentiable neural architecture (FOLNet). Interestingly, we find that the self-attention module in transformers can be composed by two of our neural logic operators, which probably explains their strong reasoning performance. Our proposed FOLNet has the same input and output interfaces as other pretrained models and thus could be pretrained/finetuned by using similar losses. It also allows FOLNet to be used in a plug-and-play manner when replacing other pretrained models. With our logical inductive bias, the same set of "logic deduction skills" learned through pretraining are expected to be equally capable of solving diverse downstream tasks. For this reason, FOLNet learns language representations that have much stronger transfer capabilities. Experimental results on several language understanding tasks show that our pretrained FOLNet model outperforms the existing strong transformer-based approaches. 1 , et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. SemEval-2017 task 1:Semantic textual similarity multilingual and crosslingual focused evaluation. In | Published as a conference paper at ICLR 2023 LEARNING LANGUAGE REPRESENTATIONS WITH LOGICAL INDUCTIVE BIAS |
d252595927 | Large language models such as GPT-3 (Brown et al., 2020) can perform arbitrary tasks without undergoing fine-tuning after being prompted with only a few labeled examples. An arbitrary task can be reformulated as a natural language prompt, and a language model can be asked to generate the completion, indirectly performing the task in a paradigm known as prompt-based learning. To date, emergent prompt-based learning capabilities have mainly been demonstrated for unidirectional language models. However, bidirectional language models pre-trained on denoising objectives such as masked language modeling produce stronger learned representations for transfer learning. This motivates the possibility of prompting bidirectional models, but their pre-training objectives have made them largely incompatible with the existing prompting paradigm. We present SAP (Sequential Autoregressive Prompting), a technique that enables the prompting of bidirectional models. Utilizing the machine translation task as a case study, we prompt the bidirectional mT5 model with SAP and demonstrate its fewshot and zero-shot translations outperform the few-shot translations of unidirectional models like GPT-3 and XGLM (Lin et al., 2021), despite mT5's approximately 50% fewer parameters. We further show SAP is effective on question answering and summarization. For the first time, our results demonstrate promptbased learning is an emergent property of a broader class of language models, rather than only unidirectional models. | Published as a conference paper at ICLR 2023 BIDIRECTIONAL LANGUAGE MODELS ARE ALSO FEW-SHOT LEARNERS |
d231719707 | Active learning is a powerful tool when labelling data is expensive, but it introduces a bias because the training data no longer follows the population distribution. We formalize this bias and investigate the situations in which it can be harmful and sometimes even helpful. We further introduce novel corrective weights to remove bias when doing so is beneficial. Through this, our work not only provides a useful mechanism that can improve the active learning approach, but also an explanation of the empirical successes of various existing approaches which ignore this bias. In particular, we show that this bias can be actively helpful when training overparameterized models-like neural networks-with relatively little data. * Equal contribution. Corresponding author sebastian.farquhar@cs.ox.ac.uk. | Published as a conference paper at ICLR 2021 ON STATISTICAL BIAS IN ACTIVE LEARNING: HOW AND WHEN TO FIX IT |
d249017593 | Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Existing work adds ∞ -bounded perturbations to the original sample so that the trained model generalizes poorly. Such perturbations, however, are easy to eliminate by adversarial training and data augmentations. In this paper, we resolve this problem from a novel perspective by perturbing only one pixel in each image. Interestingly, such a small modification could effectively degrade model accuracy to almost an untrained counterpart. Moreover, our produced One-Pixel Shortcut (OPS) could not be erased by adversarial training and strong augmentations. To generate OPS, we perturb in-class images at the same position to the same target value that could mostly and stably deviate from all the original images. Since such generation is only based on images, OPS needs significantly less computational cost than the previous methods using DNN generators. Based on OPS, we introduce an unlearnable dataset called CIFAR-10-S, which is indistinguishable from CIFAR-10 by humans but induces the trained model to extremely low accuracy. Even under adversarial training, a ResNet-18 trained on CIFAR-10-S has only 10.61% accuracy, compared to 83.02% by the existing error-Published as a conference paper at ICLR 2023 Clean One-Pixel Shortcut DNN DNN extract features train train extract features Francesco Croce and Matthias Hein. Sparse and imperceivable adversarial attacks. In | ONE-PIXEL SHORTCUT: ON THE LEARNING PREFER- ENCE OF DEEP NEURAL NETWORKS |
d246705934 | Dealing with missing values and incomplete time series is a labor-intensive, tedious, inevitable task when handling data coming from real-world applications. Effective spatio-temporal representations would allow imputation methods to reconstruct missing temporal data by exploiting information coming from sensors at different locations. However, standard methods fall short in capturing the nonlinear time and space dependencies existing within networks of interconnected sensors and do not take full advantage of the available -and often strong -relational information. Notably, most state-of-the-art imputation methods based on deep learning do not explicitly model relational aspects and, in any case, do not exploit processing frameworks able to adequately represent structured spatio-temporal data. Conversely, graph neural networks have recently surged in popularity as both expressive and scalable tools for processing sequential data with relational inductive biases. In this work, we present the first assessment of graph neural networks in the context of multivariate time series imputation. In particular, we introduce a novel graph neural network architecture, named GRIN, which aims at reconstructing missing data in the different channels of a multivariate time series by learning spatio-temporal representations through message passing. Empirical results show that our model outperforms state-of-the-art methods in the imputation task on relevant real-world benchmarks with mean absolute error improvements often higher than 20%.Vaswani et al., 2017). We argue that stronger, structural, inductive biases are needed to advance the state of the art in time series imputation and allow to build effective inference engines in the context of large and complex sensor networks as those found in real-world applications.In this work, we model input multivariate time series as sequences of graphs where edges represent relationships among different channels. We propose graph neural networks (GNNs)(Scarselli et al., 2008;Bronstein et al., 2017;Battaglia et al., 2018)as the building block of a novel, bidirectional, recurrent neural network for multivariate time series imputation (MTSI). Our method, named Graph Recurrent Imputation Network (GRIN), has at its core a recurrent neural network cell where gates are implemented by message-passing neural networks (MPNNs;Gilmer et al., 2017). Two of these networks process the input multivariate time series in both forward and backward time directions at each node, while hidden states are processed by a message-passing imputation layer which is constrained to learn how to perform imputation by looking at neighboring nodes. In fact, by considering each edge as a soft functional dependency that constraints the value observed at corresponding nodes, we argue that operating in the context of graphs introduces a positive inductive bias for MTSI. Our contributions are manifold: 1) we introduce a methodological framework to exploit graph neural networks in the context of MTSI, 2) we propose a novel, practical and effective implementation of a GNN-based architecture for MTSI, and 3) we achieve state-of-the-art results on several and varied MTSI benchmarks. Our method does not rely on any assumption on the distribution of the missing values (e.g., presence and duration of transient dynamics and/or length of missing sequences) other than stationarity of the underlying process. The rest of the paper is organized as follows. In Section 2 we discuss the related works. Then, in Section 3, we formally introduce the problem settings and the task of MTSI. We present our approach to MTSI in Section 4, by describing the novel framework to implement imputation architectures based on GNNs. We proceed with an empirical evaluation of the presented method against state-of-the-art baselines in Section 5 and, finally, we draw our conclusions in Section 6. | Published as a conference paper at ICLR 2022 FILLING THE G AP S: MULTIVARIATE TIME SERIES IMPUTATION BY GRAPH NEURAL NETWORKS |
d15106200 | WHAT DO DEEP CNNS LEARN ABOUT OBJECTS? | |
d238408147 | Network-valued data are encountered in a wide range of applications, and pose challenges in learning due to their complex structure and absence of vertex correspondence. Typical examples of such problems include classification or grouping of protein structures and social networks. Various methods, ranging from graph kernels to graph neural networks, have been proposed that achieve some success in graph classification problems. However, most methods have limited theoretical justification, and their applicability beyond classification remains unexplored. In this work, we propose methods for clustering multiple graphs, without vertex correspondence, that are inspired by the recent literature on estimating graphonssymmetric functions corresponding to infinite vertex limit of graphs. We propose a novel graph distance based on sorting-and-smoothing graphon estimators. Using the proposed graph distance, we present two clustering algorithms and show that they achieve state-of-the-art results. We prove the statistical consistency of both algorithms under Lipschitz assumptions on the graph degrees. We further study the applicability of the proposed distance for graph two-sample testing problems. arXiv:2110.02722v2 [cs.LG] 7 Nov 2021 work classification, and second is the lack of theoretical analysis of these methods, particularly in the small sample setting. Generalisation error bounds for graph kernel based learning exist(Du et al., 2019), but these bounds, based on learning theory, are meaningful only when many networks are available. However, in many applications, one needs to learn from a small population of large networks and, in such cases, an informative statistical analysis should consider the small sample, large graph regime. To address this issue, we take inspiration from the recent statistics literature on graph two-sample testing-given two (populations of) large graphs, the goal is to decide if they are from same statistical model or not. Although most theoretical studies in graph two-sample testing focus on graph with vertex correspondence (Tang et al. | GRAPHON BASED CLUSTERING AND TESTING OF NET- WORKS: ALGORITHMS AND THEORY |
d252693327 | Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons, namely nonnegativity and energy efficiency in both activity and weights, promote such sought after disentangled representations by enforcing neurons to become selective for single factors of task variation. We demonstrate these constraints lead to disentangling in a variety of tasks and architectures, including variational autoencoders. We also use this theory to explain why the brain partitions its cells into distinct cell types such as grid and object-vector cells, and also explain when the brain instead entangles representations in response to entangled task factors. Overall, this work provides a mathematical understanding of why, when, and how neurons represent factors in both brains and machines, and is a first step towards understanding of how task demands structure neural representations. | DISENTANGLING WITH BIOLOGICAL CONSTRAINTS: A THEORY OF FUNCTIONAL CELL TYPES |
d254366282 | Dynamical systems are found in innumerable forms across the physical and biological sciences, yet all these systems fall naturally into equivalence classes: conservative or dissipative, stable or unstable, compressible or incompressible. Predicting these classes from data remains an essential open challenge in computational physics on which existing time-series classification methods struggle. Here, we propose, phase2vec, an embedding method that learns highquality, physically-meaningful representations of low-dimensional dynamical systems without supervision. Our embeddings are produced by a convolutional backbone that extracts geometric features from flow data and minimizes a physicallyinformed vector field reconstruction loss. The trained architecture can not only predict the equations of unseen data, but also produces embeddings that encode meaningful physical properties of input data (e.g. stability of fixed points, conservation of energy, and the incompressibility of flows) more faithfully than standard blackbox classifiers and state-of-the-art time series classification techniques. We additionally apply our embeddings to the analysis of meteorological data, showing we can detect climatically meaningful features. Collectively, our results demonstrate the viability of embedding approaches for the discovery of dynamical features in physical systems. | PHASE2VEC: DYNAMICAL SYSTEMS EMBEDDING WITH A PHYSICS- INFORMED CONVOLUTIONAL NETWORK |
d204800654 | In the learning to learn (L2L) framework, we cast the design of optimization algorithms as a machine learning problem and use deep neural networks to learn the update rules. In this paper, we extend the L2L framework to zeroth-order (ZO) optimization setting, where no explicit gradient information is available. Our learned optimizer, modeled as recurrent neural networks (RNNs), first approximates gradient by ZO gradient estimator and then produces parameter update utilizing the knowledge of previous iterations. To reduce the high variance effect due to ZO gradient estimator, we further introduce another RNN to learn the Gaussian sampling rule and dynamically guide the query direction sampling. Our learned optimizer outperforms hand-designed algorithms in terms of convergence rate and final solution on both synthetic and practical ZO optimization problems (in particular, the black-box adversarial attack task, which is one of the most widely used applications of ZO optimization). We finally conduct extensive analytical experiments to demonstrate the effectiveness of our proposed optimizer. 1 1 Our code is available at https://github.com/RYoungJ/ZO-L2L | Published as a conference paper at ICLR 2020 LEARNING TO LEARN BY ZEROTH-ORDER ORACLE |
d238583200 | Humans decompose novel complex tasks into simpler ones to exploit previously learned skills. Analogously, hierarchical reinforcement learning seeks to leverage lower-level policies for simple tasks to solve complex ones. However, because each lower-level policy induces a different distribution of states, transitioning from one lower-level policy to another may fail due to an unexpected starting state. We introduce transition policies that smoothly connect lower-level policies by producing a distribution of states and actions that matches what is expected by the next policy. Training transition policies is challenging because the natural reward signal-whether the next policy can execute its subtask successfully-is sparse. By training transition policies via adversarial inverse reinforcement learning to match the distribution of expected states and actions, we avoid relying on taskbased reward. To further improve performance, we use deep Q-learning with a binary action space to determine when to switch from a transition policy to the next pre-trained policy, using the success or failure of the next subtask as the reward. Although the reward is still sparse, the problem is less severe due to the simple binary action space. We demonstrate our method on continuous bipedal locomotion and arm manipulation tasks that require diverse skills. We show that it smoothly connects the lower-level policies, achieving higher success rates than previous methods that search for successful trajectories based on a reward function, but do not match the state distribution. | Published as a conference paper at ICLR 2022 TRAINING TRANSITION POLICIES VIA DISTRIBUTION MATCHING FOR COMPLEX TASKS |
d246652381 | Recent studies have revealed that deep neural networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by poisoning a few training samples. The attacked model behaves normally on benign samples, whereas its prediction will be maliciously changed when the backdoor is activated. We reveal that poisoned samples tend to cluster together in the feature space of the attacked DNN model, which is mostly due to the endto-end supervised training paradigm. Inspired by this observation, we propose a novel backdoor defense via decoupling the original end-to-end training process into three stages. Specifically, we first learn the backbone of a DNN model via self-supervised learning based on training samples without their labels. The learned backbone will map samples with the same ground-truth label to similar locations in the feature space. Then, we freeze the parameters of the learned backbone and train the remaining fully connected layers via standard training with all (labeled) training samples. Lastly, to further alleviate side-effects of poisoned samples in the second stage, we remove labels of some 'low-credible' samples determined based on the learned model and conduct a semi-supervised fine-tuning of the whole model. Extensive experiments on multiple benchmark datasets and DNN models verify that the proposed defense is effective in reducing backdoor threats while preserving high accuracy in predicting benign samples. Our code is available at https://github.comPublished as a conference paper at ICLR 2022 door trigger (dubbed poisoned samples) tend to cluster together in the feature space. We reveal that this phenomenon is mostly due to the end-to-end supervised training paradigm. Specifically, the excessive learning capability allows DNNs to learn features about the backdoor trigger, while the DNNs can shrink the distance between poisoned samples in the feature space and connect the learned trigger-related features with the target label by the end-to-end supervised training. Based on this understanding, we propose to decouple the end-to-end training process for the backdoor defense. Specifically, we treat the DNNs as two disjoint parts, including a feature extractor (i.e., backbone) and a simple classifier (i.e., the remaining fully connected layers). We first learn the purified feature extractor via self-supervised learning (Kolesnikov et al., 2019; Chen et al., 2020a; Jing & Tian, 2020) with unlabeled training samples (obtained by removing their labels), and then learn the simple classifier via standard supervised training process based on the learned feature extractor and all training samples. The strong data augmentations involved in the self-supervised learning damage trigger patterns, making them unlearnable during representation learning; and the decoupling process further disconnects trigger patterns and the target label. Accordingly, hidden backdoors cannot be successfully created even the model is trained on the poisoned dataset based on our defense.Moreover, we further reveal that the representation of poisoned samples generated by the purified extractor is significantly different from those generated by the extractor learned with standard training process. Specifically, the poisoned sample lies closely to samples with its ground-truth label instead of the target label. This phenomenon makes the training of the simple classifier similar to label-noise learning (Wang et al., 2019b; Ma et al., 2020; Berthon et al., 2021). As such, we first filter high-credible training samples (i.e., training samples that are most probably to be benign) and then use those samples as labeled samples and the remaining part to form unlabeled samples to fine-tune the whole model via semi-supervised learning (Rasmus et al., 2015;Berthelot et al., 2019;Sohn et al., 2020). This approach is to further reduce the adverse effects of poisoned samples.The main contributions of this paper are three-fold. (1) We reveal that the backdoor is embedded in the feature space, which is mostly due to the end-to-end supervised training paradigm.(2) Based on our understanding, we propose a decoupling-based backdoor defense (DBD) to alleviate the threat of poisoning-based backdoor attacks. (3) Experiments on classical benchmark datasets are conducted, which verify the effectiveness of our defense. | BACKDOOR DEFENSE VIA DECOUPLING THE TRAIN- ING PROCESS |
d14121116 | We present a method for visual object classification using only a single feature, transformed color SIFT [15] with a variant of Spatial Pyramid Matching (SPM) that we called Sliding Spatial Pyramid Matching (SSPM), trained with an ensemble of linear regression (provided by LINEAR) to obtained state of the art result on Caltech-101[22]of 83.46%. SSPM is a special version of SPM where instead of dividing an image into K number of regions, a subwindow of fixed size is slide around the image with a fixed step size. For each subwindow, a histogram of visual words is generated. To obtained the visual vocabulary, instead of performing K-means clustering [26], we randomly pick N exemplars from the training set and encode them with a soft non-linear mapping method from[16]. We then trained 15 models, each with a different visual word size with linear regression. All 15 models are then averaged together to form a single strong model. | arXiv: some other text goes here Visual Objects Classification with Sliding Spatial Pyramid Matching |
d6702706 | We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a significantly larger neural net. During real-world workflows, one often trains very many different neural networks during the experimentation and design process. This is a wasteful process in which each new model is trained from scratch. Our Net2Net technique accelerates the experimentation process by instantaneously transferring the knowledge from a previous network to each new deeper or wider network. Our techniques are based on the concept of functionpreserving transformations between neural network specifications. This differs from previous approaches to pre-training that altered the function represented by a neural net when adding layers to it. Using our knowledge transfer mechanism to add depth to Inception modules, we demonstrate a new state of the art accuracy rating on the ImageNet dataset. | Net2Net: ACCELERATING LEARNING VIA KNOWLEDGE TRANSFER |
d24069181 | We propose a deep neural network for the prediction of future frames in natural video sequences. To effectively handle complex evolution of pixels in videos, we propose to decompose the motion and content, two key components generating dynamics in videos. Our model is built upon the Encoder-Decoder Convolutional Neural Network and Convolutional LSTM for pixel-level prediction, which independently capture the spatial layout of an image and the corresponding temporal dynamics. By independently modeling motion and content, predicting the next frame reduces to converting the extracted content features into the next frame content by the identified motion features, which simplifies the task of prediction. Our model is end-to-end trainable over multiple time steps, and naturally learns to decompose motion and content without separate training. We evaluate the proposed network architecture on human activity videos using KTH, Weizmann action, and UCF-101 datasets. We show state-of-the-art performance in comparison to recent approaches. To the best of our knowledge, this is the first end-to-end trainable network architecture with motion and content separation to model the spatio-temporal dynamics for pixel-level future prediction in natural videos. * This work was done while SH and XL were visiting the University of Michigan. | Published as a conference paper at ICLR 2017 DECOMPOSING MOTION AND CONTENT FOR NATURAL VIDEO SEQUENCE PREDICTION |
d12991853 | Finding minima of a real valued non-convex function over a high dimensional space is a major challenge in science. We provide evidence that some such functions that are defined on high dimensional domains have a narrow band of values whose pre-image contains the bulk of its critical points. This is in contrast with the low dimensional picture in which this band is wide. Our simulations agree with the previous theoretical work on spin glasses that proves the existence of such a band when the dimension of the domain tends to infinity. Furthermore our experiments on teacher-student networks with the MNIST dataset establish a similar phenomenon in deep networks. We finally observe that both the gradient descent and the stochastic gradient descent methods can reach this level within the same number of steps. | Under review as a workshop contribution at ICLR 2015 EXPLORATIONS ON HIGH DIMENSIONAL LANDSCAPES |
d18918187 | Image denoising based on a probabilistic model of local image patches has been employed by various researchers, and recently a deep (denoising) autoencoder has been proposed byBurger et al. [2012]and Xie et al.[2012] as a good model for this. In this paper, we propose that another popular family of models in the field of deep learning, called Boltzmann machines, can perform image denoising as well as, or in certain cases of high level of noise, better than denoising autoencoders. We empirically evaluate the two models on three different sets of images with different types and levels of noise. Throughout the experiments we also examine the effect of the depth of the models. The experiments confirmed our claim and revealed that the performance can be improved by adding more hidden layers, especially when the level of noise is high. | Boltzmann Machines and Denoising Autoencoders for Image Denoising |
d240070497 | We consider the problem of unsupervised domain adaptation (UDA) between a source and a target domain under conditional and label shift a.k.a Generalized Target Shift (GeTarS). Unlike simpler UDA settings, few works have addressed this challenging problem. Recent approaches learn domain-invariant representations, yet they have practical limitations and rely on strong assumptions that may not hold in practice. In this paper, we explore a novel and general approach to align pretrained representations, which circumvents existing drawbacks. Instead of constraining representation invariance, it learns an optimal transport map, implemented as a NN, which maps source representations onto target ones. Our approach is flexible and scalable, it preserves the problem's structure and it has strong theoretical guarantees under mild assumptions. In particular, our solution is unique, matches conditional distributions across domains, recovers target proportions and explicitly controls the target generalization risk. Through an exhaustive comparison on several datasets, we challenge the state-of-the-art in GeTarS. | Published as a conference paper at ICLR 2022 MAPPING CONDITIONAL DISTRIBUTIONS FOR DOMAIN ADAPTATION UNDER GENERALIZED TARGET SHIFT |
d232428023 | As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating or even exacerbating undesirable historical biases (e.g. gender and racial biases) has come to the fore of the public's attention.In this paper, we focus on the problem of detecting violations of individual fairness in ML models. We formalize the problem as measuring the susceptibility of ML models against a form of adversarial attack and develop a suite of inference tools for the adversarial cost function. The tools allow auditors to assess the individual fairness of ML models in a statistically-principled way: form confidence intervals for the worst-case performance differential between similar individuals and test hypotheses of model fairness with (asymptotic) non-coverage/Type I error rate control. We demonstrate the utility of our tools in a real-world case study 1 . | Published as a conference paper at ICLR 2021 STATISTICAL INFERENCE FOR INDIVIDUAL FAIRNESS |
d15002492 | We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning. Given a layer, we use non-linear least squares to compute a low-rank CP-decomposition of the 4D convolution kernel tensor into a sum of a small number of rank-one tensors. At the second step, this decomposition is used to replace the original convolutional layer with a sequence of four convolutional layers with small kernels. After such replacement, the entire network is fine-tuned on the training data using standard backpropagation process. We evaluate this approach on two CNNs and show that it yields larger CPU speedups at the cost of lower accuracy drops compared to previous approaches. For the 36-class character classification CNN, our approach obtains a 8.5x CPU speedup of the whole network with only minor accuracy drop (1% from 91% to 90%). For the standard ImageNet architecture (AlexNet), the approach speeds up the second convolution layer by a factor of 4x at the cost of 1% increase of the overall top-5 classification error. | SPEEDING-UP CONVOLUTIONAL NEURAL NETWORKS USING FINE-TUNED CP-DECOMPOSITION |
d231632265 | Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have clear limitations in terms of availability of UID data and dependence of algorithms on pseudo-labels. Herein, we propose a data augmentation method to improve generalization in both adversarial and standard learning by using out-of-distribution (OOD) data that are devoid of the abovementioned issues. We show how to improve generalization theoretically using OOD data in each learning scenario and complement our theoretical analysis with experiments on CIFAR-10, CIFAR-100, and a subset of ImageNet. The results indicate that undesirable features are shared even among image data that seem to have little correlation from a human point of view. We also present the advantages of the proposed method through comparison with other data augmentation methods, which can be used in the absence of UID data. Furthermore, we demonstrate that the proposed method can further improve the existing state-of-the-art adversarial training. | Published as a conference paper at ICLR 2021 REMOVING UNDESIRABLE FEATURE CONTRIBUTIONS USING OUT-OF-DISTRIBUTION DATA |
d6108541 | Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing.In this paper, we present a simple model that is able to generate descriptive sentences given a sample image.This model has a strong focus on the syntax of the descriptions.We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them.The system is then able to infer phrases from a given image sample.Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred.Our approach, which is considerably simpler than state-of-theart models, achieves comparable results on the recently release Microsoft COCO dataset. | Under review as a workshop contribution at ICLR 2015 SIMPLE IMAGE DESCRIPTION GENERATOR VIA A LINEAR PHRASE-BASED MODEL |
d59551640 | Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters to optimal weights and biases. We show how to construct scalable best-response approximations for neural networks by modeling the best-response as a single network whose hidden units are gated conditionally on the regularizer. We justify this approximation by showing the exact best-response for a shallow linear network with L 2 -regularized Jacobian can be represented by a similar gating mechanism. We fit this model using a gradient-based hyperparameter optimization algorithm which alternates between approximating the best-response around the current hyperparameters and optimizing the hyperparameters using the approximate best-response function. Unlike other gradient-based approaches, we do not require differentiating the training loss with respect to the hyperparameters, allowing us to tune discrete hyperparameters, data augmentation hyperparameters, and dropout probabilities. Because the hyperparameters are adapted online, our approach discovers hyperparameter schedules that can outperform fixed hyperparameter values. Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems. We call our networks, which update their own hyperparameters online during training, Self-Tuning Networks (STNs). * Equal contribution. 1 The uniqueness of the arg min is assumed. . Properties of the trace and matrix derivatives, 2007. URL https://web. stanford.edu/˜jduchi/projects/matrix_prop.pdf.Anthony V Fiacco and Yo Ishizuka. Sensitivity and stability analysis for nonlinear programming. programming for hyperparameter optimization and meta-learning. arXiv preprint arXiv:1806.04910, 2018.Yarin Gal and Zoubin Ghahramani. A theoretically grounded application of dropout in recurrent neural networks.tava. Fast and scalable Bayesian deep learning by weight-perturbation in Adam. arXiv preprint arXiv:1806.04854, 2018. . Gradient-based learning applied to document recognition. | SELF-TUNING NETWORKS: BILEVEL OPTIMIZATION OF HYPERPARAMETERS US- ING STRUCTURED BEST-RESPONSE FUNCTIONS |
d256827686 | Gaussian processes (GPs) are an attractive class of machine learning models because of their simplicity and flexibility as building blocks of more complex Bayesian models. Meanwhile, graph neural networks (GNNs) emerged recently as a promising class of models for graph-structured data in semi-supervised learning and beyond. Their competitive performance is often attributed to a proper capturing of the graph inductive bias. In this work, we introduce this inductive bias into GPs to improve their predictive performance for graph-structured data. We show that a prominent example of GNNs, the graph convolutional network, is equivalent to some GP when its layers are infinitely wide; and we analyze the kernel universality and the limiting behavior in depth. We further present a programmable procedure to compose covariance kernels inspired by this equivalence and derive example kernels corresponding to several interesting members of the GNN family. We also propose a computationally efficient approximation of the covariance matrix for scalable posterior inference with large-scale data. We demonstrate that these graph-based kernels lead to competitive classification and regression performance, as well as advantages in computation time, compared with the respective GNNs. Published as a conference paper at ICLR 2023 can be recursively computed if the weights (and biases) in each layer are iid Gaussian. Similar results for other architectures, such as convolution layers and residual connections, were subsequently established in the literature (Novak et al., 2019; Garriga-Alonso et al., 2019).One focus of this work is to establish a similar relationship between GNNs and the limiting GPs. We will derive the covariance kernel that incorporates the graph inductive bias as GNNs do. We start with one of the most widely studied GNNs, the graph convolutional network (GCN)(Kipf & Welling, 2017), and analyze the kernel universality as well as the limiting behavior when the depth also tends to infinity. We then derive covariance kernels from other GNNs by using a programmable procedure that corresponds every building block of a neural network to a kernel operation.Meanwhile, we design efficient computational procedures for posterior inference (i.e., regression and classification). GPs are notoriously difficult to scale because of the cubic complexity with respect to the number of training data. Benchmark graph datasets used by the GNN literature may contain thousands or even millions of labeled nodes(Hu et al., 2020b). The semi-supervised setting worsens the scenario, as the covariance matrix needs to be (recursively) evaluated in full because of the graph convolution operation. We propose a Nyström-like scheme to perform low-rank approximations and apply the approximation recursively on each layer, to yield a low-rank kernel matrix. | Published as a conference paper at ICLR 2023 GRAPH NEURAL NETWORK-INSPIRED KERNELS FOR GAUSSIAN PROCESSES IN SEMI-SUPERVISED LEARN- ING |
d256274926 | In the field of skeleton-based action recognition, current top-performing graph convolutional networks (GCNs) exploit intra-sequence context to construct adaptive graphs for feature aggregation. However, we argue that such context is still local since the rich cross-sequence relations have not been explicitly investigated. | Published as a conference paper at ICLR 2023 GRAPH CONTRASTIVE LEARNING FOR SKELETON- BASED ACTION RECOGNITION |
d204788776 | We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i.e., a sentence). Our formulation provides an alternative perspective that unifies classical word embedding models (e.g., Skip-gram) and modern contextual embeddings (e.g., BERT, XLNet). In addition to enhancing our theoretical understanding of these methods, our derivation leads to a principled framework that can be used to construct new self-supervised tasks. We provide an example by drawing inspirations from related methods based on mutual information maximization that have been successful in computer vision, and introduce a simple self-supervised objective that maximizes the mutual information between a global sentence representation and n-grams in the sentence. Our analysis offers a holistic view of representation learning methods to transfer knowledge and translate progress across multiple domains (e.g., natural language processing, computer vision, audio processing). | A MUTUAL INFORMATION MAXIMIZATION PERSPEC- TIVE OF LANGUAGE REPRESENTATION LEARNING |
d247025714 | In the deep learning era, long video generation of high-quality still remains challenging due to the spatio-temporal complexity and continuity of videos. Existing prior works have attempted to model video distribution by representing videos as 3D grids of RGB values, which impedes the scale of generated videos and neglects continuous dynamics. In this paper, we found that the recent emerging paradigm of implicit neural representations (INRs) that encodes a continuous signal into a parameterized neural network effectively mitigates the issue. By utilizing INRs of video, we propose dynamics-aware implicit generative adversarial network (DI-GAN), a novel generative adversarial network for video generation. Specifically, we introduce (a) an INR-based video generator that improves the motion dynamics by manipulating the space and time coordinates differently and (b) a motion discriminator that efficiently identifies the unnatural motions without observing the entire long frame sequences. We demonstrate the superiority of DIGAN under various datasets, along with multiple intriguing properties, e.g., long video synthesis, video extrapolation, and non-autoregressive video generation. For example, DIGAN improves the previous state-of-the-art FVD score on UCF-101 by 30.7% and can be trained on 128 frame videos of 128×128 resolution, 80 frames longer than the 48 frames of the previous state-of-the-art method. 1 . COIN: Compression with implicit neural representations. arXiv preprint arXiv:2103.03123, 2021a.Emilien Dupont, Yee Whye Teh, and Arnaud Doucet. Generative models as distributions of functions. arXiv preprint arXiv:2102.04776, 2021b. | Published as a conference paper at ICLR 2022 GENERATING VIDEOS WITH DYNAMICS-AWARE IMPLICIT GENERATIVE ADVERSARIAL NETWORKS |
d1915088 | Deep learning is increasingly attracting attention for processing big data. Existing frameworks for deep learning must be set up to specialized computer systems. Gaining sufficient computing resources therefore entails high costs of deployment and maintenance. In this work, we implement a matrix library and deep learning framework that uses JavaScript. It can run on web browsers operating on ordinary personal computers and smartphones. Using JavaScript, deep learning can be accomplished in widely diverse environments without the necessity for software installation. Using GPGPU from WebCL framework, our framework can train large scale convolutional neural networks such as VGGNet and ResNet. In the experiments, we demonstrate their practicality by training VGGNet in a distributed manner using web browsers as the client. | Workshop track -ICLR 2017 DEVELOPMENT OF JAVASCRIPT-BASED DEEP LEARN- ING PLATFORM AND APPLICATION TO DISTRIBUTED TRAINING |
d10370694 | Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension. We present a fine-grained gating mechanism to dynamically combine word-level and character-level representations based on properties of the words. We also extend the idea of fine-grained gating to modeling the interaction between questions and paragraphs for reading comprehension. Experiments show that our approach can improve the performance on reading comprehension tasks, achieving new state-of-the-art results on the Children's Book Test and Who Did What datasets. To demonstrate the generality of our gating mechanism, we also show improved results on a social media tag prediction task. 1 | Published as a conference paper at ICLR 2017 WORDS OR CHARACTERS? FINE-GRAINED GATING FOR READING COMPREHENSION |
d15197911 | In this work we show that adapting Deep Convolutional Neural Network training to the task of boundary detection can result in substantial improvements over the current state-of-the-art in boundary detection. Our contributions consist firstly in combining a careful design of the loss for boundary detection training, a multi-resolution architecture and training with external data to improve the detection accuracy of the current state of the art. When measured on the standard Berkeley Segmentation Dataset, we improve theoptimal dataset scale F-measure from 0.780 to 0.808 -while human performance is at 0.803. We further improve performance to 0.813 by combining deep learning with grouping, integrating the Normalized Cuts technique within a deep network. We also examine the potential of our boundary detector in conjunction with the task of semantic segmentation and demonstrate clear improvements over state-ofthe-art systems. Our detector is fully integrated in the popular Caffe framework and processes a 320x420 image in less than a second. | PUSHING THE BOUNDARIES OF BOUNDARY DETEC- TION USING DEEP LEARNING |
d224832827 | Time-series learning is the bread and butter of data-driven clinical decision support, and the recent explosion in ML research has demonstrated great potential in various healthcare settings.At the same time, medical time-series problems in the wild are challenging due to their highly composite nature: They entail design choices and interactions among components that preprocess data, impute missing values, select features, issue predictions, estimate uncertainty, and interpret models.Despite exponential growth in electronic patient data, there is a remarkable gap between the potential and realized utilization of ML for clinical research and decision support.In particular, orchestrating a real-world project lifecycle poses challenges in engineering (i.e.hard to build), evaluation (i.e.hard to assess), and efficiency (i.e.hard to optimize).Designed to address these issues simultaneously, Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a (i) software toolkit, (ii) empirical standard, and (iii) interface for optimization.Our ultimate goal lies in facilitating transparent and reproducible experimentation with complex inference workflows, providing integrated pathways for (1) personalized prediction, (2) treatment-effect estimation, and (3) information acquisition.Through illustrative examples on real-world data in outpatient, general wards, and intensive-care settings, we illustrate the applicability of the pipeline paradigm on core tasks in the healthcare journey.To the best of our knowledge, Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML. | |
d237940634 | While additional training data improves the robustness of deep neural networks against adversarial examples, it presents the challenge of curating a large number of specific real-world samples. We circumvent this challenge by using additional data from proxy distributions learned by advanced generative models. We first seek to formally understand the transfer of robustness from classifiers trained on proxy distributions to the real data distribution. We prove that the difference between the robustness of a classifier on the two distributions is upper bounded by the conditional Wasserstein distance between them. Next we use proxy distributions to significantly improve the performance of adversarial training on five different datasets. For example, we improve robust accuracy by up to 7.5% and 6.7% in ∞ and 2 threat model over baselines that are not using proxy distributions on the CIFAR-10 dataset. We also improve certified robust accuracy by 7.6% on the CIFAR-10 dataset. We further demonstrate that different generative models bring a disparate improvement in the performance in robust training. We propose a robust discrimination approach to characterize the impact of individual generative models and further provide a deeper understanding of why current state-of-the-art in diffusion-based generative models are a better choice for proxy distribution than generative adversarial networks. † Corresponding author: vvikash@princeton.edu Our code is available at https://github.com/inspire-group/proxy-distributions. 1 Proxy distributions may not necessarily be modeled by generative models. When a proxy distribution is the output of a generative model, we call it synthetic distribution and refer to data sampled from it as synthetic data.Published as a conference paper at ICLR 2022 generative models based on these features? Finally, can we also optimize the selection of individual synthetic samples to maximize the robustness transfer?Published as a conference paper at ICLR 2022 distribution. Next, using robust discriminators we provide a metric based on our analytical bound, which can accurately determine the relative ranking of different proxy distributions in terms of robustness transfer. Our metric can be calculated empirically (using samples from both distributions) and does not require the knowledge of the proxy or real data distribution. Finally, we present our robust training formulation (PORT) which uses synthetic samples generated by the generative model, together with real samples.Notation. We represent the input space by X and corresponding label space as Y. Data is sampled from a joint distribution D that is supported on X × Y. For a label y, we use D | y to denote the conditional distribution of class y. We denote the proxy distribution asD. We denote the neural network for classification by f ∶ X → Z, parameterized by θ, which maps input images to output probability vectors (z). We use h to refer to the classification functions that output labels. For a set S sampled from a distribution D, we useŜ to denote the empirical distribution with respect to set S. We use S ∼ D to denote the sampling of a dataset from a distribution D. We use (x, y) ← D to denote the sampling of a single point from D. | Published as a conference paper at ICLR 2022 ROBUST LEARNING MEETS GENERATIVE MODELS: CAN PROXY DISTRIBUTIONS IMPROVE ADVERSARIAL ROBUSTNESS? |
d15251907 | In a multi-class classification problem, it is standard to model the output of a neural network as a categorical distribution conditioned on the inputs.The output must therefore be positive and sum to one, which is traditionally enforced by a softmax.This probabilistic mapping allows to use the maximum likelihood principle, which leads to the well-known log-softmax loss.However the choice of the softmax function seems somehow arbitrary as there are many other possible normalizing functions.It is thus unclear why the log-softmax loss would perform better than other loss alternatives.In particularVincent et al. (2015)recently introduced a class of loss functions, called the spherical family, for which there exists an efficient algorithm to compute the updates of the output weights irrespective of the output size.In this paper, we explore several loss functions from this family as possible alternatives to the traditional log-softmax.In particular, we focus our investigation on spherical bounds of the log-softmax loss and on two spherical log-likelihood losses, namely the log-Spherical Softmax suggested byVincent et al. (2015)and the log-Taylor Softmax that we introduce.Although these alternatives do not yield as good results as the log-softmax loss on two language modeling tasks, they surprisingly outperform it in our experiments on MNIST and CIFAR10, suggesting that they might be relevant in a broad range of applications. | AN EXPLORATION OF SOFTMAX ALTERNATIVES BELONGING TO THE SPHERICAL LOSS FAMILY |
d57721298 | Convolutional Neural Networks (CNN) have been successful in processing data signals that are uniformly sampled in the spatial domain (e.g., images). However, most data signals do not natively exist on a grid, and in the process of being sampled onto a uniform physical grid suffer significant aliasing error and information loss. Moreover, signals can exist in different topological structures as, for example, points, lines, surfaces and volumes. It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh). To this end, we develop mathematical formulations for Non-Uniform Fourier Transforms (NUFT) to directly, and optimally, sample nonuniform data signals of different topologies defined on a simplex mesh into the spectral domain with no spatial sampling error. The spectral transform is performed in the Euclidean space, which removes the translation ambiguity from works on the graph spectrum. Our representation has four distinct advantages: (1) the process causes no spatial sampling error during the initial sampling, (2) the generality of this approach provides a unified framework for using CNNs to analyze signals of mixed topologies, (3) it allows us to leverage state-of-the-art backbone CNN architectures for effective learning without having to design a particular architecture for a particular data structure in an ad-hoc fashion, and (4) the representation allows weighted meshes where each element has a different weight (i.e., texture) indicating local properties. We achieve results on par with the state-of-the-art for the 3D shape retrieval task, and a new state-of-the-art for the point cloud to surface reconstruction task. | CONVOLUTIONAL NEURAL NETWORKS ON NON- UNIFORM GEOMETRICAL SIGNALS USING EUCLIDEAN SPECTRAL TRANSFORMATION |
d246240676 | To determine causal relationships between two variables, approaches based on Functional Causal Models (FCMs) have been proposed by properly restricting model classes; however, the performance is sensitive to the model assumptions, which makes it difficult to use. In this paper, we provide a novel dynamical-system view of FCMs and propose a new framework for identifying causal direction in the bivariate case. We first show the connection between FCMs and optimal transport, and then study optimal transport under the constraints of FCMs. Furthermore, by exploiting the dynamical interpretation of optimal transport under the FCM constraints, we determine the corresponding underlying dynamical process of the static cause-effect pair data. It provides a new dimension for describing static causal discovery tasks while enjoying more freedom for modeling the quantitative causal influences. In particular, we show that Additive Noise Models (ANMs) correspond to volume-preserving pressureless flows. Consequently, based on their velocity field divergence, we introduce a criterion for determining causal direction. With this criterion, we propose a novel optimal transport-based algorithm for ANMs which is robust to the choice of models and extend it to post-nonlinear models. Our method demonstrated state-of-the-art results on both synthetic and causal discovery benchmark datasets.In particular, we exploit the above idea by leveraging the intrinsic connection between FCMs and optimal transport. Optimal transport is originally introduced by Monge(1781), which has been applied in a large range of applications, not only because it is a natural way to describe moving particles (Ambrosio et al., 2012) but also because of its recent improvement in the computational methods(Cuturi, 2013;Kolouri et al., 2019). Recently, it has also been largely applied to generative models for measuring the distance of probability distributions(Arjovsky et al., 2017;Kolouri et al., 2018;Genevay et al., 2018). Among different optimal transport definitions, the L 2 Wasserstein distance got extensive applications in statistics(Rachev and Rüschendorf, 1998), functional analysis(Barthe, 1998), et al. (McCann, 1997Otto, 1997). The dynamical formulation of the L 2 Wasserstein distance is introduced by Benamou and Brenier(2000)for relaxing the computational costs. We find that in the context of the dynamical formulation, FCMs can be connected with optimal transport. Furthermore, with the dynamical interpretation of optimal transport, one can naturally understand FCMs from a dynamical-system view, which makes it possible to derive new criteria to identify causal direction. Moreover, it also enables us to develop practical algorithms with optimal transport for static causal discovery tasks without learning a regression model. Our main contributions are:1. Dynamical interpretation of FCMs in the bivariate case. We provide dynamical interpretations of optimal transport under the constraints of FCMs. Furthermore, we introduce a time variable, determine the underlying dynamical process under the least action principle (Arnol'd, 2013) for the static bivariate causal discovery task, and characterize properties of the corresponding dynamical systems (Sec. 3.1 and Sec. 3.2).2.A criterion for determining causal relationships between two variables. We study the corresponding dynamical systems of FCMs and prove that ANMs correspond to volume-preserving pressureless flows. Moreover, based on the divergence of their velocity fields, we propose a criterion for determining causal relationships and show that under the identifiability conditions of ANMs it is a valid criterion for ANMs, which can be extended to PNLs directly (Sec. 3.2).3. An optimal transport-based approach (DIVOT) for distinguishing cause from effect between two variables. DIVOT inherits the advantages of one-dimensional optimal transport. It can be computed efficiently and does not require independence tests, learning a regression model, or deriving likelihood functions for complicated distributions. Experimental results show that our method is robust to the choice of models and has a promising performance compared with the state-of-the-art methods on both synthetic and real cause-effect pair datasets (Sec. 4 and Sec. 6). | Published as a conference paper at ICLR 2022 OPTIMAL TRANSPORT FOR CAUSAL DISCOVERY |
d231979542 | Standard Convolutional Neural Networks (CNNs) can be easily fooled by images with small quasi-imperceptible artificial perturbations. As alternatives to CNNs, the recently proposed Capsule Networks (CapsNets) are shown to be more robust to white-box attack than CNNs under popular attack protocols. Besides, the class-conditional reconstruction part of CapsNets is also used to detect adversarial examples. In this work, we investigate the adversarial robustness of CapsNets, especially how the inner workings of CapsNets change when the output capsules are attacked. The first observation is that adversarial examples misled CapsNets by manipulating the votes from primary capsules. Another observation is the high computational cost, when we directly apply multi-step attack methods designed for CNNs to attack CapsNets, due to the computationally expensive routing mechanism. Motivated by these two observations, we propose a novel vote attack where we attack votes of CapsNets directly. Our vote attack is not only effective but also efficient by circumventing the routing process. Furthermore, we integrate our vote attack into the detection-aware attack paradigm, which can successfully bypass the class-conditional reconstruction based detection method. Extensive experiments demonstrate the superior attack performance of our vote attack on CapsNets. | Published as a conference paper at ICLR 2021 EFFECTIVE AND EFFICIENT VOTE ATTACK ON CAP- SULE NETWORKS |
d53776053 | Representation learning is a central challenge across a range of machine learning areas. In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems. Most prior work on representation learning has focused on generative approaches, learning representations that capture all underlying factors of variation in the observation space in a more disentangled or well-ordered manner. In this paper, we instead aim to learn functionally salient representations: representations that are not necessarily complete in terms of capturing all factors of variation in the observation space, but rather aim to capture those factors of variation that are important for decision making -that are "actionable." These representations are aware of the dynamics of the environment, and capture only the elements of the observation that are necessary for decision making rather than all factors of variation, without explicit reconstruction of the observation. We show how these representations can be useful to improve exploration for sparse reward problems, to enable long horizon hierarchical reinforcement learning, and as a state representation for learning policies for downstream tasks. We evaluate our method on a number of simulated environments, and compare it to prior methods for representation learning, exploration, and hierarchical reinforcement learning. * | LEARNING ACTIONABLE REPRESENTATIONS WITH GOAL-CONDITIONED POLICIES |
d256901258 | Recent advances in vision-language pre-training have pushed the state-of-the-art on various vision-language tasks, making machines more capable of multi-modal writing (image-to-text generation) and painting (text-to-image generation). However, few studies investigate if these two essential capabilities can be learned together and boost each other, making a versatile and powerful multi-modal foundation model. In this work, we disclose the potential of symmetric generative vision-language pre-training in learning to write and paint concurrently, and propose a new unified modal model, named DAVINCI, trained with prefix language modeling and prefix image modeling, a simple generative self-supervised objective on image-text pairs. Thanks to the proposed prefix multi-modal modeling framework, DAVINCI is simple to train, scalable to huge data, adaptable to both writing and painting tasks, and also strong on other vision, text, and multi-modal understanding tasks. DAVINCI achieves competitive performance on a wide range of 27 generation/understanding tasks and demonstrates the superiority of combining vision/language generative pre-training. Furthermore, we carefully benchmark the performance of different vision-language pre-training objectives on different scales of pre-training datasets on a heterogeneous and broad distribution coverage. Our results demonstrate the potential of exploiting self-supervision in both language and vision inputs, and establish new, stronger baselines for future comparisons at different data scales. 1 * Work done during the internship at ByteDance AI Lab. † Corresponding author 1 The code and pre-trained models are available at https://github.com/shizhediao/DaVinci. | Published as a conference paper at ICLR 2023 WRITE AND PAINT: GENERATIVE VISION-LANGUAGE MODELS ARE UNIFIED MODAL LEARNERS |
d234358796 | Intrinsically motivated artificial agents learn advantageous behavior without externally-provided rewards. Previously, it was shown that maximizing mutual information between agent actuators and future states, known as the empowerment principle, enables unsupervised stabilization of dynamical systems at upright positions, which is a prototypical intrinsically motivated behavior for upright standing and walking. This follows from the coincidence between the objective of stabilization and the objective of empowerment. Unfortunately, sample-based estimation of this kind of mutual information is challenging. Recently, various variational lower bounds (VLBs) on empowerment have been proposed as solutions; however, they are often biased, unstable in training, and have high sample complexity. In this work, we propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel, which allows us to efficiently calculate an unbiased estimator of empowerment by convex optimization. We demonstrate our solution for sample-based unsupervised stabilization on different dynamical control systems and show the advantages of our method by comparing it to the existing VLB approaches. Specifically, we show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images. Consequently, our method opens a path to wider and easier adoption of empowerment for various applications. 1 | Published as a conference paper at ICLR 2021 EFFICIENT EMPOWERMENT ESTIMATION FOR UNSUPERVISED STABILIZATION |
d257353270 | The Teacher-Student Framework (TSF) is a reinforcement learning setting where a teacher agent guards the training of a student agent by intervening and providing online demonstrations. Assuming optimal, the teacher policy has the perfect timing and capability to intervene in the learning process of the student agent, providing safety guarantee and exploration guidance. Nevertheless, in many real-world settings it is expensive or even impossible to obtain a well-performing teacher policy. In this work, we relax the assumption of a well-performing teacher and develop a new method that can incorporate arbitrary teacher policies with modest or inferior performance. We instantiate an Off-Policy Reinforcement Learning algorithm, termed Teacher-Student Shared Control (TS2C), which incorporates teacher intervention based on trajectory-based value estimation. Theoretical analysis validates that the proposed TS2C algorithm attains efficient exploration and substantial safety guarantee without being affected by the teacher's own performance. Experiments on various continuous control tasks show that our method can exploit teacher policies at different performance levels while maintaining a low training cost. Moreover, the student policy surpasses the imperfect teacher policy in terms of higher accumulated reward in held-out testing environments. Code is available at https://metadriverse.github.io/TS2C. | Published as a conference paper at ICLR 2023 GUARDED POLICY OPTIMIZATION WITH IMPERFECT ONLINE DEMONSTRATIONS |
d208248131 | Granger causality is a widely-used criterion for analyzing interactions in largescale networks. As most physical interactions are inherently nonlinear, we consider the problem of inferring the existence of pairwise Granger causality between nonlinearly interacting stochastic processes from their time series measurements. Our proposed approach relies on modeling the embedded nonlinearities in the measurements using a component-wise time series prediction model based on Statistical Recurrent Units (SRUs). We make a case that the network topology of Granger causal relations is directly inferrable from a structured sparse estimate of the internal parameters of the SRU networks trained to predict the processes' time series measurements. We propose a variant of SRU, called economy-SRU, which, by design has considerably fewer trainable parameters, and therefore less prone to overfitting. The economy-SRU computes a low-dimensional sketch of its high-dimensional hidden state in the form of random projections to generate the feedback for its recurrent processing. Additionally, the internal weight parameters of the economy-SRU are strategically regularized in a group-wise manner to facilitate the proposed network in extracting meaningful predictive features that are highly time-localized to mimic real-world causal events. Extensive experiments are carried out to demonstrate that the proposed economy-SRU based time series prediction model outperforms the MLP, LSTM and attention-gated CNN-based time series models considered previously for inferring Granger causality. . Towards a rigorous assessment of systems biology models: The DREAM3 challenges. PLOS ONE, 5(2):1-18, Feb 2010. . Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations. . Scalable matrix-valued kernel learning for high-dimensional nonlinear multivariate regression and Granger causality. In Pan. Estimating brain connectivity with varying-length time lags using a recurrent neural network. | Published as a conference paper at ICLR 2020 ECONOMY STATISTICAL RECURRENT UNITS FOR INFERRING NONLINEAR GRANGER CAUSALITY |
d16521404 | The goal of a generative model is to capture the distribution underlying the data, typically through latent variables. After training, these variables are often used as a new representation, more effective than the original features in a variety of learning tasks. However, the representations constructed by contemporary generative models are usually point-wise deterministic mappings from the original feature space. Thus, even with representations robust to class-specific transformations, statistically driven models trained on them would not be able to generalize when the labeled data is scarce. Inspired by the stochasticity of the synaptic connections in the brain, we introduce Energy-based Stochastic Ensembles. These ensembles can learn non-deterministic representations, i.e., mappings from the feature space to a family of distributions in the latent space. These mappings are encoded in a distribution over a (possibly infinite) collection of models. By conditionally sampling models from the ensemble, we obtain multiple representations for every input example and effectively augment the data. We propose an algorithm similar to contrastive divergence for training restricted Boltzmann stochastic ensembles. Finally, we demonstrate the concept of the stochastic representations on a synthetic dataset as well as test them in the one-shot learning scenario on MNIST. | LEARNING NON-DETERMINISTIC REPRESENTATIONS WITH ENERGY-BASED ENSEMBLES |
d211259315 | Stochastic regularization of neural networks (e.g. dropout) is a wide-spread technique in deep learning that allows for better generalization. Despite its success, continuous-time models, such as neural ordinary differential equation (ODE), usually rely on a completely deterministic feed-forward operation. This work provides an empirical study of stochastically regularized neural ODE on several image-classification tasks (CIFAR-10, CIFAR-100, TinyImageNet). Building upon the formalism of stochastic differential equations (SDEs), we demonstrate that neural SDE is able to outperform its deterministic counterpart. Further, we show that data augmentation during the training improves the performance of both deterministic and stochastic versions of the same model. However, the improvements obtained by the data augmentation completely eliminate the empirical gains of the stochastic regularization, making the difference in the performance of neural ODE and neural SDE negligible. | Stochasticity in Neural ODEs: An Empirical Study |
d18275776 | We propose Diverse Embedding Neural Network (DENN), a novel architecture for language models (LMs). A DENNLM projects the input word history vector onto multiple diverse low-dimensional sub-spaces instead of a single higherdimensional sub-space as in conventional feed-forward neural network LMs. We encourage these sub-spaces to be diverse during network training through an augmented loss function. Our language modeling experiments on the Penn Treebank data set show the performance benefit of using a DENNLM. | DIVERSE EMBEDDING NEURAL NETWORK LANGUAGE MODELS |
d235658909 | Data poisoning has been proposed as a compelling defense against facial recognition models trained on Web-scraped pictures. Users can perturb images they post online, so that models will misclassify future (unperturbed) pictures. We demonstrate that this strategy provides a false sense of security, as it ignores an inherent asymmetry between the parties: users' pictures are perturbed once and for all before being published (at which point they are scraped) and must thereafter fool all future models-including models trained adaptively against the users' past attacks, or models that use technologies discovered after the attack. We evaluate two systems for poisoning attacks against large-scale facial recognition, Fawkes (500,000+ downloads) and LowKey. We demonstrate how an "oblivious" model trainer can simply wait for future developments in computer vision to nullify the protection of pictures collected in the past. We further show that an adversary with black-box access to the attack can (i) train a robust model that resists the perturbations of collected pictures and (ii) detect poisoned pictures uploaded online. We caution that facial recognition poisoning will not admit an "arms race" between attackers and defenders. Once perturbed pictures are scraped, the attack cannot be changed so any future successful defense irrevocably undermines users' privacy. | Published as a conference paper at ICLR 2022 DATA POISONING WON'T SAVE YOU FROM FACIAL RECOGNITION |
d202541346 | Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed the high-dimensional observations into a lowerdimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space. An important open question is how to learn a representation that is amenable to existing control algorithms? In this paper, we focus on learning representations for locally-linear control algorithms, such as iterative LQR (iLQR). By formulating and analyzing the representation learning problem from an optimal control perspective, we establish three underlying principles that the learned representation should comprise: 1) accurate prediction in the observation space, 2) consistency between latent and observation space dynamics, and 3) low curvature in the latent space transitions. These principles naturally correspond to a loss function that consists of three terms: prediction, consistency, and curvature (PCC). Crucially, to make PCC tractable, we derive an amortized variational bound for the PCC loss function. Extensive experiments on benchmark domains demonstrate that the new variational-PCC learning algorithm benefits from significantly more stable and reproducible training, and leads to superior control performance. Further ablation studies give support to the importance of all three PCC components for learning a good latent space for control.Published as a conference paper at ICLR 2020 the process of encoding, transitioning via the latent dynamics, and then decoding, to adhere to the true observation dynamics. The second is consistency: given the ability to encode a observation trajectory sampled from the true environment, we expect the latent dynamics to be consistent with the encoded trajectory. Finally, curvature: in order to learn a latent space that is specifically amenable to LLC algorithms, we expect the (learned) latent dynamics to exhibit low curvature in order to minimize the approximation error of its first-order Taylor expansion employed by LLC algorithms. Our contributions are thus as follows: (1) We propose the Prediction, Consistency, and Curvature (PCC) framework for learning a latent space that is amenable to LLC algorithms and show that the elements of PCC arise systematically from bounding the suboptimality of the solution of the LLC algorithm in the latent space. (2) We design a latent variable model that adheres to the PCC framework and derive a tractable variational bound for training the model.(3)To the best of our knowledge, our proposed curvature loss for the transition dynamics (in the latent space) is novel. We also propose a direct amortization of the Jacobian calculation in the curvature loss to help training with curvature loss more efficiently. (4) Through extensive experimental comparison, we show that the PCC model consistently outperforms E2C (Watter et al., 2015) and RCE (Banijamali et al., 2018) on a number of control-from-images tasks, and verify via ablation, the importance of regularizing the model to have consistency and low-curvature. . Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Advances in Neural Information Processing Systems, pp. 4754-4765, 2018. Roy De Maesschalck, Delphine Jouan-Rimbaud, and Désiré L Massart. The mahalanobis distance. Chemometrics and intelligent laboratory systems, 50(1):1-18, 2000. Marc Deisenroth and Carl E Rasmussen. Pilco: A model-based and data-efficient approach to policy search. In . Visual foresight: Model-based deep reinforcement learning for vision-based robotic control. arXiv preprint arXiv:1812.00568, 2018. Bernard Espiau, François Chaumette, and Patrick Rives. A new approach to visual servoing in robotics. , et al. Model-based reinforcement learning for atari. arXiv preprint arXiv:1903.00374, 2019. 9 Published as a conference paper at ICLR 2020 Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. She. Singularity-avoiding swing-up control for underactuated three-link gymnast robot using virtual coupling between control torques. International Journal of Robust and Nonlinear Control, 25(2):207-221, 2015. Weiwei Li and Emanuel Todorov. Iterative linear quadratic regulator design for nonlinear biological movement systems. In ICINCO (1), pp. 222-229, 2004. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.Kevin P Murphy. Machine learning: a probabilistic perspective. MIT press, 2012.Erik Ordentlich and Marcelo J Weinberger. A distribution dependent refinement of pinsker's inequality. | Published as a conference paper at ICLR 2020 PREDICTION, CONSISTENCY, CURVATURE: REPRESEN- TATION LEARNING FOR LOCALLY-LINEAR CONTROL |
d211258622 | The early phase of training of deep neural networks is critical for their final performance. In this work, we study how the hyperparameters of stochastic gradient descent (SGD) used in the early phase of training affect the rest of the optimization trajectory. We argue for the existence of the "break-even" point on this trajectory, beyond which the curvature of the loss surface and noise in the gradient are implicitly regularized by SGD. In particular, we demonstrate on multiple classification tasks that using a large learning rate in the initial phase of training reduces the variance of the gradient, and improves the conditioning of the covariance of gradients. These effects are beneficial from the optimization perspective and become visible after the break-even point. Complementing prior work, we also show that using a low learning rate results in bad conditioning of the loss surface even for a neural network with batch normalization layers. In short, our work shows that key properties of the loss surface are strongly influenced by SGD in the early phase of training. We argue that studying the impact of the identified effects on generalization is a promising future direction. * Equal contribution. 1 We define it as K = 1 N N i=1 (gi − g) T (gi − g), where gi = g(x i , yi; θ) is the gradient of the training loss L with respect to θ on xi, N is the number of training examples, and g is the full-batch gradient. | THE BREAK-EVEN POINT ON OPTIMIZATION TRAJEC- TORIES OF DEEP NEURAL NETWORKS |
d256662319 | Recent multi-modal contrastive learning models have demonstrated the ability to learn an embedding space suitable for building strong vision classifiers, by leveraging the rich information in large-scale image-caption datasets. Our work highlights a distinct advantage of this multi-modal embedding space: the ability to diagnose vision classifiers through natural language. The traditional process of diagnosing model behaviors in deployment settings involves labor-intensive data acquisition and annotation. Our proposed method can discover high-error data slices, identify influential attributes and further rectify undesirable model behaviors, without requiring any visual data. Through a combination of theoretical explanation and empirical verification, we present conditions under which classifiers trained on embeddings from one modality can be equivalently applied to embeddings from another modality. On a range of image datasets with known error slices, we demonstrate that our method can effectively identify the error slices and influential attributes, and can further use language to rectify failure modes of the classifier. | Published as a conference paper at ICLR 2023 DIAGNOSING AND RECTIFYING VISION MODELS USING LANGUAGE |
d257050233 | Federated learning is an emerging distributed machine learning framework which jointly trains a global model via a large number of local devices with data privacy protections. Its performance suffers from the non-vanishing biases introduced by the local inconsistent optimal and the rugged client-drifts by the local overfitting. In this paper, we propose a novel and practical method, FedSpeed, to alleviate the negative impacts posed by these problems. Concretely, FedSpeed applies the prox-correction term on the current local updates to efficiently reduce the biases introduced by the prox-term, a necessary regularizer to maintain the strong local consistency. Furthermore, FedSpeed merges the vanilla stochastic gradient with a perturbation computed from an extra gradient ascent step in the neighborhood, thereby alleviating the issue of local over-fitting. Our theoretical analysis indicates that the convergence rate is related to both the communication rounds T and local intervals K with a upper bound O(1/T ) if setting a proper local interval. Moreover, we conduct extensive experiments on the real-world dataset to demonstrate the efficiency of our proposed FedSpeed, which performs significantly faster and achieves the state-of-the-art (SOTA) performance on the general FL experimental settings than several baselines. Our code is available at https://github.com/woodenchild95/FL-Simulator.git.Published as a conference paper at ICLR 2023 Malinovskiy et al. (2020) in different forms. The inconsistency due to the local heterogeneous data will compromise the global convergence during the training process. Eventually it leads to serious client-drifts which can be formulated as x * = i∈ [m] x * i /m. Larger data heterogeneity may enlarge the drifts, thereby degrading the practical training convergence rate and generalization performance.In order to strengthen the local consistency during the local training process, and avoid the client-drifts resulting from the local over-fitting, we propose a novel and practical algorithm, dubbed as FedSpeed.Notably, FedSpeed incorporates two novel components to achieve SOTA performance. i) Firstly, FedSpeed inherits a penalized prox-term to force the local offset to be closer to the initial point at each communication round. However, recognized from Hanzely & Richtárik (2020); Khaled et al.(2019) that the prox-term between global and local solutions may introduce undesirable local training bias, we propose and utilize a prox-correction term to counteract the adverse impact. Indeed, in our theoretical analysis, the implication of the prox-correction term could be considered as a momentumbased term of the weighted local gradients. Via utilizing the historical gradient information, the bias brought by the prox-term can be effectively corrected. ii) Secondly, to avoid the rugged local over-fitting, FedSpeed incorporates a local gradient perturbation via merging the vanilla stochastic gradient with an extra gradient, which can be viewed as taking an extra gradient ascent step for each local update. Based on the analysis in Zhao et al.(2022); van der Hoeven (2020), we demonstrate that the gradient perturbation term could be approximated as adding a penalized squared L2-norm of the stochastic gradients to the original objective function, which can efficiently search for the flatten local minima Andriushchenko & Flammarion (2022) to prevent the local over-fitting problems. | Published as a conference paper at ICLR 2023 FEDSPEED: LARGER LOCAL INTERVAL, LESS COM- MUNICATION ROUND, AND HIGHER GENERALIZATION ACCURACY |
d238259607 | The kernel thinning (KT) algorithm of Dwivedi and Mackey (2021) compresses a probability distribution more effectively than independent sampling by targeting a reproducing kernel Hilbert space (RKHS) and leveraging a less smooth squareroot kernel. Here we provide four improvements. First, we show that KT applied directly to the target RKHS yields tighter, dimension-free guarantees for any kernel, any distribution, and any fixed function in the RKHS. Second, we show that, for analytic kernels like Gaussian, inverse multiquadric, and sinc, target KT admits maximum mean discrepancy (MMD) guarantees comparable to or better than those of square-root KT without making explicit use of a square-root kernel. Third, we prove that KT with a fractional power kernel yields better-than-Monte-Carlo MMD guarantees for non-smooth kernels, like Laplace and Matérn, that do not have square-roots. Fourth, we establish that KT applied to a sum of the target and power kernels (a procedure we call KT+) simultaneously inherits the improved MMD guarantees of power KT and the tighter individual function guarantees of target KT. In our experiments with target KT and KT+, we witness significant improvements in integration error even in 100 dimensions and when compressing challenging differential equation posteriors. | Published as a conference paper at ICLR 2022 GENERALIZED KERNEL THINNING |
d247476396 | Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collection. Significant progress has been made recently in offline model-based reinforcement learning, approaches which leverage a learned dynamics model. This typically involves constructing a probabilistic model, and using the model uncertainty to penalize rewards where there is insufficient data, solving for a pessimistic MDP that lower bounds the true MDP. Existing methods, however, exhibit a breakdown between theory and practice, whereby pessimistic return ought to be bounded by the total variation distance of the model from the true dynamics, but is instead implemented through a penalty based on estimated model uncertainty. This has spawned a variety of uncertainty heuristics, with little to no comparison between differing approaches. In this paper, we compare these heuristics, and design novel protocols to investigate their interaction with other hyperparameters, such as the number of models, or imaginary rollout horizon. Using these insights, we show that selecting these key hyperparameters using Bayesian Optimization produces superior configurations that are vastly different to those currently used in existing hand-tuned state-of-the-art methods, and result in drastically stronger performance. . Deep reinforcement learning in a handful of trials using probabilistic dynamics models. . MOReL : Modelbased offline reinforcement learning. In Advances in Neural Information Processing Systems. 2020.Ilya Kostrikov, Ashvin Nair, and Sergey Levine. Offline reinforcement learning with implicit Q-learning, 2021. | Published as a conference paper at ICLR 2022 REVISITING DESIGN CHOICES IN OFFLINE MODEL-BASED REINFORCEMENT LEARNING |
d211532626 | Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories. † † Source code and pretrained models are available on https://github.com/ | SEMANTICALLY-GUIDED REPRESENTATION LEARN- ING FOR SELF-SUPERVISED MONOCULAR DEPTH |
d246016304 | Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate over thousands of timesteps. A key problem in the inference is to estimate the variance in each timestep of the reverse process. In this work, we present a surprising result that both the optimal reverse variance and the corresponding optimal KL divergence of a DPM have analytic forms w.r.t. its score function. Building upon it, we propose Analytic-DPM, a training-free inference framework that estimates the analytic forms of the variance and KL divergence using the Monte Carlo method and a pretrained score-based model. Further, to correct the potential bias caused by the score-based model, we derive both lower and upper bounds of the optimal variance and clip the estimate for a better result. Empirically, our analytic-DPM improves the log-likelihood of various DPMs, produces high-quality samples, and meanwhile enjoys a 20× to 80× speed up.arXiv:2201.06503v3 [cs.LG] 3 May 2022Published as a conference paper at ICLR 2022 correct the potential bias caused by the score-based model, we derive both lower and upper bounds of the optimal variance and clip its estimate for a better result. Finally, we reveal an interesting relationship between the score function and the data covariance matrix.Analytic-DPM is applicable to a variety of DPMs(Ho et al., 2020;Song et al., 2020a;Nichol & Dhariwal, 2021)in a plug-and-play manner. Empirically, Analytic-DPM consistently improves the log-likelihood of these DPMs and meanwhile enjoys a 20× to 40× speed up. Besides, Analytic-DPM also consistently improves the sample quality of DDIMs (Song et al., 2020a) and requires up to 50 timesteps (which is a 20× to 80× speed up compared to the full timesteps) to achieve a comparable FID to the corresponding baseline. | ANALYTIC-DPM: AN ANALYTIC ESTIMATE OF THE OPTIMAL REVERSE VARIANCE IN DIFFUSION PROB- ABILISTIC MODELS |
d59317065 | We study an interesting problem in training neural network-based models for natural language generation tasks, which we call the representation degeneration problem. We observe that when training a model for natural language generation tasks through likelihood maximization with the weight tying trick, especially with big training datasets, most of the learnt word embeddings tend to degenerate and be distributed into a narrow cone, which largely limits the representation power of word embeddings. We analyze the conditions and causes of this problem and propose a novel regularization method to address it. Experiments on language modeling and machine translation show that our method can largely mitigate the representation degeneration problem and achieve better performance than baseline algorithms. * Authors contribute equally. † This work was done when Jun Gao was an intern at Microsoft Research Asia. 1 The concept of hidden states has multiple meanings in the literature of neural networks. In this paper, we use hidden state as the input to the last softmax layer. | REPRESENTATION DEGENERATION PROBLEM IN TRAINING NATURAL LANGUAGE GENERATION MOD- ELS |
d646822 | We introduce a novel approach to training generative adversarial networks (GANs,Goodfellow et al., 2014), where we train a generator to match a target distribution that converges to the data distribution at the limit of a perfect discriminator. This objective can be interpreted as training a generator to produce samples that lie on the decision boundary of a current discriminator in training at each update, and we call a GAN trained using this algorithm a boundary-seeking GAN (BGAN). This approach can be used to train a generator with discrete output when the generator outputs a parametric conditional distribution. We demonstrate the effectiveness of the proposed algorithm with discrete image data. In contrary to the proposed algorithm, we observe that the recently proposed Gumbel-Softmax technique for re-parametrizing the discrete variables does not work for training a GAN with discrete data. Finally, we notice that the proposed boundaryseeking algorithm works even with continuous variables, and demonstrate its effectiveness with two widely used image data sets, SVHN and CelebA. | Boundary-Seeking Generative Adversarial Networks |
d246634920 | Recently many neural models have been proposed to solve combinatorial puzzles by implicitly learning underlying constraints using their solved instances, such as sudoku or graph coloring (GCP). One drawback of the proposed architectures, which are often based on Graph Neural Networks (GNN)(Zhou et al., 2020), is that they cannot generalize across the size of the output space from which variables are assigned a value, for example, set of colors in a GCP, or board-size in sudoku. We call the output space for the variables as 'value-set'. While many works have demonstrated generalization of GNNs across graph size, there has been no study on how to design a GNN for achieving value-set invariance for problems that come from the same domain. For example, learning to solve 16 × 16 sudoku after being trained on only 9 × 9 sudokus, or coloring a 7 colorable graph after training on 4 colorable graphs. In this work, we propose novel methods to extend GNN based architectures to achieve value-set invariance. Specifically, our model builds on recently proposed Recurrent Relational Networks (RRN)(Palm et al., 2018). Our first approach exploits the graph-size invariance of GNNs by converting a multi-class node classification problem into a binary node classification problem. Our second approach works directly with multiple classes by adding multiple nodes corresponding to the values in the value-set, and then connecting variable nodes to value nodes depending on the problem initialization. Our experimental evaluation on three different combinatorial problems demonstrates that both our models perform well on our novel problem, compared to a generic neural reasoner. Between two of our models, we observe an inherent trade-off: while the binarized model gives better performance when trained on smaller value-sets, multi-valued model is much more memory efficient, resulting in improved performance when trained on larger value-sets, where binarized model fails to train.Published as a conference paper at ICLR 2022 to solve similar problems of a different size, e.g., we may solve a 12 × 12 sudoku after learning to solve a 9 × 9 sudoku. We note that graph based models have been shown to generalize well on varying graph sizes, e.g., finding a satisfying solution of a CNF encoding of a CSP with 100 Boolean-variables, after training on CNF encodings of CSPs with only 40 Boolean-variables(Selsam et al., 2019). However, the model trained using CNF encoding of Boolean-CSPs cannot be used directly for a non-Boolean CSP in which variables take value from a different (larger) value-set.In response, we study value-set invariance in combinatorial puzzles from the same domain. To formally define a similar puzzle with variables taking values from a different value-set, we make use of Lifted CSP (Joslin & Roy, 1997), a (finite) first-order representation that can be ground to CSPs of varying variable and value-set sizes. We note that even though we use Lifted CSPs to define value-set invariance, its complete specification is assumed to be unknown. Specifically, we do not have access to the constraints of the CSP, and thus neural SAT solvers like NeuroSAT (Selsam et al., 2019) can not be used. While training, we only assume access to solved instances along with their constraint graph. We define our problem as: given solved instances and corresponding constraint graph of an unknown ground CSP with a value-set of size k, can we learn neural models that generalize to instances of the same lifted CSP, but with a different value-set of size k (typically k > k)? An example task includes training a model using data of 9 × 9 Sudoku, but testing on a 12 × 12 or a 16 × 16 Sudoku. We build our solution using RRNs as the base architecture. They run GNN on the constraint graph, and employ iterative message passing in a recurrent fashion -the nodes (variables) are then decoded to obtain a solution. We present two ways to enhance RRNs for value-set invariance.Binarized Model: Our first model converts a multi-class classification problem into a binary classification problem by converting a multi-valued variable into multiple Boolean variables, one for each value in the value-set. The binarized constraint graph gets defined as: if there is an edge between two variables in original constraint graph, there are k edges between Boolean nodes corresponding to the same value and the same two variables in the new graph. In addition, all k Boolean variables, corresponding to a multi-valued variable, are connected with each other. This model naturally achieves value-set invariance. At test time, a larger value-set just results in a larger graph size. All GNN weights are tied, and because all the variables in the binarized model are Boolean, embeddings for binary values '0' and '1', trained at training time, are directly applicable at test time.Multi-valued Model: Our second model directly operates on the given multi-valued variables and the corresponding constraint graph, but introduces a value node for every value in the value-set. Each pre-assigned (unassigned) variable node is connected to that (respectively, every possible) value node. The challenge in this model is initializing value nodes at test time when k > k. We circumvent this problem by training upfront k or more value embeddings by randomly sub-selecting a k sized subset during each learning iteration. This random sub-selection exploits the symmetry of value-set elements across instances. During test time, k of the learned embeddings are used.We perform extensive experimental evaluation on puzzles generated from three different structured CSPs: Graph Coloring (GCP), Futoshiki, and Sudoku. We compare two of our models with an NLM (Dong et al., 2019) baseline -a generic neural reasoner, which either fails to scale or performs significantly worse for most test sizes used in our experiments. We also compare our two models along the axes of performance and scalability and discuss their strengths and weaknesses.RELATED WORKThis paper belongs to the broad research area of neural reasoning models, in which neural models learn to solve pure reasoning tasks in a data-driven fashion. Some example tasks include theorem proving(Rocktäschel et al., 2015;Evans & Grefenstette, 2018), logical reasoning (Cingillioglu & Russo, 2019), probabilistic logic reasoning(Manhaeve et al., 2018), classical planning (Dong et al., 2019), probabilistic planning in a known MDP (Tamar et al., 2017;Bajpai et al., 2018), and our focus -combinatorial problems that are instances of an unknown constraint satisfaction problem. | Published as a conference paper at ICLR 2022 NEURAL MODELS FOR OUTPUT-SPACE INVARIANCE IN COMBINATORIAL PROBLEMS |
d252596208 | In offline reinforcement learning, weighted regression is a common method to ensure the learned policy stays close to the behavior policy and to prevent selecting out-of-sample actions. In this work, we show that due to the limited distributional expressivity of policy models, previous methods might still select unseen actions during training, which deviates from their initial motivation. To address this problem, we adopt a generative approach by decoupling the learned policy into two parts: an expressive generative behavior model and an action evaluation model. The key insight is that such decoupling avoids learning an explicitly parameterized policy model with a closed-form expression. Directly learning the behavior policy allows us to leverage existing advances in generative modeling, such as diffusionbased methods, to model diverse behaviors. As for action evaluation, we combine our method with an in-sample planning technique to further avoid selecting outof-sample actions and increase computational efficiency. Experimental results on D4RL datasets show that our proposed method achieves competitive or superior performance compared with state-of-the-art offline RL methods, especially in complex tasks such as AntMaze. We also empirically demonstrate that our method can successfully learn from a heterogeneous dataset containing multiple distinctive but similarly successful strategies, whereas previous unimodal policies fail. The source code is provided at https://github.com/ChenDRAG/SfBC. | OFFLINE REINFORCEMENT LEARNING VIA HIGH- FIDELITY GENERATIVE BEHAVIOR MODELING |
d238419638 | Many machine learning tasks involve learning functions that are known to be invariant or equivariant to certain symmetries of the input data. However, it is often challenging to design neural network architectures that respect these symmetries while being expressive and computationally efficient. For example, Euclidean motion invariant/equivariant graph or point cloud neural networks. We introduce Frame Averaging (FA), a general purpose and systematic framework for adapting known (backbone) architectures to become invariant or equivariant to new symmetry types. Our framework builds on the well known group averaging operator that guarantees invariance or equivariance but is intractable. In contrast, we observe that for many important classes of symmetries, this operator can be replaced with an averaging operator over a small subset of the group elements, called a frame. We show that averaging over a frame guarantees exact invariance or equivariance while often being much simpler to compute than averaging over the entire group. Furthermore, we prove that FA-based models have maximal expressive power in a broad setting and in general preserve the expressive power of their backbone architectures. Using frame averaging, we propose a new class of universal Graph Neural Networks (GNNs), universal Euclidean motion invariant point cloud networks, and Euclidean motion invariant Message Passing (MP) GNNs. We demonstrate the practical effectiveness of FA on several applications including point cloud normal estimation, beyond 2-WL graph separation, and n-body dynamics prediction, achieving state-of-the-art results in all of these benchmarks. * Equal contribution Published as a conference paper at ICLR 2022 where G = {g} denotes the group, ψ ∶ V → R is invariant and Ψ ∶ V → W is equivariant with respect to G. Furthermore, since invariant and equivariant functions are fixed under group averaging, i.e., ψ = φ for invariant φ and Ψ = Φ for equivariant Φ, the above scheme often leads to universal (i.e., maximally expressive) models (Yarotsky, 2021). However, the challenge with equation 1 is that when the cardinality of G is large (e.g., combinatorial groups such as permutations) or infinite (e.g., continuous groups such as rotations), then exact averaging is intractable. In such cases, we are forced to approximate the sum via heuristics or Monte Carlo (MC), thereby sacrificing the exact invariance/equivariance property for computational efficiency, e.g.,Murphy et al. (2018;define heuristic averaging strategies for approximate permutation invariance in GNNs; similarly, and use MC averaging for approximate rotation equivariance in GNNs. A concurrent approach is to find cases where computing the symmetrization operator can be done more efficiently(Sannai et al., 2021).The key observation of the current paper is that the group average in equation 1 can be replaced with an average over a carefully selected subset F(X) ⊂ G while retaining both exact invariance/equivariance and expressive power. Therefore, if F can be chosen so that the cardinality F(X) is mostly small, averaging over F(X) results in both expressive and efficient invariant/equivariant model. We call the set-valued function F ∶ V → 2 G , a frame, and show that it can successfully replace full group averaging if it satisfies a set equivariance property. We name this framework Frame Averaging (FA) and it serves as the basis for the design of invariant/equivariant networks in this paper. . Endowing deep 3d models with rotation invariance based on principal component analysis. | Published as a conference paper at ICLR 2022 FRAME AVERAGING FOR INVARIANT AND EQUIVARIANT NETWORK DESIGN |
d252693397 | With the increasing attention to large vision-language models such as CLIP, there has been a significant amount of effort dedicated to building efficient prompts. Unlike conventional methods of only learning one single prompt, we propose to learn multiple comprehensive prompts to describe diverse characteristics of categories such as intrinsic attributes or extrinsic contexts. However, directly matching each prompt to the same visual feature is problematic, as it pushes the prompts to converge to one point. To solve this problem, we propose to apply optimal transport to match the vision and text modalities. Specifically, we first model images and the categories with visual and textual feature sets. Then, we apply a two-stage optimization strategy to learn the prompts. In the inner loop, we optimize the optimal transport distance to align visual features and prompts by the Sinkhorn algorithm, while in the outer loop, we learn the prompts by this distance from the supervised data. Extensive experiments are conducted on the few-shot recognition task and the improvement demonstrates the superiority of our method. | PLOT: PROMPT LEARNING WITH OPTIMAL TRANS- PORT FOR VISION-LANGUAGE MODELS |
d257427380 | Neural Ordinary Differential Equations (NODEs) are a novel neural architecture, built around initial value problems with learned dynamics which are solved during inference. Thought to be inherently more robust against adversarial perturbations, they were recently shown to be vulnerable to strong adversarial attacks, highlighting the need for formal guarantees. However, despite significant progress in robustness verification for standard feed-forward architectures, the verification of high dimensional NODEs remains an open problem. In this work, we address this challenge and propose GAINS, an analysis framework for NODEs combining three key ideas: (i) a novel class of ODE solvers, based on variable but discrete time steps, (ii) an efficient graph representation of solver trajectories, and (iii) a novel abstraction algorithm operating on this graph representation. Together, these advances enable the efficient analysis and certified training of high-dimensional NODEs, by reducing the runtime from an intractable O(exp(d) + exp(T )) to O(d + T 2 log 2 T ) in the dimensionality d and integration time T . In an extensive evaluation on computer vision (MNIST and FMNIST) and time-series forecasting (PHYSIO-NET) problems, we demonstrate the effectiveness of both our certified training and verification methods. | Published as a conference paper at ICLR 2023 EFFICIENT CERTIFIED TRAINING AND ROBUSTNESS VERIFICATION OF NEURAL ODES |
d13754527 | We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training. In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains. We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances. CROSSGRAD parallelly trains a label and a domain classifier on examples perturbed by loss gradients of each other's objectives. This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions. Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and that (2) data augmentation is a more stable and accurate method than domain adversarial training. 1 * These two authors contributed equally 1 Code and dataset can be found at https | Published as a conference paper at ICLR 2018 GENERALIZING ACROSS DOMAINS VIA CROSS-GRADIENT TRAINING |
d238583557 | Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to successful reasoning about agent-agent interactions. However, it remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance based on a variant of Shapley values. Applying this procedure to stateof-the-art trajectory prediction methods on standard benchmark datasets shows that they are, in fact, unable to reason about interactions. Instead, the past trajectory of the target is the only feature used for predicting its future. For a task with richer social interaction patterns, on the other hand, the tested models do pick up such interactions to a certain extent, as quantified by our feature attribution method. We discuss the limits of the proposed method and its links to causality. † Work done during an internship at Amazon. Contact | YOU MOSTLY WALK ALONE: ANALYZING FEATURE AT- TRIBUTION IN TRAJECTORY PREDICTION |
d231942702 | We present the group equivariant conditional neural process (EquivCNP), a metalearning method with permutation invariance in a data set as in conventional conditional neural processes (CNPs), and it also has transformation equivariance in data space. Incorporating group equivariance, such as rotation and scaling equivariance, provides a way to consider the symmetry of real-world data. We give a decomposition theorem for permutation-invariant and group-equivariant maps, which leads us to construct EquivCNPs with an infinite-dimensional latent space to handle group symmetries. In this paper, we build architecture using Lie group convolutional layers for practical implementation. We show that EquivCNP with translation equivariance achieves comparable performance to conventional CNPs in a 1D regression task. Moreover, we demonstrate that incorporating an appropriate Lie group equivariance, EquivCNP is capable of zero-shot generalization for an image-completion task by selecting an appropriate Lie group equivariance. | GROUP EQUIVARIANT CONDITIONAL NEURAL PRO- CESSES |
d246441850 | Forgetting is often seen as an unwanted characteristic in both human and machine learning. However, we propose that forgetting can in fact be favorable to learning. We introduce forget-and-relearn as a powerful paradigm for shaping the learning trajectories of artificial neural networks. In this process, the forgetting step selectively removes undesirable information from the model, and the relearning step reinforces features that are consistently useful under different conditions. The forget-and-relearn framework unifies many existing iterative training algorithms in the image classification and language emergence literature, and allows us to understand the success of these algorithms in terms of the disproportionate forgetting of undesirable information. We leverage this understanding to improve upon existing algorithms by designing more targeted forgetting operations. Insights from our analysis provide a coherent view on the dynamics of iterative training in neural networks and offer a clear path towards performance improvements.Published as a conference paper at ICLR 2022 shown to improve compositionality in emergent languages(Ren et al., 2020;Vani et al., 2021)and prevent language drift(Lu et al., 2020). We propose that many existing iterative algorithms are instances of a more general forget-and-relearn process, and that their success can be understood by studying the shared underlying mechanism.Forget-and-relearn is an iterative training paradigm which alternates between a forgetting stage and a relearning stage. At a high level, we define a forgetting stage as any process that results in a decrease in training accuracy. More specifically, let D = {(X i , Y i )} i∈[n] be the training dataset. Let U represent uniform noise sampled from (0, 1), which we use as a source of randomness for stochastic functions. Given a neural architecture, let F be the set of functions computable by a neural network with that architecture, and for any N ∈ F, let Acc(N ) = 1 n n i=1 I{N (X i ) = Y i } be the training accuracy of N . Let N t be a network trained on D, and let C := E[Acc(Ñ )] represent the performance of a randomly initialized classifier on D. We say that f : F ×(0, 1) → F is a forgetting operation if two conditions hold: (i) P (Acc(f (N t , U )) < Acc(N t ) | Acc(N t ) > C) = 1; (ii) the mutual information I(f (N t , U ), D) is positive. The first criterion ensures that forgetting will allow for relearning due to a decrease in accuracy. The second criterion captures the idea that forgetting equates to a partial removal of information rather than a complete removal. For standard neural networks, it is sufficient to show that training accuracy is lower than the model prior to forgetting, but higher than chance accuracy for the given task. | Published as a conference paper at ICLR 2022 FORTUITOUS FORGETTING IN CONNECTIONIST NETWORKS |
d252199400 | The success of deep learning is due in large part to our ability to solve certain massive non-convex optimization problems with relative ease. Though non-convex optimization is NP-hard, simple algorithms -often variants of stochastic gradient descent -exhibit surprising effectiveness in fitting large neural networks in practice. We argue that neural network loss landscapes often contain (nearly) a single basin after accounting for all possible permutation symmetries of hidden units a laEntezari et al. (2021). We introduce three algorithms to permute the units of one model to bring them into alignment with a reference model in order to merge the two models in weight space. This transformation produces a functionally equivalent set of weights that lie in an approximately convex basin near the reference model. Experimentally, we demonstrate the single basin phenomenon across a variety of model architectures and datasets, including the first (to our knowledge) demonstration of zero-barrier linear mode connectivity between independently trained ResNet models on CIFAR-10. Additionally, we investigate intriguing phenomena relating model width and training time to mode connectivity. Finally, we discuss shortcomings of the linear mode connectivity hypothesis, including a counterexample to the single basin theory. arXiv:2209.04836v6 [cs.LG] 1 Mar 2023 Published as a conference paper at ICLR 2023 ARCHITECTURE NUM. PERMUTATION SYMMETRIES MLP (3 layers, 512 width) 10 ∧ 3498 VGG16 10 ∧ 35160 ResNet50 10 ∧ 55109Atoms in the observable universe 10 ∧ 82Table 1: Permutation symmetries of deep learning models vs. an upper estimate on the number of atoms in the known, observable universe. Deep learning loss landscapes contain incomprehensible amounts of geometric repetition.We refer to such solutions as being linearly mode connected (LMC) , an extension of mode connectivityDraxler et al., 2018). If true, Conjecture 1 will both materially expand our understanding of how SGD works in the context of deep learning and offer a credible explanation for the preceding phenomena, in particular.Contributions.In this paper, we attempt to uncover what invariances may be responsible for the phenomena cited above and the unreasonable effectiveness of SGD in deep learning. We make the following contributions:1. Matching methods. We propose three algorithms, grounded in concepts and techniques from combinatorial optimization, to align the weights of two independently trained models. Where appropriate, we prove hardness results for these problems and propose approximation algorithms. Our fastest method identifies permutations in mere seconds on current hardware. 2. Relationship to optimization algorithms. We demonstrate by means of counterexample that linear mode connectivity is an emergent property of training procedures, not of model architectures. We connect this result to prior work on the implicit biases of SGD. 3. Experiments, including zero-barrier LMC for ResNets. Empirically, we explore the existence of linear mode connectivity modulo permutation symmetries in experiments across MLPs, CNNs, and ResNets trained on MNIST, CIFAR-10, and CIFAR-100. We contribute the first-ever demonstration of zero-barrier LMC between two independently trained ResNets. We explore the relationship between LMC and model width as well as training time. Finally, we show evidence of our methods' ability to combine models trained on independent datasets into a merged model that outperforms both input models in terms of test loss (but not accuracy) and is no more expensive in compute or memory than either input model.BACKGROUNDAlthough our methods can be applied to arbitrary model architectures, we proceed with the multilayer perceptron (MLP) for its ease of presentation(Bishop, 2007). Consider an L-layer MLP,where σ denotes an element-wise nonlinear activation function. Furthermore, consider a loss, L(Θ), that measures the suitability of a particular set of weights Θ towards some goal, e.g., fitting to a training dataset.Central to our investigation is the phenomenon of permutation symmetries of weight space. Given Θ, we can apply some permutation to the output features of any intermediate layer, , of the model, denoted by a permutation matrix P ∈ S d , 1 z +1 = P P z +1 = P P σ(W z + b ) = P σ(P W z + P b )for σ, an element-wise operator. It follows that as long as we reorder the input weights of layer + 1 according to P , we will have a functionally equivalent model. To be precise, if we define Θ to be identical to Θ with the exception of W = P W , b = P b , W +1 = W +1 P , 1 We denote the set of all d × d permutation matrices -isomorphic to the symmetric group -as S d , to the possible chagrin of pure mathematicians. | GIT RE-BASIN: MERGING MODELS MODULO PERMU- TATION SYMMETRIES |
d231592390 | The volume of "free" data on the internet has been key to the current success of deep learning. However, it also raises privacy concerns about the unauthorized exploitation of personal data for training commercial models. It is thus crucial to develop methods to prevent unauthorized data exploitation. This paper raises the question: can data be made unlearnable for deep learning models? We present a type of error-minimizing noise that can indeed make training examples unlearnable. Error-minimizing noise is intentionally generated to reduce the error of one or more of the training example(s) close to zero, which can trick the model into believing there is "nothing" to learn from these example(s). The noise is restricted to be imperceptible to human eyes, and thus does not affect normal data utility. We empirically verify the effectiveness of error-minimizing noise in both samplewise and class-wise forms. We also demonstrate its flexibility under extensive experimental settings and practicability in a case study of face recognition. Our work establishes an important first step towards making personal data unexploitable to deep learning models.The development of unlearnable examples should take full advantage of the unique characteristics, and more importantly, the weaknesses of DNNs. One well-studied characteristic of DNNs is that they tend to capture more of the high-frequency components of the data . Surprisingly, † Correspondence to: Xingjun Ma Learned-Miller. Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. 2008. | Published as a conference paper at ICLR 2021 UNLEARNABLE EXAMPLES: MAKING PERSONAL DATA UNEXPLOITABLE |
d52197364 | In this work, we attempt to answer a critical question: whether there exists some input sequence that will cause a well-trained discrete-space neural network sequence-to-sequence (seq2seq) model to generate egregious outputs (aggressive, malicious, attacking, etc.). And if such inputs exist, how to find them efficiently. We adopt an empirical methodology, in which we first create lists of egregious output sequences, and then design a discrete optimization algorithm to find input sequences that will cause the model to generate them. Moreover, the optimization algorithm is enhanced for large vocabulary search and constrained to search for input sequences that are likely to be input by real-world users. In our experiments, we apply this approach to dialogue response generation models trained on three real-world dialogue data-sets: Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate malicious responses. We demonstrate that given the trigger inputs our algorithm finds, a significant number of malicious sentences are assigned large probability by the model, which reveals an undesirable consequence of standard seq2seq training. | Arxiv Preprint DETECTING EGREGIOUS RESPONSES IN NEURAL SEQUENCE-TO-SEQUENCE MODELS |
d252668589 | One main challenge in federated learning is the large communication cost of exchanging weight updates from clients to the server at each round. While prior work has made great progress in compressing the weight updates through gradient compression methods, we propose a radically different approach that does not update the weights at all. Instead, our method freezes the weights at their initial random values and learns how to sparsify the random network for the best performance. To this end, the clients collaborate in training a stochastic binary mask to find the optimal sparse random network within the original one. At the end of the training, the final model is a sparse network with random weights -or a subnetwork inside the dense random network. We show improvements in accuracy, communication (less than 1 bit per parameter (bpp)), convergence speed, and final model size (less than 1 bpp) over relevant baselines on MNIST, EMNIST, CIFAR-10, and CIFAR-100 datasets, in the low bitrate regime.(2) Our framework provides efficient communication from clients to the server by requiring (less than) 1 bpp per client while yielding faster convergence and higher accuracy than the baselines.(3) We propose a Bayesian aggregation strategy at the server side to better deal with partial client participation and non-IID data splits.(4) The final model (a sparse network with random weights) can be efficiently represented with a random seed and a binary mask which requires (less than) 1 bpp -at least 32× more efficient storage and communication of the final model with respect to standard FL strategies.(5) We demonstrate the efficacy of our strategy on MNIST, EMNSIT, CIFAR-10, and CIFAR-100 datasets under both IID and non-IID data splits; and show improvements in accuracy, bitrate, convergence speed, and final model size over relevant baselines, under various system configurations.RELATED WORKIn this section, we briefly discuss the related work in (1) communication-efficient FL, (2) pruning for FL, and (3) finding subnetworks in a random network. | Published as a conference paper at ICLR 2023 SPARSE RANDOM NETWORKS FOR COMMUNICATION-EFFICIENT FEDERATED LEARNING |
d246430796 | Privacy is a central tenet of Federated learning (FL), in which a central server trains models without centralizing user data. However, gradient updates used in FL can leak user information. While the most industrial uses of FL are for text applications (e.g. keystroke prediction), the majority of attacks on user privacy in FL have focused on simple image classifiers and threat models that assume honest execution of the FL protocol from the server. We propose a novel attack that reveals private user text by deploying malicious parameter vectors, and which succeeds even with mini-batches, multiple users, and long sequences. Unlike previous attacks on FL, the attack exploits characteristics of both the Transformer architecture and the token embedding, separately extracting tokens and positional embeddings to retrieve high-fidelity text. We argue that the threat model of malicious server states is highly relevant from a user-centric perspective, and show that in this scenario, text applications using transformer models are much more vulnerable than previously thought. * Authors contributed equally. Order chosen randomly. | Published as a conference paper at ICLR 2023 DECEPTICONS: CORRUPTED TRANSFORMERS BREACH PRIVACY IN FEDERATED LEARNING FOR LANGUAGE MODELS |
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