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d238743865 | Understanding the implicit bias of Stochastic Gradient Descent (SGD) is one of the key challenges in deep learning, especially for overparametrized models, where the local minimizers of the loss function L can form a manifold. Intuitively, with a sufficiently small learning rate η, SGD tracks Gradient Descent (GD) until it gets close to such manifold, where the gradient noise prevents further convergence. In such regime, Blanc et al. (2020) proved that SGD with label noise locally decreases a regularizer-like term, the sharpness of loss, tr[∇ 2 L]. The current paper gives a general framework for such analysis by adapting ideas from Katzenberger (1991). It allows in principle a complete characterization for the regularization effect of SGD around such manifold-i.e., the "implicit bias"-using a stochastic differential equation (SDE) describing the limiting dynamics of the parameters, which is determined jointly by the loss function and the noise covariance. This yields some new results:(1)a global analysis of the implicit bias valid for η −2 steps, in contrast to the local analysis of Blanc et al. (2020) that is only valid for η −1.6 steps and (2) allowing arbitrary noise covariance. As an application, we show with arbitrary large initialization, label noise SGD can always escape the kernel regime and only requires O(κ ln d) samples for learning a κ-sparse overparametrized linear model in R d (Woodworth et al., 2020), while GD initialized in the kernel regime requires Ω(d) samples. This upper bound is minimax optimal and improves the previous O(κ 2 ) upper bound (HaoChen et al., 2020). arXiv:2110.06914v4 [cs.LG] 28 Jul 2022Sanjeev Arora, Nadav Cohen, Wei Hu, and Yuping Luo. Implicit regularization in deep matrix factorization. arXiv preprint arXiv:1905.13655, 2019a. | What Happens after SGD Reaches Zero Loss? -A Mathematical Framework |
d227210816 | Formal verification of neural networks (NNs) is a challenging and important problem. Existing efficient complete solvers typically require the branch-and-bound (BaB) process, which splits the problem domain into sub-domains and solves each sub-domain using faster but weaker incomplete verifiers, such as Linear Programming (LP) on linearly relaxed sub-domains. In this paper, we propose to use the backward mode linear relaxation based perturbation analysis (LiRPA) to replace LP during the BaB process, which can be efficiently implemented on the typical machine learning accelerators such as GPUs and TPUs. However, unlike LP, LiRPA when applied naively can produce much weaker bounds and even cannot check certain conflicts of sub-domains during splitting, making the entire procedure incomplete after BaB. To address these challenges, we apply a fast gradient based bound tightening procedure combined with batch splits and the design of minimal usage of LP bound procedure, enabling us to effectively use LiRPA on the accelerator hardware for the challenging complete NN verification problem and significantly outperform LP-based approaches. On a single GPU, we demonstrate an order of magnitude speedup compared to existing LP-based approaches.Recently, a Branch and Bound (BaB) style framework(Bunel et al., 2018;2020b) has been adopted for efficient complete verification. BaB solves the optimization problem min x∈C f (x) to a global * Equal Contribution. | Fast and Complete: Enabling Complete Neural Network Veri- fication with Rapid and Massively Parallel Incomplete Verifiers |
d213896662 | We address the challenging problem of deep representation learning -the efficient adaption of a pre-trained deep network to different tasks. Specifically, we propose to explore gradient-based features. These features are gradients of the model parameters with respect to a task-specific loss given an input sample. Our key innovation is the design of a linear model that incorporates both gradient and activation of the pre-trained network. We demonstrate that our model provides a local linear approximation to an underlying deep model, and discuss important theoretical insights. Moreover, we present an efficient algorithm for the training and inference of our model without computing the actual gradients. Our method is evaluated across a number of representation-learning tasks on several datasets and using different network architectures. Strong results are obtained in all settings, and are well-aligned with our theoretical insights 1 .arXiv:2004.05529v1 [cs.LG] 12 Apr 2020Published as a conference paper at ICLR 2020 is evaluated across tasks, datasets and network architectures and compared against a set of baseline methods. We observe empirically that our model with the gradient features outperforms the traditional activation-based logistic regressor by a significant margin in all settings. Moreover, our model compares favorably against fine-tuning of network parameters.Our main contributions are thus summarized as follows.• We propose a novel representation-learning method. At the core of our method lies in a linear model that builds on gradients of model parameters as the feature representation. • From a theoretical perspective, we claim that our linear model provides a local approximation of fine-tuning an underlying deep model. From a practical perspective, we devise an efficient and scalable algorithm for the training and inference of our method. • We demonstrate strong results of our method across various representation-learning tasks, different network architectures and several datasets. Furthermore, these empirical results are wellaligned with our theoretical insight. | Published as a conference paper at ICLR 2020 GRADIENTS AS FEATURES FOR DEEP REPRESENTATION LEARNING |
d238215749 | Training multi-layer Graph Convolution Networks (GCN) using standard SGD techniques scales poorly as each descent step ends up updating node embeddings for a large portion of the graph. Recent attempts to remedy this sub-sample the graph that reduce compute but introduce additional variance and may offer suboptimal performance. This paper develops the IGLU method that caches intermediate computations at various GCN layers thus enabling lazy updates that significantly reduce the compute cost of descent. IGLU introduces bounded bias into the gradients but nevertheless converges to a first-order saddle point under standard assumptions such as objective smoothness. Benchmark experiments show that IGLU offers up to 1.2% better accuracy despite requiring up to 88% less compute. | IGLU: EFFICIENT GCN TRAINING VIA LAZY UP- DATES |
d238744008 | Emphatic temporal difference (ETD) learning [26] is a successful method to conduct the off-policy value function evaluation with function approximation. Although ETD has been shown to converge asymptotically to a desirable value function, it is well-known that ETD often encounters a large variance so that its sample complexity can increase exponentially fast with the number of iterations. In this work, we propose a new ETD method, called PER-ETD (i.e., PEriodically Restarted-ETD), which restarts and updates the follow-on trace only for a finite period for each iteration of the evaluation parameter. Further, PER-ETD features a design of the logarithmical increase of the restart period with the number of iterations, which guarantees the best trade-off between the variance and bias and keeps both vanishing sublinearly. We show that PER-ETD converges to the same desirable fixed point as ETD, but improves the exponential sample complexity of ETD to be polynomials. Our experiments validate the superior performance of PER-ETD and its advantage over ETD. | PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method * |
d8535316 | Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query. Recently, attention mechanisms have been successfully extended to MC. Typically these methods use attention to focus on a small portion of the context and summarize it with a fixed-size vector, couple attentions temporally, and/or often form a uni-directional attention. In this paper we introduce the Bi-Directional Attention Flow (BIDAF) network, a multi-stage hierarchical process that represents the context at different levels of granularity and uses bidirectional attention flow mechanism to obtain a query-aware context representation without early summarization. Our experimental evaluations show that our model achieves the state-of-the-art results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze test. | BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION |
d247411310 | Offline reinforcement learning, which seeks to utilize offline/historical data to optimize sequential decision-making strategies, has gained surging prominence in recent studies. Due to the advantage that appropriate function approximators can help mitigate the sample complexity burden in modern reinforcement learning problems, existing endeavors usually enforce powerful function representation models (e.g. neural networks) to learn the optimal policies. However, a precise understanding of the statistical limits with function representations, remains elusive, even when such a representation is linear.Towards this goal, we study the statistical limits of offline reinforcement learning with linear model representations. To derive the tight offline learning bound, we design the variance-aware pessimistic value iteration (VAPVI), which adopts the conditional variance information of the value function for time-inhomogeneous episodic linear Markov decision processes (MDPs). VAPVI leverages estimated variances of the value functions to reweight the Bellman residuals in the least-square pessimistic value iteration and provides improved offline learning bounds over the best-known existing results (whereas the Bellman residuals are equally weighted by design). More importantly, our learning bounds are expressed in terms of system quantities, which provide natural instance-dependent characterizations that previous results are short of. We hope our results draw a clearer picture of what offline learning should look like when linear representations are provided. | Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism * |
d3579956 | Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt, it typically performs badly on the training distribution before the shift. Our method, Memory-based Parameter Adaptation, stores examples in memory and then uses a context-based lookup to directly modify the weights of a neural network. Much higher learning rates can be used for this local adaptation, reneging the need for many iterations over similar data before good predictions can be made. As our method is memory-based, it alleviates several shortcomings of neural networks, such as catastrophic forgetting, fast, stable acquisition of new knowledge, learning with an imbalanced class labels, and fast learning during evaluation. We demonstrate this on a range of supervised tasks: large-scale image classification and language modelling. Published as a conference paper at ICLR 2018 Training setting Testing setting store experience output network embedding network episodic memory local adaptation Figure 1: Architecture for the MbPA model. Left: Training usage. The parametric network is used directly and experiences are stored in the memory. Right: Testing setting. The embedding is used to query the episodic memory, the retrieved context is used to adapt the parameters of the output network.the high performance and good generalisation of standard deep models. It combines desirable properties of many existing few-shot, continual learning and language models. We draw inspiration from the theory of complementary learning systems (CLS:McClelland et al., 1995;Leibo et al., 2015;Kumaran et al., 2016), where effective continual, life-long learning necessitates two complementary systems: one that allows for the gradual acquisition of structured knowledge, and another that allows rapid learning of the specifics of individual experiences. As such, MbPA consists of two components: a parametric component (a standard neural network) and a non-parametric component (a neural network augmented with a memory containing previous problem instances). The parametric component learns slowly but generalises well, whereas the non-parametric component rapidly adapts the weights of the parametric component. The non-parametric, instance-based adaptation of the weights is local, in the sense the modification is directly dictated by the inputs to the parametric component. The local adaptation is discarded after the model produces its output, avoiding long term consequences of strong local adaptation (such as overfitting), allowing the weights of the parametric model to learn slowly leading to strong performance and generalisation.The contributions of our work are: (i) proposing an architecture for enhancing powerful parametric models with a fast adaptation mechanism to efficiently cope with changes in the task at hand; (ii) establish connections between our method and attention mechanisms frequently used for querying memories; (iii) present a Bayesian interpretation of the method allowing a principled form of regularisation; (iv) evaluating the method on a range of different tasks: continual learning, incremental learning and data distribution shifts, obtaining promising results. | Published as a conference paper at ICLR 2018 MEMORY-BASED PARAMETER ADAPTATION |
d250072949 | While diffusion models have shown great success in image generation, their noise-inverting generative process does not explicitly consider the structure of images, such as their inherent multi-scale nature. Inspired by diffusion models and the empirical success of coarse-to-fine modelling, we propose a new diffusion-like model that generates images through stochastically reversing the heat equation, a PDE that locally erases fine-scale information when run over the 2D plane of the image. We interpret the solution of the forward heat equation with constant additive noise as a variational approximation in the diffusion latent variable model. Our new model shows emergent qualitative properties not seen in standard diffusion models, such as disentanglement of overall colour and shape in images. Spectral analysis on natural images highlights connections to diffusion models and reveals an implicit coarse-to-fine inductive bias in them. . Diffusion schrödinger bridge with applications to score-based generative modeling. Cascaded diffusion models for high fidelity image generation. Journal of Machine Learning Research, 23(47):1-33, 2022.Emiel Hoogeboom and Tim Salimans. Blurring diffusion models. arXiv preprint arXiv:2209.05557, 2022.Chin-Wei Huang, Jae Hyun Lim, and Aaron C Courville. A variational perspective on diffusion-based generative models and score matching. . Gradient-based learning applied to document recognition. , et al. Photo-realistic single image super-resolution using a generative adversarial network. In | Published as a conference paper at ICLR 2023 GENERATIVE MODELLING WITH INVERSE HEAT DISSIPATION |
d253761231 | As data becomes increasingly vital, a company would be very cautious about releasing data, because the competitors could use it to train high-performance models, thereby posing a tremendous threat to the company's commercial competence. To prevent training good models on the data, we could add imperceptible perturbations to it. Since such perturbations aim at hurting the entire training process, they should reflect the vulnerability of DNN training, rather than that of a single model. Based on this new idea, we seek perturbed examples that are always unrecognized (never correctly classified) in training. In this paper, we uncover them by model checkpoints' gradients, forming the proposed self-ensemble protection (SEP), which is very effective because(1)learning on examples ignored during normal training tends to yield DNNs ignoring normal examples; (2) checkpoints' cross-model gradients are close to orthogonal, meaning that they are as diverse as DNNs with different architectures. That is, our amazing performance of ensemble only requires the computation of training one model. By extensive experiments with 9 baselines on 3 datasets and 5 architectures, SEP is verified to be a new state-of-the-art, e.g., our small ∞ = 2/255 perturbations reduce the accuracy of a CIFAR-10 ResNet18 from 94.56% to 14.68%, compared to 41.35% by the best-known method. Code is available at https://github.com/Sizhe-Chen/SEP.arXiv:2211.12005v3 [cs.LG] 12 Apr 2023Published as a conference paper at ICLR 2023 An effective ensemble demands high diversity of sub-models, which is generally quantified by their gradient similarity(Pang et al., 2019;Yang et al., 2021), i.e., the gradients on the same image from different sub-models should be orthogonal. Surprisingly, we found that checkpoints' gradients are as orthogonal as DNNs with different architectures in the conventional ensemble. In this regard, we argue that intermediate checkpoints are very diverse to form the proposed self-ensemble protection (SEP), challenging existing beliefs on their similarity(Li et al., 2022). | SELF-ENSEMBLE PROTECTION: TRAINING CHECK- POINTS ARE GOOD DATA PROTECTORS |
d231627538 | We propose greedy and local search algorithms for rank-constrained convex optimization, namely solving min rank(A)≤r * R(A) given a convex function R : R m×n → R and a parameter r * . These algorithms consist of repeating two steps: (a) adding a new rank-1 matrix to A and (b) enforcing the rank constraint on A. We refine and improve the theoretical analysis of Shalev-Shwartz et al.(2011), and show that if the rank-restricted condition number of R is κ, a solution A with rank O(r * · min{κ log R(0)−R(A * ) , κ 2 }) and R(A) ≤ R(A * ) + can be recovered, where A * is the optimal solution. This significantly generalizes associated results on sparse convex optimization, as well as rank-constrained convex optimization for smooth functions. We then introduce new practical variants of these algorithms that have superior runtime and recover better solutions in practice. We demonstrate the versatility of these methods on a wide range of applications involving matrix completion and robust principal component analysis. | Published as a conference paper at ICLR 2021 LOCAL SEARCH ALGORITHMS FOR RANK- CONSTRAINED CONVEX OPTIMIZATION |
d203953161 | Manifold assumption in learning states that: the data lie approximately on a manifold of much lower dimension than the input space. Generative models learn to generate data according to the underlying data distribution. Generative models are used in various tasks, such as data augmentation and generating variation of images. This paper addresses the following question: do generative models need to be aware of the topology of the underlying data manifold in which the data lie? This paper suggests that the answer is yes and demonstrates that these can have ramifications on security-critical applications, such as generative-model based defenses for adversarial examples. We provide theoretical and experimental results to support our claims. | ON NEED FOR TOPOLOGY AWARENESS OF GENERA- TIVE MODELS |
d17127188 | State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction problems such as semantic segmentation are structurally different from image classification. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multiscale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy. | MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS |
d256274785 | The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance. In this paper, we first revisit the popular pseudo-labeling methods via a unified sample weighting formulation and demonstrate the inherent quantity-quality trade-off problem of pseudo-labeling with thresholding, which may prohibit learning. To this end, we propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training, effectively exploiting the unlabeled data. We derive a truncated Gaussian function to weight samples based on their confidence, which can be viewed as a soft version of the confidence threshold. We further enhance the utilization of weakly-learned classes by proposing a uniform alignment approach. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification. | SOFTMATCH: ADDRESSING THE QUANTITY-QUALITY TRADE-OFF IN SEMI-SUPERVISED LEARNING |
d252683604 | Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination. Its performance heavily relies on graph augmentation to reflect invariant patterns that are robust to small perturbations; yet it still remains unclear about what graph invariance GCL should capture. Recent studies mainly perform topology augmentations in a uniformly random manner in the spatial domain, ignoring its influence on the intrinsic structural properties embedded in the spectral domain. In this work, we aim to find a principled way for topology augmentations by exploring the invariance of graphs from the spectral perspective. We develop spectral augmentation which guides topology augmentations by maximizing the spectral change. Extensive experiments on both graph and node classification tasks demonstrate the effectiveness of our method in unsupervised learning, as well as the generalization capability in transfer learning and the robustness property under adversarial attacks. Our study sheds light on a general principle for graph topology augmentation. | Published as a conference paper at ICLR 2023 SPECTRAL AUGMENTATION FOR SELF-SUPERVISED LEARNING ON GRAPHS |
d235683116 | We consider the problem of object goal navigation in unseen environments. Solving this problem requires learning of contextual semantic priors, a challenging endeavour given the spatial and semantic variability of indoor environments. Current methods learn to implicitly encode these priors through goal-oriented navigation policy functions operating on spatial representations that are limited to the agent's observable areas. In this work, we propose a novel framework that actively learns to generate semantic maps outside the field of view of the agent and leverages the uncertainty over the semantic classes in the unobserved areas to decide on long term goals. We demonstrate that through this spatial prediction strategy, we are able to learn semantic priors in scenes that can be leveraged in unknown environments. Additionally, we show how different objectives can be defined by balancing exploration with exploitation during searching for semantic targets. Our method is validated in the visually realistic environments of the Matterport3D dataset and show improved results on object goal navigation over competitive baselines.Recently, learned approaches to navigation have been gaining popularity, where initial efforts in addressing target-driven navigation focused on end-to-end reactive approaches that learn to map pixels directly to actionsMousavian et al., 2019). These methods do not have an * Denotes equal contribution. | Published as a conference paper at ICLR 2022 LEARNING TO MAP FOR ACTIVE SEMANTIC GOAL NAVIGATION |
d252918693 | An oft-cited challenge of federated learning is the presence of heterogeneity. Data heterogeneity refers to the fact that data from different clients may follow very different distributions. System heterogeneity refers to the fact that client devices have different system capabilities. A considerable number of federated optimization methods address this challenge. In the literature, empirical evaluations usually start federated training from random initialization. However, in many practical applications of federated learning, the server has access to proxy data for the training task that can be used to pre-train a model before starting federated training. We empirically study the impact of starting from a pre-trained model in federated learning using four standard federated learning benchmark datasets. Unsurprisingly, starting from a pre-trained model reduces the training time required to reach a target error rate and enables the training of more accurate models (up to 40%) than is possible when starting from random initialization. Surprisingly, we also find that starting federated learning from a pre-trained initialization reduces the effect of both data and system heterogeneity. We recommend that future work proposing and evaluating federated optimization methods evaluate the performance when starting from random and pre-trained initializations. We also believe this study raises several questions for further work on understanding the role of heterogeneity in federated optimization. Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360, 2016. | WHERE TO BEGIN? ON THE IMPACT OF PRE- TRAINING AND INITIALIZATION IN FEDERATED LEARNING |
d222341528 | Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network. However, these methods still require multiple forward passes for prediction, leading to a significant computational cost. In this work, we show a surprising result: the benefits of using multiple predictions can be achieved 'for free' under a single model's forward pass. In particular, we show that, using a multi-input multi-output (MIMO) configuration, one can utilize a single model's capacity to train multiple subnetworks that independently learn the task at hand. By ensembling the predictions made by the subnetworks, we improve model robustness without increasing compute. We observe a significant improvement in negative log-likelihood, accuracy, and calibration error on CIFAR10, CIFAR100, ImageNet, and their out-of-distribution variants compared to previous methods. | Published as a conference paper at ICLR 2021 TRAINING INDEPENDENT SUBNETWORKS FOR ROBUST PREDICTION |
d210023826 | Temporal difference (TD) learning is a popular algorithm for policy evaluation in reinforcement learning, but the vanilla TD can substantially suffer from the inherent optimization variance. A variance reduced TD (VRTD) algorithm was proposed by Korda and La (2015), which applies the variance reduction technique directly to the online TD learning with Markovian samples. In this work, we first point out the technical errors in the analysis of VRTD in Korda and La(2015), and then provide a mathematically solid analysis of the non-asymptotic convergence of VRTD and its variance reduction performance. We show that VRTD is guaranteed to converge to a neighborhood of the fixed-point solution of TD at a linear convergence rate. Furthermore, the variance error (for both i.i.d. and Markovian sampling) and the bias error (for Markovian sampling) of VRTD are significantly reduced by the batch size of variance reduction in comparison to those of vanilla TD. As a result, the overall computational complexity of VRTD to attain a given accurate solution outperforms that of TD under Markov sampling and outperforms that of TD under i.i.d. sampling for a sufficiently small conditional number. | REANALYSIS OF VARIANCE REDUCED TEMPORAL DIF- FERENCE LEARNING |
d2158905 | When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns on the DNA sequence known as "motifs". However, it is difficult to manually construct motifs due to their complexity. Recently, external learned memory models have proven to be effective methods for reasoning over inputs and supporting sets. In this work, we present memory matching networks (MMN) for classifying DNA sequences as protein binding sites. Our model learns a memory bank of encoded motifs, which are dynamic memory modules, and then matches a new test sequence to each of the motifs to classify the sequence as a binding or non binding site. | Workshop track -ICLR 2017 MEMORY MATCHING NETWORKS FOR GENOMIC SEQUENCE CLASSIFICATION |
d219981345 | Post-hoc multi-class calibration is a common approach for providing high-quality confidence estimates of deep neural network predictions. Recent work has shown that widely used scaling methods underestimate their calibration error, while alternative Histogram Binning (HB) methods often fail to preserve classification accuracy. When classes have small prior probabilities, HB also faces the issue of severe sample-inefficiency after the conversion into K one-vs-rest class-wise calibration problems. The goal of this paper is to resolve the identified issues of HB in order to provide calibrated confidence estimates using only a small holdout calibration dataset for bin optimization while preserving multi-class ranking accuracy. From an information-theoretic perspective, we derive the I-Max concept for binning, which maximizes the mutual information between labels and quantized logits. This concept mitigates potential loss in ranking performance due to lossy quantization, and by disentangling the optimization of bin edges and representatives allows simultaneous improvement of ranking and calibration performance. To improve the sample efficiency and estimates from a small calibration set, we propose a shared class-wise (sCW) calibration strategy, sharing one calibrator among similar classes (e.g., with similar class priors) so that the training sets of their class-wise calibration problems can be merged to train the single calibrator. The combination of sCW and I-Max binning outperforms the state of the art calibration methods on various evaluation metrics across different benchmark datasets and models, using a small calibration set (e.g., 1k samples for ImageNet). * | MULTI-CLASS UNCERTAINTY CALIBRATION VIA MU- TUAL INFORMATION MAXIMIZATION-BASED BINNING |
d11280172 | The efficiency of graph-based semi-supervised algorithms depends on the graph of instances on which they are applied. The instances are often in a vectorial form before a graph linking them is built. The construction of the graph relies on a metric over the vectorial space that help define the weight of the connection between entities. The classic choice for this metric is usually a distance measure or a similarity measure based on the euclidean norm. We claim that in some cases the euclidean norm on the initial vectorial space might not be the more appropriate to solve the task efficiently. We propose an algorithm that aims at learning the most appropriate vectorial representation for building a graph on which the task at hand is solved efficiently. | A METRIC LEARNING APPROACH FOR GRAPH-BASED LABEL PROPAGATION |
d257255156 | Privacy and security concerns in real-world applications have led to the development of adversarially robust federated models. However, the straightforward combination between adversarial training and federated learning in one framework can lead to the undesired robustness deterioration. We discover that the attribution behind this phenomenon is that the generated adversarial data could exacerbate the data heterogeneity among local clients, making the wrapped federated learning perform poorly. To deal with this problem, we propose a novel framework called Slack Federated Adversarial Training (SFAT), assigning the client-wise slack during aggregation to combat the intensified heterogeneity. Theoretically, we analyze the convergence of the proposed method to properly relax the objective when combining federated learning and adversarial training. Experimentally, we verify the rationality and effectiveness of SFAT on various benchmarked and real-world datasets with different adversarial training and federated optimization methods. | Published as a conference paper at ICLR 2023 COMBATING EXACERBATED HETEROGENEITY FOR ROBUST MODELS IN FEDERATED LEARNING |
d249712412 | End-to-end learning for visual robotic manipulation is known to suffer from sample inefficiency, requiring large numbers of demonstrations. The spatial rototranslation equivariance, or the SE(3)-equivariance can be exploited to improve the sample efficiency for learning robotic manipulation. In this paper, we present SE(3)-equivariant models for visual robotic manipulation from point clouds that can be trained fully end-to-end. By utilizing the representation theory of the Lie group, we construct novel SE(3)-equivariant energy-based models that allow highly sample efficient end-to-end learning. We show that our models can learn from scratch without prior knowledge and yet are highly sample efficient (5∼10 demonstrations are enough). Furthermore, we show that our models can generalize to tasks with (i) previously unseen target object poses, (ii) previously unseen target object instances of the category, and (iii) previously unseen visual distractors. We experiment with 6-DoF robotic manipulation tasks to validate our models' sample efficiency and generalizability. Codes are available at: https | Published as a conference paper at ICLR 2023 EQUIVARIANT DESCRIPTOR FIELDS: SE(3)- EQUIVARIANT ENERGY-BASED MODELS FOR END- TO-END VISUAL ROBOTIC MANIPULATION LEARNING |
d248693515 | This work considers identifying parameters characterizing a physical system's dynamic motion directly from a video whose rendering configurations are inaccessible. Existing solutions require massive training data or lack generalizability to unknown rendering configurations. We propose a novel approach that marries domain randomization and differentiable rendering gradients to address this problem. Our core idea is to train a rendering-invariant state-prediction (RISP) network that transforms image differences into state differences independent of rendering configurations, e.g., lighting, shadows, or material reflectance. To train this predictor, we formulate a new loss on rendering variances using gradients from differentiable rendering. Moreover, we present an efficient, second-order method to compute the gradients of this loss, allowing it to be integrated seamlessly into modern deep learning frameworks. We evaluate our method in rigid-body and deformable-body simulation environments using four tasks: state estimation, system identification, imitation learning, and visuomotor control. We further demonstrate the efficacy of our approach on a real-world example: inferring the state and action sequences of a quadrotor from a video of its motion sequences. Compared with existing methods, our approach achieves significantly lower reconstruction errors and has better generalizability among unknown rendering configurations 1 . * Equal contribution 1 Videos, code, and data are available on the project webpage: | Published as a conference paper at ICLR 2022 RISP: RENDERING-INVARIANT STATE PREDICTOR WITH DIFFERENTIABLE SIMULATION AND RENDER- ING FOR CROSS-DOMAIN PARAMETER ESTIMATION |
d231609191 | With increasingly more data and computation involved in their training, machine learning models constitute valuable intellectual property. This has spurred interest in model stealing, which is made more practical by advances in learning with partial, little, or no supervision. Existing defenses focus on inserting unique watermarks in a model's decision surface, but this is insufficient: the watermarks are not sampled from the training distribution and thus are not always preserved during model stealing. In this paper, we make the key observation that knowledge contained in the stolen model's training set is what is common to all stolen copies. The adversary's goal, irrespective of the attack employed, is always to extract this knowledge or its by-products. This gives the original model's owner a strong advantage over the adversary: model owners have access to the original training data. We thus introduce dataset inference, the process of identifying whether a suspected model copy has private knowledge from the original model's dataset, as a defense against model stealing. We develop an approach for dataset inference that combines statistical testing with the ability to estimate the distance of multiple data points to the decision boundary. Our experiments on CIFAR10, SVHN, CIFAR100 and ImageNet show that model owners can claim with confidence greater than 99% that their model (or dataset as a matter of fact) was stolen, despite only exposing 50 of the stolen model's training points. Dataset inference defends against state-of-the-art attacks even when the adversary is adaptive. Unlike prior work, it does not require retraining or overfitting the defended model. 1 * Work done while an intern at the University of Toronto and Vector Institute 1 Code and models for reproducing our work can be found at github.com/cleverhans-lab/dataset-inference arXiv:2104.10706v1 [stat.ML] 21 Apr 2021Published as a conference paper at ICLR 2021 commercialization, or simply via insider-access. The adversary may (3.a) fine-tune over the victim model, or (3.b) use the victim for data-free distillation(Fang et al., 2019). | Published as a conference paper at ICLR 2021 DATASET INFERENCE: OWNERSHIP RESOLUTION IN MACHINE LEARNING |
d15615409 | When deep learning is applied to visual object recognition, data augmentation is often used to generate additional training data without extra labeling cost. It helps to reduce overfitting and increase the performance of the algorithm. In this paper we investigate if it is possible to use data augmentation as the main component of an unsupervised feature learning architecture. To that end we sample a set of random image patches and declare each of them to be a separate single-image surrogate class. We then extend these trivial one-element classes by applying a variety of transformations to the initial 'seed' patches. Finally we train a convolutional neural network to discriminate between these surrogate classes. The feature representation learned by the network can then be used in various vision tasks. We find that this simple feature learning algorithm is surprisingly successful, achieving competitive classification results on several popular vision datasets (STL-10, CIFAR-10, Caltech-101). | Unsupervised feature learning by augmenting single images |
d30472538 | We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, soundbased navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting. | HoME: a Household Multimodal Environment |
d219721506 | Checkpointing enables training larger models by freeing intermediate activations and recomputing them on demand. Previous checkpointing techniques are difficult to generalize to dynamic models because they statically plan recomputations offline. We present Dynamic Tensor Rematerialization (DTR), a greedy online algorithm for heuristically checkpointing arbitrary models. DTR is extensible and general: it is parameterized by an eviction policy and only collects lightweight metadata on tensors and operators. Though DTR has no advance knowledge of the model or training task, we prove it can train an N -layer feedforward network on an Ω( √ N ) memory budget with only O(N ) tensor operations. Moreover, we identify a general eviction heuristic and show how it allows DTR to automatically provide favorable checkpointing performance across a variety of models and memory budgets. A PREPRINT PerformOp(op, args): Note: Performs op(args), rematerializing any evicted arguments. Wraps every operator invocation. Exclude members of args from eviction for any evicted arg in args: Rematerialize(arg) buf := AllocateBuffer(size(op(args))) res := call op(args), store into buf Permit eviction for members of args again Update metadata for args and res return res Rematerialize(t): op, args := operator and arguments that produced t (from metadata) return PerformOp(op, args) PerformEviction(): Free the tensor chosen by the heuristic AllocateBuffer(b): Note: Wraps every memory allocation. while available memory < b: PerformEviction() return new buffer of size bExecution trace of computing t 7 : t 7 = PerformOp(op 7 , t 5 , t 6 ) [t 5 , t 6 become unevictable] Rematerialize(t 5 ) t 5 = PerformOp(op 5 , t 3 ) [t 3 becomes unevictable] AllocateBuffer(t 5 .size) PerformEviction() #eg, t 2 [t 3 becomes evictable] AllocateBuffer(t 7 .size) PerformEviction() #eg, t 3 t 0 t 2 t 1 t 3 t 4 t 7 Deallocate(t): Note: Wraps every tensor deallocation. Heuristic decides policy for t (e.g., free permanently or simply evict) t 5 t 6 MEMORY BUDGET: 4 = IN MEMORY t 0 | DYNAMIC TENSOR REMATERIALIZATION A PREPRINT |
d209832429 | In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, current task-specific meta-learning methods may either suffer from hand-crafted structure design or lack the capability to capture complex relations between tasks. In this paper, motivated by the way of knowledge organization in knowledge bases, we propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. When a new task arrives, it can quickly find the most relevant structure and tailor the learned structure knowledge to the meta-learner. As a result, the proposed framework not only addresses the challenge of task heterogeneity by a learned meta-knowledge graph, but also increases the model interpretability. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.Published as a conference paper at ICLR 2020 customization on a new task. This inspiration comes from the way of structuring knowledge in knowledge bases (i.e., knowledge graphs). In knowledge bases, the underlying relational structures across text entities are automatically constructed and applied to a new query to improve the searching efficiency. In the meta-learning problem, similarly, we aim at automatically establishing the metaknowledge graph between prior knowledge learned from previous tasks. When a new task arrives, it queries the meta-knowledge graph and quickly attends to the most relevant entities (vertices), and then takes advantage of the relational knowledge structures between them to boost the learning effectiveness with the limited training data.The proposed meta-learning framework is named as Automated Relational Meta-Learning (ARML). Specifically, the ARML automatically builds the meta-knowledge graph from meta-training tasks to memorize and organize learned knowledge from historical tasks, where each vertex represents one type of meta-knowledge (e.g., the common contour between birds and aircrafts). To learn the meta-knowledge graph at meta-training time, for each task, we construct a prototype-based relational graph for each class, where each vertex represents one prototype. The prototype-based relational graph not only captures the underlying relationship behind samples, but alleviates the potential effects of abnormal samples. The meta-knowledge graph is then learned by summarizing the information from the corresponding prototype-based relational graphs of meta-training tasks. After constructing the meta-knowledge graph, when a new task comes in, the prototype-based relational graph of the new task taps into the meta-knowledge graph for acquiring the most relevant knowledge, which further enhances the task representation and facilitates its training process.Our major contributions of the proposed ARML are three-fold: (1) it automatically constructs the meta-knowledge graph to facilitate learning a new task; (2) it empirically outperforms the state-ofthe-art meta-learning algorithms; (3) the meta-knowledge graph well captures the relationship among tasks and improves the interpretability of meta-learning algorithms. | Published as a conference paper at ICLR 2020 AUTOMATED RELATIONAL META-LEARNING |
d238583108 | To mitigate the burden of data labeling, we aim at improving data efficiency for both classification and regression setups in deep learning. However, the current focus is on classification problems while rare attention has been paid to deep regression, which usually requires more human effort to labeling. Further, due to the intrinsic difference between categorical and continuous label space, the common intuitions for classification, e.g. cluster assumptions or pseudo labeling strategies, cannot be naturally adapted into deep regression. To this end, we first delved into the existing data-efficient methods in deep learning and found that they either encourage invariance to data stochasticity (e.g., consistency regularization under different augmentations) or model stochasticity (e.g., difference penalty for predictions of models with different dropout). To take the power of both worlds, we propose a novel χ-model by simultaneously encouraging the invariance to data stochasticity and model stochasticity. Further, the χ-model plays a minimax game between the feature extractor and task-specific heads to further enhance the invariance to model stochasticity. Extensive experiments verify the superiority of the χ-model among various tasks, from a single-value prediction task of age estimation to a dense-value prediction task of keypoint localization, a 2D synthetic and a 3D realistic dataset, as well as a multi-category object recognition task. | Preliminary work χ-MODEL: IMPROVING DATA EFFICIENCY IN DEEP LEARNING WITH A MINIMAX MODEL |
d59608662 | The loss function of deep networks is known to be non-convex but the precise nature of this nonconvexity is still an active area of research. In this work, we study the loss landscape of deep networks through the eigendecompositions of their Hessian matrix. In particular, we examine how important the negative eigenvalues are and the benefits one can observe in handling them appropriately. * This work was done during an internship with the Google Brain team in Montreal. | Negative eigenvalues of the Hessian in deep neural networks |
d252596129 | Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations. This raises limitations in real-world applications like weather prediction where flexible extrapolation at arbitrary spatiotemporal locations is required. We address this problem by introducing a new data-driven approach, DINO, that models a PDE's flow with continuous-time dynamics of spatially continuous functions. This is achieved by embedding spatial observations independently of their discretization via Implicit Neural Representations in a small latent space temporally driven by a learned ODE. This separate and flexible treatment of time and space makes DINO the first data-driven model to combine the following advantages. It extrapolates at arbitrary spatial and temporal locations; it can learn from sparse irregular grids or manifolds; at test time, it generalizes to new grids or resolutions. DINO outperforms alternative neural PDE forecasters in a variety of challenging generalization scenarios on representative PDE systems. | Published as a conference paper at ICLR 2023 CONTINUOUS PDE DYNAMICS FORECASTING WITH IMPLICIT NEURAL REPRESENTATIONS |
d1528564 | Deep neural networks are powerful parametric models that can be trained efficiently using the backpropagation algorithm. Stochastic neural networks combine the power of large parametric functions with that of graphical models, which makes it possible to learn very complex distributions. However, as backpropagation is not directly applicable to stochastic networks that include discrete sampling operations within their computational graph, training such networks remains difficult. We present MuProp, an unbiased gradient estimator for stochastic networks, designed to make this task easier. MuProp improves on the likelihood-ratio estimator by reducing its variance using a control variate based on the first-order Taylor expansion of a mean-field network. Crucially, unlike prior attempts at using backpropagation for training stochastic networks, the resulting estimator is unbiased and well behaved. Our experiments on structured output prediction and discrete latent variable modeling demonstrate that MuProp yields consistently good performance across a range of difficult tasks. | MUPROP: UNBIASED BACKPROPAGATION FOR STOCHASTIC NEURAL NETWORKS |
d239016143 | Q-learning is a popular Reinforcement Learning (RL) algorithm which is widely used in practice with function approximation (Mnih et al., 2015). In contrast, existing theoretical results are pessimistic about Q-learning. For example, (Baird, 1995) shows that Q-learning does not converge even with linear function approximation for linear MDPs. Furthermore, even for tabular MDPs with synchronous updates, Q-learning was shown to have sub-optimal sample complexity (Li et al., 2021; Azar et al., 2013). The goal of this work is to bridge the gap between practical success of Q-learning and the relatively pessimistic theoretical results. The starting point of our work is the observation that in practice, Q-learning is used with two important modifications: (i) training with two networks, called online network and target network simultaneously (online target learning, or OTL) , and (ii) experience replay (ER) (Mnih et al., 2015). While they have been observed to play a significant role in the practical success of Q-learning, a thorough theoretical understanding of how these two modifications improve the convergence behavior of Q-learning has been missing in literature. By carefully combining Q-learning with OTL and reverse experience replay (RER) (a form of experience replay), we present novel methods Q-Rex and Q-RexDaRe (Q-Rex+ data reuse). We show that Q-Rex efficiently finds the optimal policy for linear MDPs (or more generally for MDPs with zero inherent Bellman error with linear approximation (ZIBEL)) and provide non-asymptotic bounds on sample complexity -the first such result for a Q-learning method for this class of MDPs under standard assumptions. Furthermore, we demonstrate that Q-RexDaRe in fact achieves near optimal sample complexity in the tabular setting, improving upon the existing results for vanilla Q-learning. | ONLINE TARGET Q-LEARNING WITH REVERSE EXPE- RIENCE REPLAY: EFFICIENTLY FINDING THE OPTIMAL POLICY FOR LINEAR MDPS |
d239009856 | Many text generation systems benefit from using a retriever to retrieve passages from a textual knowledge corpus (e.g., Wikipedia) and providing these passages as additional context to the generator. For open-ended generation tasks (like generating informative utterances in conversations) many varied passages may be equally relevant and we find that existing methods that jointly train the retriever and generator underperform: the retriever may not find relevant passages even amongst the top-10 and the generator may hence not learn a preference to ground its generated output in them. We propose using an additional guide retriever that is allowed to use the target output and "in hindsight" retrieve relevant passages during training. We model the guide retriever after the posterior distribution Q of passages given the input and the target output and train it jointly with the standard retriever and the generator by maximizing the evidence lower bound (ELBo) in expectation over Q. For informative conversations from the Wizard of Wikipedia dataset, with posterior-guided training, the retriever finds passages with higher relevance in the top-10 (23% relative improvement), the generator's responses are more grounded in the retrieved passage (19% relative improvement) and the end-to-end system produces better overall output (6.4% relative improvement). | HINDSIGHT: POSTERIOR-GUIDED TRAINING OF RE- TRIEVERS FOR IMPROVED OPEN-ENDED GENERATION |
d23884510 | Stacked denoising auto encoders (DAEs) are well known to learn useful deep representations, which can be used to improve supervised training by initializing a deep network. We investigate a training scheme of a deep DAE, where DAE layers are gradually added and keep adapting as additional layers are added. We show that in the regime of mid-sized datasets, this gradual training provides a small but consistent improvement over stacked training in both reconstruction quality and classification error over stacked training on MNIST and CIFAR datasets. | GRADUAL TRAINING METHOD FOR DENOISING AUTO ENCODERS |
d15734062 | We provide novel guaranteed approaches for training feedforward neural networks with sparse connectivity. We leverage on the techniques developed previously for learning linear networks and show that they can also be effectively adopted to learn non-linear networks. We operate on the moments involving label and the score function of the input, and show that their factorization provably yields the weight matrix of the first layer of a deep network under mild conditions. In practice, the output of our method can be employed as effective initializers for gradient descent. | PROVABLE METHODS FOR TRAINING NEURAL NET- WORKS WITH SPARSE CONNECTIVITY |
d257378676 | In this paper, we propose a new self-supervised method, which is called Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images. In DMAE, we corrupt each image by adding Gaussian noises to each pixel value and randomly masking several patches. A Transformer-based encoder-decoder model is then trained to reconstruct the original image from the corrupted one. In this learning paradigm, the encoder will learn to capture relevant semantics for the downstream tasks, which is also robust to Gaussian additive noises. We show that the pre-trained encoder can naturally be used as the base classifier in Gaussian smoothed models, where we can analytically compute the certified radius for any data point. Although the proposed method is simple, it yields significant performance improvement in downstream classification tasks. We show that the DMAE ViT-Base model, which just uses 1/10 parameters of the model developed in recent work(Carlini et al., 2022), achieves competitive or better certified accuracy in various settings. The DMAE ViT-Large model significantly surpasses all previous results, establishing a new state-of-the-art on ImageNet dataset. We further demonstrate that the pre-trained model has good transferability to the CIFAR-10 dataset, suggesting its wide adaptability. Models and code are available at https://github.com/quanlin-wu/dmae. | Published as a conference paper at ICLR 2023 DENOISING MASKED AUTOENCODERS HELP ROBUST CLASSIFICATION |
d253224276 | Generating complex behaviors that satisfy the preferences of non-expert users is a crucial requirement for AI agents. Interactive reward learning from trajectory comparisons (a.k.a. RLHF) is one way to allow non-expert users to convey complex objectives by expressing preferences over short clips of agent behaviors. Even though this parametric method can encode complex tacit knowledge present in the underlying tasks, it implicitly assumes that the human is unable to provide richer feedback than binary preference labels, leading to intolerably high feedback complexity and poor user experience. While providing a detailed symbolic closed-form specification of the objectives might be tempting, it is not always feasible even for an expert user. However, in most cases, humans are aware of how the agent should change its behavior along meaningful axes to fulfill their underlying purpose, even if they are not able to fully specify task objectives symbolically. Using this as motivation, we introduce the notion of Relative Behavioral Attributes, which allows the users to tweak the agent behavior through symbolic concepts (e.g., increasing the softness or speed of agents' movement). We propose two practical methods that can learn to model any kind of behavioral attributes from ordered behavior clips. We demonstrate the effectiveness of our methods on four tasks with nine different behavioral attributes, showing that once the attributes are learned, end users can produce desirable agent behaviors relatively effortlessly, by providing feedback just around ten times. This is over an order of magnitude less than that required by the popular learning-from-humanpreferences baselines. The supplementary video and source code are available at: https://guansuns.github.io/pages/rba. | Published as a conference paper at ICLR 2023 RELATIVE BEHAVIORAL ATTRIBUTES: FILLING THE GAP BETWEEN SYMBOLIC GOAL SPECIFICATION AND REWARD LEARNING FROM HUMAN PREFERENCES |
d258557844 | Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains from AT on some tasks, e.g. commonsense reasoning, named entity recognition. This paper investigates AT from the perspective of the contextualized language representation outputted by PLM encoders. We find the current AT attacks lean to generate sub-optimal adversarial examples that can fool the decoder part but have a minor effect on the encoder. However, we find it necessary to effectively deviate the latter one to allow AT to gain. Based on the observation, we propose simple yet effective Contextualized representation-Adversarial Training (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder. It allows a global optimization of adversarial examples that can fool the entire model. We also find CreAT gives rise to a better direction to optimize the adversarial examples, to let them less sensitive to hyperparameters. Compared to AT, CreAT produces consistent performance gains on a wider range of tasks and is proven to be more effective for language pretraining where only the encoder part is kept for downstream tasks. We achieve the new state-of-the-art performances on a series of challenging benchmarks, e.g.Published as a conference paper at ICLR 2023 decoder, thus generating greater training risk, while may yield a minor impact on the encoder part. When this happens, AT can lead to poor results, or degenerate to random perturbation training (RPT)(Bishop, 1995).Based on the observations, we are motivated that AT facilitates model training through robust representation learning. Those adversarial examples that effectively deviate the contextualized representation are the necessary driver for its success. To this end, we propose simple yet effective Contextualized representation-Adversarial Training (CreAT), to remedy the potential defect of AT. The CreAT attack is explicitly optimized to deviate the contextualized representation. The obtained "global" worst-case adversarial examples can fool the entire model.CreAT contributes to a consistent fine-tuning improvement on a wider range of downstream tasks. We find that this new optimization direction of the attack causes more converged hyperparameters compared to AT. That means CreAT is less sensitive to the inner ascent steps and step sizes. Additionally, we always re-train the decoder from scratch and keep the PLM encoder weights for fine-tuning. As a direct result of that, CreAT is shown to be more effective for language pre-training. We apply CreAT to MLM-style pre-training and achieve the new state-of-the-art performances on a series of challenging benchmarks (e.g. AdvGLUE, HellaSWAG, ANLI). | Published as a conference paper at ICLR 2023 TOWARD ADVERSARIAL TRAINING ON CONTEXTUAL- IZED LANGUAGE REPRESENTATION |
d252683116 | In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics for quantifying the quality of such disentangled representations: disentanglement (D), completeness (C) and informativeness (I).In this work, we first connect this DCI framework to two common notions of linear and nonlinear identifiability, thereby establishing a formal link between disentanglement and the closely-related field of independent component analysis. We then propose an extended DCI-ES framework with two new measures of representation quality-explicitness (E) and size (S)-and point out how D and C can be computed for black-box predictors. Our main idea is that the functional capacity required to use a representation is an important but thus-far neglected aspect of representation quality, which we quantify using explicitness or ease-of-use (E). We illustrate the relevance of our extensions on the MPI3D and Cars3D datasets. * Equal contribution. | DCI-ES: AN EXTENDED DISENTANGLEMENT FRAME- WORK WITH CONNECTIONS TO IDENTIFIABILITY |
d247363675 | Equivariant neural networks enforce symmetry within the structure of their convolutional layers, resulting in a substantial improvement in sample efficiency when learning an equivariant or invariant function. Such models are applicable to robotic manipulation learning which can often be formulated as a rotationally symmetric problem. This paper studies equivariant model architectures in the context of Q-learning and actor-critic reinforcement learning. We identify equivariant and invariant characteristics of the optimal Q-function and the optimal policy and propose equivariant DQN and SAC algorithms that leverage this structure. We present experiments that demonstrate that our equivariant versions of DQN and SAC can be significantly more sample efficient than competing algorithms on an important class of robotic manipulation problems. | SO(2)-EQUIVARIANT REINFORCEMENT LEARNING |
d52937611 | Recurrent neural networks are known for their notorious exploding and vanishing gradient problem (EVGP). This problem becomes more evident in tasks where the information needed to correctly solve them exist over long time scales, because EVGP prevents important gradient components from being back-propagated adequately over a large number of steps. We introduce a simple stochastic algorithm (h-detach) that is specific to LSTM optimization and targeted towards addressing this problem. Specifically, we show that when the LSTM weights are large, the gradient components through the linear path (cell state) in the LSTM computational graph get suppressed. Based on the hypothesis that these components carry information about long term dependencies (which we show empirically), their suppression can prevent LSTMs from capturing them. Our algorithm prevents gradients flowing through this path from getting suppressed, thus allowing the LSTM to capture such dependencies better. We show significant convergence and generalization improvements using our algorithm on various benchmark datasets. | h-DETACH: MODIFYING THE LSTM GRADIENT TO- WARDS BETTER OPTIMIZATION |
d214667180 | Image captioning models have been able to generate grammatically correct and human understandable sentences. However most of the captions convey limited information as the model used is trained on datasets that do not caption all possible objects existing in everyday life. Due to this lack of prior information most of the captions are biased to only a few objects present in the scene, hence limiting their usage in daily life. In this paper, we attempt to show the biased nature of the currently existing image captioning models and present a new image captioning dataset, Egoshots, consisting of 978 real life images with no captions. We further exploit the state of the art pre-trained image captioning and object recognition networks to annotate our images and show the limitations of existing works. Furthermore, in order to evaluate the quality of the generated captions, we propose a new image captioning metric, object based Semantic Fidelity (SF). Existing image captioning metrics can evaluate a caption only in the presence of their corresponding annotations; however, SF allows evaluating captions generated for images without annotations, making it highly useful for real life generated captions. . From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. | Egoshots, AN EGO-VISION LIFE-LOGGING DATASET AND SEMANTIC FIDELITY METRIC TO EVALUATE DIVERSITY IN IMAGE CAPTIONING MODELS |
d220968818 | We show how to assess a language model's knowledge of basic concepts of morality. We introduce the ETHICS dataset, a new benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality. Models predict widespread moral judgments about diverse text scenarios. This requires connecting physical and social world knowledge to value judgements, a capability that may enable us to steer chatbot outputs or eventually regularize open-ended reinforcement learning agents. With the ETHICS dataset, we find that current language models have a promising but incomplete ability to predict basic human ethical judgements. Our work shows that progress can be made on machine ethics today, and it provides a steppingstone toward AI that is aligned with human values. * Equal Contribution. | Published as a conference paper at ICLR 2021 ALIGNING AI WITH SHARED HUMAN VALUES |
d651286 | We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techniques include adding more image transformations to the training data, adding more transformations to generate additional predictions at test time and using complementary models applied to higher resolution images. This paper summarizes our entry in the Imagenet Large Scale Vis ual Recognition Challenge 2013. Our system achieved a top 5 classification error rate of 13.55% using no external data which is over a 20% relative improvement on the previous year's winner. | Some Improvements on Deep Convolutional Neural Network Based Image Classification |
d257254973 | Learning to Optimize (L2O) has drawn increasing attention as it often remarkably accelerates the optimization procedure of complex tasks by "overfitting" specific task types, leading to enhanced performance compared to analytical optimizers. Generally, L2O develops a parameterized optimization method (i.e., "optimizer") by learning from solving sample problems. This data-driven procedure yields L2O that can efficiently solve problems similar to those seen in training, that is, drawn from the same "task distribution". However, such learned optimizers often struggle when new test problems come with a substantial deviation from the training task distribution. This paper investigates a potential solution to this open challenge, by meta-training an L2O optimizer that can perform fast test-time self-adaptation to an out-of-distribution task, in only a few steps. We theoretically characterize the generalization of L2O, and further show that our proposed framework (termed as M-L2O) provably facilitates rapid task adaptation by locating well-adapted initial points for the optimizer weight. Empirical observations on several classic tasks like LASSO, Quadratic and Rosenbrock demonstrate that M-L2O converges significantly faster than vanilla L2O with only 5 steps of adaptation, echoing our theoretical results. Codes are available in https://github.com/VITA-Group/M-L2O. | M-L2O: TOWARDS GENERALIZABLE LEARNING-TO- OPTIMIZE BY TEST-TIME FAST SELF-ADAPTATION |
d239050183 | Traffic forecasting is a challenging problem due to complex road networks and sudden speed changes caused by various events on roads. Several models have been proposed to solve this challenging problem, with a focus on learning the spatio-temporal dependencies of roads. In this work, we propose a new perspective for converting the forecasting problem into a pattern-matching task, assuming that large traffic data can be represented by a set of patterns. To evaluate the validity of this new perspective, we design a novel traffic forecasting model called Pattern-Matching Memory Networks (PM-MemNet), which learns to match input data to representative patterns with a key-value memory structure. We first extract and cluster representative traffic patterns that serve as keys in the memory. Then, by matching the extracted keys and inputs, PM-MemNet acquires the necessary information on existing traffic patterns from the memory and uses it for forecasting. To model the spatio-temporal correlation of traffic, we proposed a novel memory architecture, GCMem, which integrates attention and graph convolution. The experimental results indicate that PM-MemNet is more accurate than state-of-the-art models, such as Graph WaveNet, with higher responsiveness. We also present a qualitative analysis describing how PM-MemNet works and achieves higher accuracy when road speed changes rapidly. | LEARNING TO REMEMBER PATTERNS: PATTERN MATCHING MEMORY NETWORKS FOR TRAFFIC FORE- CASTING |
d252595953 | Learning mappings between infinite dimensional function spaces has achieved empirical success in many disciplines of machine learning, including generative modeling, machine learning solving Partial Difference Equations , functional data analysis, causal inference, and multi-agent reinforcement learning. In this paper, we study the statistical limit of learning a Hilbert-Schmidt operator between two infinite-dimensional Sobolev reproducing kernel Hilbert spaces. We establish the information-theoretic lower bound in terms of the Sobolev Hilbert-Schmidt norm and show that a regularization that learns the spectral components below the bias contour and ignores the ones that above the variance contour can achieve the optimal learning rate. At the same time, the spectral components between the bias and variance contours give us the flexibility in designing computationally feasible machine learning algorithms. Based on this observation, we develop a multilevel kernel operator learning algorithm that is optimal when learning linear operators between infinite-dimensional function spaces. which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. arXiv:2209.14430v3 [cs.LG] 24 Jul 2023 networks for parametric pdes. arXiv preprint arXiv:2005.03180. Blanchet, Jose, and Zhipeng Liu. 2016. Malliavin-based multilevel monte carlo estimators for densities of max-stable processes. In International conference on monte carlo and quasi-monte carlo methods in scientific computing, 75-97. Springer. Blanchet, Jose H, and Peter W Glynn. 2015. Unbiased monte carlo for optimization and functions of expectations via multi-level randomization. In 2015 winter simulation conference (wsc), 3656-3667. IEEE. Boullé, Nicolas, Seick Kim, Tianyi Shi, and Alex Townsend. 2022. Learning green's functions associated with time-dependent partial differential equations. Journal of Machine Learning Research 23 (218): 1-34. Caponnetto, Andrea, and Ernesto De Vito. 2007. Optimal rates for the regularized least-squares algorithm. Foundations of Computational Mathematics 7 (3): 331-368. Chen, Guanting, Alex Shkolnik, and Kay Giesecke. 2020. Unbiased simulation estimators for path integrals of diffusions. In 2020 winter simulation conference (wsc), 277-288. IEEE. Chen, Tianping, and Hong Chen. 1995. Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems. IEEE Transactions on Neural Networks 6 (4): 911-917. . 2022. The cost-accuracy trade-off in operator learning with neural networks. arXiv preprint arXiv:2203.13181. | Working Paper. Do not distribute |
d209202200 | Answering compositional questions that require multiple steps of reasoning against text is challenging, especially when they involve discrete, symbolic operations. Neural module networks (NMNs) learn to parse such questions as executable programs composed of learnable modules, performing well on synthetic visual QA domains. However, we find that it is challenging to learn these models for non-synthetic questions on open-domain text, where a model needs to deal with the diversity of natural language and perform a broader range of reasoning. We extend NMNs by: (a) introducing modules that reason over a paragraph of text, performing symbolic reasoning (such as arithmetic, sorting, counting) over numbers and dates in a probabilistic and differentiable manner; and (b) proposing an unsupervised auxiliary loss to help extract arguments associated with the events in text. Additionally, we show that a limited amount of heuristically-obtained question program and intermediate module output supervision provides sufficient inductive bias for accurate learning. Our proposed model significantly outperforms state-of-the-art models on a subset of the DROP dataset that poses a variety of reasoning challenges that are covered by our modules. | Published as a conference paper at ICLR 2020 NEURAL MODULE NETWORKS FOR REASONING OVER TEXT |
d222133021 | In attempts to produce machine learning models less reliant on spurious patterns in training data, researchers have recently proposed a human-in-the-loop process for generating counterfactually augmented datasets. As applied in NLP, given some documents and their (initial) labels, humans are tasked with revising the text to make a (given) counterfactual label applicable. Importantly, the instructions prohibit edits that are not necessary to flip the applicable label. Models trained on the augmented (original and revised) data have been shown to rely less on semantically irrelevant words and to generalize better out-of-domain. While this work draws on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model is not clear nor are the principles underlying the observed improvements in out-of-domain evaluation. In this paper, we explore a toy analog, using linear Gaussian models. Our analysis reveals interesting relationships between causal models, measurement noise, out-of-domain generalization, and reliance on spurious signals. Interestingly our analysis suggests that data corrupted by adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain. This analysis yields interesting insights that help to explain the efficacy of counterfactually augmented data. Finally, we present a large-scale empirical study that supports this hypothesis. | Explaining The Efficacy of Counterfactually-Augmented Data |
d3693512 | We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them. We validate DEN on multiple public datasets in lifelong learning scenarios on multiple public datasets, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch model with substantially fewer number of parameters. | LIFELONG LEARNING WITH DYNAMICALLY EXPAND- ABLE NETWORKS |
d246823055 | Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves the state-of-the-art (SOTA) performance. However, existing work is mainly built on a small memory buffer containing a few original data, which cannot fully characterize the old data distribution. In this work, we propose memory replay with data compression (MRDC) to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer. Observing that the trade-off between the quality and quantity of compressed data is highly nontrivial for the efficacy of memory replay, we propose a novel method based on determinantal point processes (DPPs) to efficiently determine an appropriate compression quality for currently-arrived training samples. In this way, using a naive data compression algorithm with a properly selected quality can largely boost recent strong baselines by saving more compressed data in a limited storage space. We extensively validate this across several benchmarks of class-incremental learning and in a realistic scenario of object detection for autonomous driving. , et al. Imagenet large scale visual recognition challenge. | Published as a conference paper at ICLR 2022 MEMORY REPLAY WITH DATA COMPRESSION FOR CONTINUAL LEARNING |
d256459559 | Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which formulate a wide range of important real-world applications. Cut selection-which aims to select a proper subset of the candidate cuts to improve the efficiency of solving MILPs-heavily depends on (P1) which cuts should be preferred, and (P2) how many cuts should be selected. Although many modern MILP solvers tackle (P1)-(P2) by manually designed heuristics, machine learning offers a promising approach to learn more effective heuristics from MILPs collected from specific applications. However, many existing learning-based methods focus on learning which cuts should be preferred, neglecting the importance of learning the number of cuts that should be selected. Moreover, we observe from extensive empirical results that (P3) what order of selected cuts should be preferred has a significant impact on the efficiency of solving MILPs as well. To address this challenge, we propose a novel hierarchical sequence model (HEM) to learn cut selection policies via reinforcement learning. Specifically, HEM consists of a two-level model: (1) a higher-level model to learn the number of cuts that should be selected, (2) and a lower-level model-that formulates the cut selection task as a sequence to sequence learning problem-to learn policies selecting an ordered subset with the size determined by the higher-level model. To the best of our knowledge, HEM is the first method that can tackle (P1)-(P3) in cut selection simultaneously from a data-driven perspective. Experiments show that HEM significantly improves the efficiency of solving MILPs compared to human-designed and learning-based baselines on both synthetic and large-scale real-world MILPs, including MIPLIB 2017. Moreover, experiments demonstrate that HEM well generalizes to MILPs that are significantly larger than those seen during training. * Equal contribution. | Published as a conference paper at ICLR 2023 LEARNING CUT SELECTION FOR MIXED-INTEGER LINEAR PROGRAMMING VIA HIERARCHICAL SEQUENCE MODEL |
d213659918 | Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features. We study the possibility of preventing such adversarial inference, yet without too much accuracy degradation. We propose a generic method to revise the neural network to boost the challenge of inferring input attributes from features, while maintaining highly accurate outputs. In particular, the method transforms real-valued features into complex-valued ones, in which the input is hidden in a randomized phase of the transformed features. The knowledge of the phase acts like a key, with which any party can easily recover the output from the processing result, but without which the party can neither recover the output nor distinguish the original input. Preliminary experiments on various datasets and network structures have shown that our method significantly diminishes the adversary's ability in inferring about the input while largely preserves the resulting accuracy. | INTERPRETABLE COMPLEX-VALUED NEURAL NET- WORKS FOR PRIVACY PROTECTION |
d203610572 | We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike. In this work, we: (1) prove the widespread existence of suboptimal local minima in the loss landscape of neural networks, and we use our theory to find examples; (2) show that small-norm parameters are not optimal for generalization; (3) demonstrate that ResNets do not conform to wide-network theories, such as the neural tangent kernel, and that the interaction between skip connections and batch normalization plays a role; (4) find that rank does not correlate with generalization or robustness in a practical setting. * Authors contributed equally. | TRUTH OR BACKPROPAGANDA? AN EMPIRICAL IN- VESTIGATION OF DEEP LEARNING THEORY |
d251066662 | The problem of how to genetically modify cells in order to maximize a certain cellular phenotype has taken center stage in drug development over the last few years (with, for example, genetically edited CAR-T, CAR-NK, and CAR-NKT cells entering cancer clinical trials). Exhausting the search space for all possible genetic edits (perturbations) or combinations thereof is infeasible due to cost and experimental limitations. This work provides a theoretically sound framework for iteratively exploring the space of perturbations in pooled batches in order to maximize a target phenotype under an experimental budget. Inspired by this application domain, we study the problem of batch query bandit optimization and introduce the Optimistic Arm Elimination (OAE) principle designed to find an almost optimal arm under different functional relationships between the queries (arms) and the outputs (rewards). We analyze the convergence properties of OAE by relating it to the Eluder dimension of the algorithm's function class and validate that OAE outperforms other strategies in finding optimal actions in experiments on simulated problems, public datasets well-studied in bandit contexts, and in genetic perturbation datasets when the regression model is a deep neural network. OAE also outperforms the benchmark algorithms in 3 of 4 datasets in the GeneDisco experimental planning challenge. | Published as a conference paper at ICLR 2023 NEURAL DESIGN FOR GENETIC PERTURBATION EX- PERIMENTS |
d214495004 | We are interested in derivative-free optimization of high-dimensional functions. The sample complexity of existing methods is high and depends on problem dimensionality, unlike the dimensionality-independent rates of first-order methods. The recent success of deep learning suggests that many datasets lie on low-dimensional manifolds that can be represented by deep nonlinear models. We therefore consider derivative-free optimization of a high-dimensional function that lies on a latent low-dimensional manifold. We develop an online learning approach that learns this manifold while performing the optimization. In other words, we jointly learn the manifold and optimize the function. Our analysis suggests that the presented method significantly reduces sample complexity. We empirically evaluate the method on continuous optimization benchmarks and high-dimensional continuous control problems. Our method achieves significantly lower sample complexity than Augmented Random Search, Bayesian optimization, covariance matrix adaptation (CMA-ES), and other derivative-free optimization algorithms. | Published as a conference paper at ICLR 2020 LEARNING TO GUIDE RANDOM SEARCH |
d248572465 | Based on large-scale pre-trained multilingual representations, recent cross-lingual transfer methods have achieved impressive transfer performances. However, the performance of target languages still lags far behind the source language. In this paper, our analyses indicate such a performance gap is strongly associated with the cross-lingual representation discrepancy. To achieve better cross-lingual transfer performance, we propose the cross-lingual manifold mixup (X-MIXUP) method, which adaptively calibrates the representation discrepancy and gives compromised representations for target languages. Experiments on the XTREME benchmark show X-MIXUP achieves 1.8% performance gains on multiple text understanding tasks, compared with strong baselines, and reduces the cross-lingual representation discrepancy significantly. | ENHANCING CROSS-LINGUAL TRANSFER BY MANI- FOLD MIXUP |
d18166574 | A key challenge in designing convolutional network models is sizing them appropriately. Many factors are involved in these decisions, including number of layers, feature maps, kernel sizes, etc. Complicating this further is the fact that each of these influence not only the numbers and dimensions of the activation units, but also the total number of parameters. In this paper we focus on assessing the independent contributions of three of these linked variables: The numbers of layers, feature maps, and parameters. To accomplish this, we employ a recursive convolutional network whose weights are tied between layers; this allows us to vary each of the three factors in a controlled setting. We find that while increasing the numbers of layers and parameters each have clear benefit, the number of feature maps (and hence dimensionality of the representation) appears ancillary, and finds most of its benefit through the introduction of more weights. Our results (i) empirically confirm the notion that adding layers alone increases computational power, within the context of convolutional layers, and (ii) suggest that precise sizing of convolutional feature map dimensions is itself of little concern; more attention should be paid to the number of parameters in these layers instead. | Understanding Deep Architectures using a Recursive Convolutional Network |
d3525710 | Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective. In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression. * Equal contribution 1 Despite the name, CNNs typically use cross-correlation instead of convolution in the forward pass. In this paper we will generally use the term cross-correlation, or correlation for short. 2 To be more precise: although the symmetry group of the plane contains more than just translations, the translations form a subgroup that acts on the plane. In the case of the sphere there is no coherent way to define a composition for points on the sphere, and so the sphere cannot act on itself (it is not a group). For this reason, we must consider the whole of SO(3). | Published as a conference paper at ICLR 2018 SPHERICAL CNNS |
d256827775 | We propose a conditional positional encoding (CPE) scheme for vision Transformers (Dosovitskiy et al., 2021;Touvron et al., 2020). Unlike previous fixed or learnable positional encodings that are predefined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a result, CPE can easily generalize to the input sequences that are longer than what the model has ever seen during the training. Besides, CPE can keep the desired translation equivalence in vision tasks, resulting in improved performance. We implement CPE with a simple Position Encoding Generator (PEG) to get seamlessly incorporated into the current Transformer framework. Built on PEG, we present Conditional Position encoding Vision Transformer (CPVT). We demonstrate that CPVT has visually similar attention maps compared to those with learned positional encodings and delivers outperforming results. Our Code | Published as a conference paper at ICLR 2023 CONDITIONAL POSITIONAL ENCODINGS FOR VISION TRANSFORMERS |
d6820006 | Ensembles of neural networks are known to be much more robust and accurate than individual networks. However, training multiple deep networks for model averaging is computationally expensive. In this paper, we propose a method to obtain the seemingly contradictory goal of ensembling multiple neural networks at no additional training cost. We achieve this goal by training a single neural network, converging to several local minima along its optimization path and saving the model parameters. To obtain repeated rapid convergence, we leverage recent work on cyclic learning rate schedules. The resulting technique, which we refer to as Snapshot Ensembling, is simple, yet surprisingly effective. We show in a series of experiments that our approach is compatible with diverse network architectures and learning tasks. It consistently yields lower error rates than state-of-the-art single models at no additional training cost, and compares favorably with traditional network ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain error rates of 3.4% and 17.4% respectively. * Authors contribute equally. | Published as a conference paper at ICLR 2017 SNAPSHOT ENSEMBLES: TRAIN 1, GET M FOR FREE |
d59336240 | We explore various methods for computing sentence representations from pretrained word embeddings without any training, i.e., using nothing but random parameterizations. Our aim is to put sentence embeddings on more solid footing by 1) looking at how much modern sentence embeddings gain over random methods-as it turns out, surprisingly little; and by 2) providing the field with more appropriate baselines going forward-which are, as it turns out, quite strong. We also make important observations about proper experimental protocol for sentence classification evaluation, together with recommendations for future research. * Work done as an intern at Facebook AI Research. 1 Code available at https://github.com/facebookresearch/randsent. We go down a well-paved avenue of exploration in the machine learning research community, and exploit an insight originally due to Cover (1965): "A complex pattern-classification problem, cast in a high-dimensional space nonlinearly, is more likely to be linearly separable than in a lowdimensional space, provided that the space is not densely populated." That is, we examine three types of models for obtaining randomly computed sentence representations from pre-trained word embeddings: bag of random embedding projections, randomly initialized recurrent networks and echo state networks.Our goal is not to obtain a new state of the art, but to put current state of the art methods on more solid footing by 1) looking at how much they gain compared to random methods; and 2) providing the field with more solid baselines going forward. We make several important observations about proper experimental protocol for sentence classification evaluation; and finish with a list of takeaway recommendations. | NO TRAINING REQUIRED: EXPLORING RANDOM EN- CODERS FOR SENTENCE CLASSIFICATION |
d10872458 | Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that runs backward from visible to latent, estimating the posterior distribution of latent given visible. We propose a novel interpretation of the wake-sleep algorithm which suggests that better estimators of the gradient can be obtained by sampling latent variables multiple times from the inference network. This view is based on importance sampling as an estimator of the likelihood, with the approximate inference network as a proposal distribution. This interpretation is confirmed experimentally, showing that better likelihood can be achieved with this reweighted wake-sleep procedure, which also provides a natural way to estimate the likelihood itself. Based on this interpretation, we propose that a sigmoid belief network is not sufficiently powerful for the layers of the inference network, in order to recover a good estimator of the posterior distribution of latent variables. Our experiments show that using a more powerful layer model, such as NADE, yields substantially better generative models.1 | Reweighted Wake-Sleep |
d219687094 | The properties of flat minima in the empirical risk landscape of neural networks have been debated for some time. Increasing evidence suggests they possess better generalization capabilities with respect to sharp ones. In this work we first discuss the relationship between alternative measures of flatness: The local entropy, which is useful for analysis and algorithm development, and the local energy, which is easier to compute and was shown empirically in extensive tests on state-of-the-art networks to be the best predictor of generalization capabilities. We show semi-analytically in simple controlled scenarios that these two measures correlate strongly with each other and with generalization. Then, we extend the analysis to the deep learning scenario by extensive numerical validations. We study two algorithms, Entropy-SGD and Replicated-SGD, that explicitly include the local entropy in the optimization objective. We devise a training schedule by which we consistently find flatter minima (using both flatness measures), and improve the generalization error for common architectures (e.g. ResNet, EfficientNet). | ENTROPIC GRADIENT DESCENT ALGORITHMS AND WIDE FLAT MINIMA A PREPRINT |
d253237810 | Empirical studies of the loss landscape of deep networks have revealed that many local minima are connected through low-loss valleys. Yet, little is known about the theoretical origin of such valleys. We present a general framework for finding continuous symmetries in the parameter space, which carve out low-loss valleys. Our framework uses equivariances of the activation functions and can be applied to different layer architectures. To generalize this framework to nonlinear neural networks, we introduce a novel set of nonlinear, data-dependent symmetries. These symmetries can transform a trained model such that it performs similarly on new samples, which allows ensemble building that improves robustness under certain adversarial attacks. We then show that conserved quantities associated with linear symmetries can be used to define coordinates along low-loss valleys. The conserved quantities help reveal that using common initialization methods, gradient flow only explores a small part of the global minimum. By relating conserved quantities to convergence rate and sharpness of the minimum, we provide insights on how initialization impacts convergence and generalizability. * Equal contribution. † Work done during an internship at IBM. Loss surface simplexes for mode connecting volumes and fast ensembling. In International Conference on Machine Learning, pp. 769-779. PMLR, 2021. Alan J Bray and David S Dean. Statistics of critical points of gaussian fields on large-dimensional spaces. Physical review letters, 98(15):150201, 2007. | Published as a conference paper at ICLR 2023 SYMMETRIES, FLAT MINIMA, AND THE CONSERVED QUANTITIES OF GRADIENT FLOW |
d4842909 | Current end-to-end machine reading and question answering (Q&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score 1 on the test set, which is significantly better than the best published F1 score of 81.8. | Published as a conference paper at ICLR 2018 QANET: COMBINING LOCAL CONVOLUTION WITH GLOBAL SELF-ATTENTION FOR READING COMPRE- HENSION |
d227342850 | Establishing a theoretical analysis that explains why deep learning can outperform shallow learning such as kernel methods is one of the biggest issues in the deep learning literature. Towards answering this question, we evaluate excess risk of a deep learning estimator trained by a noisy gradient descent with ridge regularization on a mildly overparameterized neural network, and discuss its superiority to a class of linear estimators that includes neural tangent kernel approach, random feature model, other kernel methods, k-NN estimator and so on. We consider a teacher-student regression model, and eventually show that any linear estimator can be outperformed by deep learning in a sense of the minimax optimal rate especially for a high dimension setting. The obtained excess bounds are so-called fast learning rate which is faster than O(1/ √ n) that is obtained by usual Rademacher complexity analysis. This discrepancy is induced by the non-convex geometry of the model and the noisy gradient descent used for neural network training provably reaches a near global optimal solution even though the loss landscape is highly non-convex. Although the noisy gradient descent does not employ any explicit or implicit sparsity inducing regularization, it shows a preferable generalization performance that dominates linear estimators. Y. Cao and Q. Gu. A generalization theory of gradient descent for learning over-parameterized deep ReLU networks. arXiv preprint arXiv:1902.01384, 2019. A. Caponnetto and E. de Vito. Optimal rates for regularized least-squares algorithm. Foundations of Computational Mathematics, 7(3):331-368, 2007. | Benefit of deep learning with non-convex noisy gradient descent BENEFIT OF DEEP LEARNING WITH NON-CONVEX NOISY GRADIENT DESCENT: PROVABLE EXCESS RISK BOUND AND SUPERIORITY TO KERNEL METHODS |
d208267807 | Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question. This paper introduces a new graphbased recurrent retrieval approach that learns to retrieve reasoning paths over the Wikipedia graph to answer multi-hop open-domain questions. Our retriever model trains a recurrent neural network that learns to sequentially retrieve evidence paragraphs in the reasoning path by conditioning on the previously retrieved documents. Our reader model ranks the reasoning paths and extracts the answer span included in the best reasoning path. Experimental results show state-of-the-art results in three open-domain QA datasets, showcasing the effectiveness and robustness of our method. Notably, our method achieves significant improvement in HotpotQA, outperforming the previous best model by more than 14 points. 1 . Efficient and robust question answering from minimal context over documents. In ACL, 2018.Sewon Min, Danqi Chen, Hannaneh Hajishirzi, and Luke Zettlemoyer. A discrete hard em approach for weakly supervised question answering. In EMNLP, 2019a. -hop reading comprehension through question decomposition and rescoring. In ACL, 2019c. | Published as a conference paper at ICLR 2020 LEARNING TO RETRIEVE REASONING PATHS OVER WIKIPEDIA GRAPH FOR QUESTION ANSWERING |
d256503461 | Self-supervised learning aims to learn a embedding space where semantically similar samples are close. Contrastive learning methods pull views of samples together and push different samples away, which utilizes semantic invariance of augmentation but ignores the relationship between samples. To better exploit the power of augmentation, we observe that semantically similar samples are more likely to have similar augmented views. Therefore, we can take the augmented views as a special description of a sample. In this paper, we model such a description as the augmentation distribution and we call it augmentation feature. The similarity in augmentation feature reflects how much the views of two samples overlap and is related to their semantical similarity. Without computational burdens to explicitly estimate values of the augmentation feature, we propose Augmentation Component Analysis (ACA) with a contrastive-like loss to learn principal components and an on-the-fly projection loss to embed data. ACA equals an efficient dimension reduction by PCA and extracts low-dimensional embeddings, theoretically preserving the similarity of augmentation distribution between samples. Empirical results show our method can achieve competitive results against various traditional contrastive learning methods on different benchmarks. Code available at https://github.com/hanlu-nju/AugCA. | Published as a conference paper at ICLR 2023 AUGMENTATION COMPONENT ANALYSIS: MODELING SIMILARITY VIA THE AUGMENTATION OVERLAPS |
d53501574 | Image-to-image translation has recently received significant attention due to advances in deep learning. Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way. However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner-and cross-domain variations. To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain. We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain. Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain. To avoid semantic inconsistencies during translation that naturally appear due to the large inner-and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels. Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.1 | EXEMPLAR GUIDED UNSUPERVISED IMAGE-TO- IMAGE TRANSLATION WITH SEMANTIC CONSISTENCY |
d209439606 | We make the following striking observation: fully convolutional VAE models trained on 32×32 ImageNet can generalize well, not just to 64×64 but also to far larger photographs, with no changes to the model. We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless compression (Townsend et al., 2019) to large color photographs, and achieving state of the art for compression of full size ImageNet images. We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results 1 . * Equal contribution. 1 Available at https://github.com/hilloc-submission/hilloc. | HILLOC: LOSSLESS IMAGE COMPRESSION WITH HI- ERARCHICAL LATENT VARIABLE MODELS |
d3570621 | We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is "matrix factorization by design" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and states into the independent groups. Both approaches allow us to train large LSTM networks significantly faster to the state-of the art perplexity. On the One Billion Word Benchmark we improve single model perplexity down to 23.36. | Workshop track -ICLR 2017 FACTORIZATION TRICKS FOR LSTM NETWORKS |
d238531645 | Sparsely activated models (SAMs), such as Mixture-of-Experts (MoE), can easily scale to have outrageously large amounts of parameters without significant increase in computational cost. However, SAMs are reported to be parameter inefficient such that larger models do not always lead to better performance. While most on-going research focuses on improving SAMs models by exploring methods of routing inputs to experts, our analysis reveals that such research might not lead to the solution we expect, i.e., the commonly-used routing methods based on gating mechanisms do not work better than randomly routing inputs to experts. In this paper, we propose a new expert-based model, THOR (Transformer witH StOchastic ExpeRts). Unlike classic expert-based models, such as the Switch Transformer(Fedus et al., 2021), experts in THOR are randomly activated for each input during training and inference. THOR models are trained using a consistency regularized loss, where experts learn not only from training data but also from other experts as teachers, such that all the experts make consistent predictions. We validate the effectiveness of THOR on machine translation tasks. Results show that THOR models are more parameter efficient in that they significantly outperform the Transformer and MoE models across various settings. For example, in multilingual translation, THOR outperforms the Switch Transformer by 2 BLEU scores, and obtains the same BLEU score as that of a state-of-the-art MoE model(Kim et al., 2021)that is 18 times larger. Our code is publicly available at: https://github.com/microsoft/ Stochastic-Mixture-of-Experts. * Work was done during an internship at Microsoft. | Published as a conference paper at ICLR 2022 TAMING SPARSELY ACTIVATED TRANSFORMER WITH STOCHASTIC EXPERTS |
d232185279 | Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predictions between the groups. Nevertheless, even though the constraints are satisfied during training, they might not generalize at evaluation time. To improve the generalizability of fair classifiers, we propose fair mixup, a new data augmentation strategy for imposing the fairness constraint. In particular, we show that fairness can be achieved by regularizing the models on paths of interpolated samples between the groups. We use mixup, a powerful data augmentation strategy to generate these interpolates. We analyze fair mixup and empirically show that it ensures a better generalization for both accuracy and fairness measurement in tabular, vision, and language benchmarks. The code is available at https://github.com/chingyaoc/fair-mixup. * Work done during an internship at IBM Research AI | Published as a conference paper at ICLR 2021 FAIR MIXUP: FAIRNESS VIA INTERPOLATION |
d56517468 | Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structures into tensor product representations (TPRs;Smolensky, 1990), which additively combine tensor products of vectors representing roles (e.g., sequence positions) and vectors representing fillers (e.g., particular words). To test this hypothesis, we introduce Tensor Product Decomposition Networks (TPDNs), which use TPRs to approximate existing vector representations. We demonstrate using synthetic data that TPDNs can successfully approximate linear and tree-based RNN autoencoder representations, suggesting that these representations exhibit interpretable compositional structure; we explore the settings that lead RNNs to induce such structuresensitive representations. By contrast, further TPDN experiments show that the representations of four models trained to encode naturally-occurring sentences can be largely approximated with a bag-of-words, with only marginal improvements from more sophisticated structures. We conclude that TPDNs provide a powerful method for interpreting vector representations, and that standard RNNs can induce compositional sequence representations that are remarkably well approximated by TPRs; at the same time, existing training tasks for sentence representation learning may not be sufficient for inducing robust structural representations. | RNNS IMPLICITLY IMPLEMENT TENSOR PRODUCT REPRESENTATIONS |
d222134076 | Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between two views of an image. NCE uses randomly sampled negative examples to normalize the objective. In this paper, we show that choosing difficult negatives, or those more similar to the current instance, can yield stronger representations. To do this, we introduce a family of mutual information estimators that sample negatives conditionally -in a "ring" around each positive. We prove that these estimators lower-bound mutual information, with higher bias but lower variance than NCE. Experimentally, we find our approach, applied on top of existing models (IR, CMC, and MoCo) improves accuracy by 2-5% points in each case, measured by linear evaluation on four standard image datasets. Moreover, we find continued benefits when transferring features to a variety of new image distributions from the Meta-Dataset collection and to a variety of downstream tasks such as object detection, instance segmentation, and keypoint detection.Recent contrastive algorithms were developed as estimators of mutual information(Oord et al., 2018;Hjelm et al., 2018;Bachman et al., 2019), building on the intuition that a good lowdimensional "representation" would be one that linearizes the useful information embedded within a high-dimensional data point. In the visual domain, these estimators optimize an encoder by maximizing the similarity of encodings for two augmentations (i.e. transformations) of the same image. Doing so is trivial unless this similarity function is normalized. This is done by using "negative examples", contrasting an image (e.g. of a cat) with a set of possible other images (e.g. of dogs, tables, cars, etc.). We hypothesize that the manner in which we choose these negatives greatly effects the quality of the representations. For instance, differentiating a cat from other breeds of cats is visually more difficult than differentiating a cat from other classes. The encoder may thus have to focus on more granular, semantic information (e.g. fur patterns) that may be useful for downstream visual 1 | CONDITIONAL NEGATIVE SAMPLING FOR CON- TRASTIVE LEARNING OF VISUAL REPRESENTATIONS |
d222208634 | Flexible neural sequence models outperform grammar-and automaton-based counterparts on a variety of tasks. However, neural models perform poorly in settings requiring compositional generalization beyond the training data-particularly to rare or unseen subsequences. Past work has found symbolic scaffolding (e.g. grammars or automata) essential in these settings. We describe R&R, a learned data augmentation scheme that enables a large category of compositional generalizations without appeal to latent symbolic structure. R&R has two components: recombination of original training examples via a prototype-based generative model and resampling of generated examples to encourage extrapolation. Training an ordinary neural sequence model on a dataset augmented with recombined and resampled examples significantly improves generalization in two language processing problems-instruction following (SCAN) and morphological analysis (SIGMORPHON 2018)-where R&R enables learning of new constructions and tenses from as few as eight initial examples. . Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In | Published as a conference paper at ICLR 2021 LEARNING TO RECOMBINE AND RESAMPLE DATA FOR COMPOSITIONAL GENERALIZATION |
d246441923 | Differentiable physics modeling combines physics models with gradient-based learning to provide model explicability and data efficiency. It has been used to learn dynamics, solve inverse problems and facilitate design, and is at its inception of impact. Current successes have concentrated on general physics models such as rigid bodies, deformable sheets, etc, assuming relatively simple structures and forces. Their granularity is intrinsically coarse and therefore incapable of modelling complex physical phenomena. Fine-grained models are still to be developed to incorporate sophisticated material structures and force interactions with gradient-based learning. Following this motivation, we propose a new differentiable fabrics model for composite materials such as cloths, where we dive into the granularity of yarns and model individual yarn physics and yarn-to-yarn interactions. To this end, we propose several differentiable forces, whose counterparts in empirical physics are indifferentiable, to facilitate gradient-based learning. These forces, albeit applied to cloths, are ubiquitous in various physical systems. Through comprehensive evaluation and comparison, we demonstrate our model's explicability in learning meaningful physical parameters, versatility in incorporating complex physical structures and heterogeneous materials, data-efficiency in learning, and high-fidelity in capturing subtle dynamics. Code is available in: https://github.com/realcrane/Fine-grained-Differentiable-P hysics-A-Yarn-level-Model-for-Fabrics.git | FINE-GRAINED DIFFERENTIABLE PHYSICS: A YARN- LEVEL MODEL FOR FABRICS |
d209439872 | Recent breakthroughs of pretrained language models have shown the effectiveness of self-supervised learning for a wide range of natural language processing (NLP) tasks. In addition to standard syntactic and semantic NLP tasks, pretrained models achieve strong improvements on tasks that involve real-world knowledge, suggesting that large-scale language modeling could be an implicit method to capture knowledge. In this work, we further investigate the extent to which pretrained models such as BERT capture knowledge using a zero-shot fact completion task. Moreover, we propose a simple yet effective weakly supervised pretraining objective, which explicitly forces the model to incorporate knowledge about real-world entities. Models trained with our new objective yield significant improvements on the fact completion task. When applied to downstream tasks, our model consistently outperforms BERT on four entity-related question answering datasets (i.e., WebQuestions, TriviaQA, SearchQA and Quasar-T) with an average 2.7 F1 improvements and a standard fine-grained entity typing dataset (i.e., FIGER) with 5.7 accuracy gains. | Published as a conference paper at ICLR 2020 PRETRAINED ENCYCLOPEDIA: WEAKLY SUPERVISED KNOWLEDGE-PRETRAINED LANGUAGE MODEL |
d254535822 | Training large, deep neural networks to convergence can be prohibitively expensive. As a result, often only a small selection of popular, dense models are reused across different contexts and tasks. Increasingly, sparsely activated models, which seek to decouple model size from computation costs, are becoming an attractive alternative to dense models. Although more efficient in terms of quality and computation cost, sparse models remain data-hungry and costly to train from scratch in the large scale regime. In this work, we propose sparse upcycling -a simple way to reuse sunk training costs by initializing a sparsely activated Mixture-of-Experts model from a dense checkpoint. We show that sparsely upcycled T5 Base, Large, and XL language models and Vision Transformer Base and Large models, respectively, significantly outperform their dense counterparts on SuperGLUE and ImageNet, using only ∼ 50% of the initial dense pretraining sunk cost. The upcycled models also outperform sparse models trained from scratch on 100% of the initial dense pretraining computation budget. 1 . wav2vec 2.0: A framework for self-supervised learning of speech representations. Ray, et al. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155, 2022. , et al. Learning transferable visual models from natural language supervision. . Scaling vision with sparse mixture of experts. | Published as a conference paper at ICLR 2023 SPARSE UPCYCLING: TRAINING MIXTURE-OF-EXPERTS FROM DENSE CHECKPOINTS |
d15085443 | We evaluate natural gradient, an algorithm originally proposed in Amari (1997), for learning deep models. The contributions of this paper are as follows. We show the connection between natural gradient and three other recently proposed methods: Hessian-Free (Martens, 2010), Krylov Subspace Descent (Vinyals and Povey, 2012) and TONGA(Le Roux et al., 2008). We empirically evaluate the robustness of natural gradient to the ordering of the training set compared to stochastic gradient descent and show how unlabeled data can be used to improve generalization error. Another contribution is to extend natural gradient to incorporate second error information alongside the manifold information. Lastly we benchmark this new algorithm as well as natural gradient, where both are implemented using a truncated Newton approach for inverting the metric matrix instead of using a diagonal approximation of it. | Revisiting natural gradient for deep networks |
d222140788 | Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps in order to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a particular Markovian diffusion process. We generalize DDPMs via a class of non-Markovian diffusion processes that lead to the same training objective. These non-Markovian processes can correspond to generative processes that are deterministic, giving rise to implicit models that produce high quality samples much faster. We empirically demonstrate that DDIMs can produce high quality samples 10× to 50× faster in terms of wall-clock time compared to DDPMs, allow us to trade off computation for sample quality, perform semantically meaningful image interpolation directly in the latent space, and reconstruct observations with very low error. | Published as a conference paper at ICLR 2021 DENOISING DIFFUSION IMPLICIT MODELS |
d209832510 | Signatory is a library for calculating signature and logsignature transforms and related functionality. The focus is on making this functionality available for use in machine learning, and as such includes features such as GPU support and backpropagation. To our knowledge it is the first publically available GPU-capable library for these operations. It also implements several new algorithmic improvements, and provides several new features not available in previous libraries. The library operates as a Python wrapper around C++, and is compatible with the PyTorch ecosystem. It may be installed directly via pip. Source code, documentation, examples, benchmarks and tests may be found at https://github.com/patrick-kidger/signatory. The license is Apache-2.0. | SIGNATORY: DIFFERENTIABLE COMPUTATIONS OF THE SIGNATURE AND LOGSIGNATURE TRANSFORMS, ON BOTH CPU AND GPU * |
d232146803 | Dropout has been demonstrated as a simple and effective module to not only regularize the training process of deep neural networks, but also provide the uncertainty estimation for prediction. However, the quality of uncertainty estimation is highly dependent on the dropout probabilities. Most current models use the same dropout distributions across all data samples due to its simplicity. Despite the potential gains in the flexibility of modeling uncertainty, sample-dependent dropout, on the other hand, is less explored as it often encounters scalability issues or involves non-trivial model changes. In this paper, we propose contextual dropout with an efficient structural design as a simple and scalable sample-dependent dropout module, which can be applied to a wide range of models at the expense of only slightly increased memory and computational cost. We learn the dropout probabilities with a variational objective, compatible with both Bernoulli dropout and Gaussian dropout. We apply the contextual dropout module to various models with applications to image classification and visual question answering and demonstrate the scalability of the method with large-scale datasets, such as ImageNet and VQA 2.0. Our experimental results show that the proposed method outperforms baseline methods in terms of both accuracy and quality of uncertainty estimation. * Equal contribution. Corresponding | |
d245131210 | We are interested in interactive agents that learn to coordinate, namely, a builder -which performs actions but ignores the goal of the task, i.e. has no access to rewards -and an architect which guides the builder towards the goal of the task. We define and explore a formal setting where artificial agents are equipped with mechanisms that allow them to simultaneously learn a task while at the same time evolving a shared communication protocol. Ideally, such learning should only rely on high-level communication priors and be able to handle a large variety of tasks and meanings while deriving communication protocols that can be reused across tasks. The field of Experimental Semiotics has shown the extent of human proficiency at learning from a priori unknown instructions meanings. Therefore, we take inspiration from it and present the Architect-Builder Problem (ABP): an asymmetrical setting in which an architect must learn to guide a builder towards constructing a specific structure. The architect knows the target structure but cannot act in the environment and can only send arbitrary messages to the builder. The builder on the other hand can act in the environment, but receives no rewards nor has any knowledge about the task, and must learn to solve it relying only on the messages sent by the architect. Crucially, the meaning of messages is initially not defined nor shared between the agents but must be negotiated throughout learning. Under these constraints, we propose Architect-Builder Iterated Guiding (ABIG), a solution to the Architect-Builder Problem where the architect leverages a learned model of the builder to guide it while the builder uses self-imitation learning to reinforce its guided behavior. To palliate to the non-stationarity induced by the two agents concurrently learning, ABIG structures the sequence of interactions between the agents into interaction frames. We analyze the key learning mechanisms of ABIG and test it in a 2-dimensional instantiation of the ABP where tasks involve grasping cubes, placing them at a given location, or building various shapes. In this environment, ABIG results in a low-level, high-frequency, guiding communication protocol that not only enables an architect-builder pair to solve the task at hand, but that can also generalize to unseen tasks. * Equal contribution. † Work conducted while at Inria. ‡ Canada CIFAR AI Chair. | Published as a conference paper at ICLR 2022 LEARNING TO GUIDE AND TO BE GUIDED IN THE ARCHITECT-BUILDER PROBLEM |
d59222819 | We investigate how reinforcement learning agents can learn to cooperate. Drawing inspiration from human societies, in which successful coordination of many individuals is often facilitated by hierarchical organisation, we introduce Feudal Multiagent Hierarchies (FMH). In this framework, a 'manager' agent, which is tasked with maximising the environmentally-determined reward function, learns to communicate subgoals to multiple, simultaneously-operating, 'worker' agents. Workers, which are rewarded for achieving managerial subgoals, take concurrent actions in the world. We outline the structure of FMH and demonstrate its potential for decentralised learning and control. We find that, given an adequate set of subgoals from which to choose, FMH performs, and particularly scales, substantially better than cooperative approaches that use a shared reward function. | Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning |
d9556453 | Code super-optimization is the task of transforming any given program to a more efficient version while preserving its input-output behaviour. In some sense, it is similar to the paraphrase problem from natural language processing where the intention is to change the syntax of an utterance without changing its semantics. Code-optimization has been the subject of years of research that has resulted in the development of rule-based transformation strategies that are used by compilers. More recently, however, a class of stochastic search based methods have been shown to outperform these strategies. This approach involves repeated sampling of modifications to the program from a proposal distribution, which are accepted or rejected based on whether they preserve correctness and the improvement they achieve. These methods, however, neither learn from past behaviour nor do they try to leverage the semantics of the program under consideration. Motivated by this observation, we present a novel learning based approach for code super-optimization. Intuitively, our method works by learning the proposal distribution using unbiased estimators of the gradient of the expected improvement. Experiments on benchmarks comprising of automatically generated as well as existing ("Hacker's Delight") programs show that the proposed method is able to significantly outperform state of the art approaches for code super-optimization. | Published as a conference paper at ICLR 2017 LEARNING TO SUPEROPTIMIZE PROGRAMS |
d233423379 | Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action using a multivariate Gaussian with a diagonal covariance structure. This modeling choice assumes that different dimensions of the next state and reward are conditionally independent given the current state and action and may be driven by the fact that fully observable physics-based simulation environments entail deterministic transition dynamics. In this paper, we challenge this conditional independence assumption and propose a family of expressive autoregressive dynamics models that generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We demonstrate that autoregressive dynamics models indeed outperform standard feedforward models in log-likelihood on heldout transitions. Furthermore, we compare different model-based and model-free off-policy evaluation (OPE) methods on RL Unplugged, a suite of offline MuJoCo datasets, and find that autoregressive dynamics models consistently outperform all baselines, achieving a new state-of-the-art. Finally, we show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer through data augmentation and improving performance using model-based planning. * Work done as an intern at Google Brain.arXiv:2104.13877v1 [cs.LG] 28 Apr 2021Published as a conference paper at ICLR 2021 Autoregressive generative models have seen success in generating natural images (Parmar et al., 2018), text (Brown et al., 2020, and speech (Oord et al., 2016), but they have not seen use in Model-based RL for continuous control.We find that autoregressive dynamics models achieve higher log-likelihood compared to their feedforward counterparts on heldout validation transitions of all DM continuous control tasks (Tassa et al., 2018) from the RL Unplugged dataset (Gulcehre et al., 2020). To determine the impact of improved transition dynamics models, we primarily focus on OPE because it allows us to isolate contributions of the dynamics model in value estimation vs. the many other factors of variation in policy optimization and data collection. We find that autoregressive dynamics models consistently outperform existing Model-based and Model-free OPE baselines on continuous control in both ranking and value estimation metrics. We expect that our advances in model-based OPE will improve offline policy selection for offline RL (Paine et al., 2020). Finally, we show that our autoregressive dynamics models can help improve offline policy optimization by model predictive control, achieving a new state-of-the-art on cheetah-run and fish-swim from RL Unplugged(Gulcehre et al., 2020).Key contributions of this paper include:• We propose autoregressive dynamics models to capture dependencies between state dimensions in forward prediction. We show that autoregressive models improve log-likelihood over nonautoregressive models for continuous control tasks from the DM Control Suite (Tassa et al., 2018). • We apply autoregressive dynamics models to Off-Policy Evaluation (OPE), surpassing the performance of state-of-the art baselines in median absolute error, rank correlation, and normalized top-5 regret across 9 control tasks. • We show that autoregressive dynamics models are more useful than feedforward models for offline policy optimization, serving as a way to enrich experience replay by data augmentation and improving performance via model-based planning. | Published as a conference paper at ICLR 2021 AUTOREGRESSIVE DYNAMICS MODELS FOR OFFLINE POLICY EVALUATION AND OPTIMIZATION |
d5069968 | Deep neural networks (DNN) have shown unprecedented success in various computer vision applications such as image classification and object detection. However, it is still a common (yet inconvenient) practice to prepare at least tens of thousands of labeled image to finetune a network on every task before the model is ready to use. Recent study[1]shows that a DNN has strong dependency towards the training dataset, and the learned features cannot be easily transferred to a different but relevant task without fine-tuning. In this paper, we propose a simple yet powerful remedy, called Adaptive Batch Normalization(AdaBN), to increase the generalization ability of a DNN. Our approach is based on the well-known Batch Normalization technique [2] which has become a standard component in modern deep learning. In contrary to other deep learning domain adaptation methods, our method does not require additional components, and is parameterfree. It archives state-of-the-art performance despite its surprising simplicity. Furthermore, we demonstrate that our method is complementary with other existing methods. Combining AdaBN with existing domain adaptation treatments may further improve model performance. | Revisiting Batch Normalization For Practical Domain Adaptation |
d257079046 | A fundamental challenge for multi-task learning is that different tasks may conflict with each other when they are solved jointly, and a cause of this phenomenon is conflicting gradients during optimization. Recent works attempt to mitigate the influence of conflicting gradients by directly altering the gradients based on some criteria. However, our empirical study shows that "gradient surgery" cannot effectively reduce the occurrence of conflicting gradients. In this paper, we take a different approach to reduce conflicting gradients from the root. In essence, we investigate the task gradients w.r.t. each shared network layer, select the layers with high conflict scores, and turn them to task-specific layers. Our experiments show that such a simple approach can greatly reduce the occurrence of conflicting gradients in the remaining shared layers and achieve better performance, with only a slight increase in model parameters in many cases. Our approach can be easily applied to improve various state-of-the-art methods including gradient manipulation methods and branched architecture search methods. Given a network architecture (e.g., ResNet18), it only needs to search for the conflict layers once, and the network can be modified to be used with different methods on the same or even different datasets to gain performance improvement. The source code is available at https://github.com/moukamisama/Recon.We propose a different approach to reduce conflicting gradients for MTL. Specifically, we investigate layer-wise conflicting gradients, i.e., the task gradients w.r.t. each shared network layer. We first train the network with a regular MTL algorithm (e.g., joint-training) for a number of iterations, compute the conflict scores for all shared layers, and select those with highest conflict scores (indicating severe conflicts). We then set the selected shared layers task-specific and train the modified network from scratch. As demonstrated by comprehensive experiments and analysis, our simple approach Recon has the following key advantages: (1) Recon can greatly reduce conflicting gradients with only a slight increase in model parameters (less than 1% in some cases) and lead to significantly better performance.(2) Recon can be easily applied to improve various gradient manipulation methods and branched architecture search methods. Given a network architecture, it only needs to search for the conflict layers once, and the network can be modified to be used with different methods and even on different datasets to gain performance improvement.(3) Recon can achieve better performance than branched architecture search methods with a much smaller model. | Published as a conference paper at ICLR 2023 RECON: REDUCING CONFLICTING GRADIENTS FROM THE ROOT FOR MULTI-TASK LEARNING |
d226226888 | Large-scale language models have recently demonstrated impressive empirical performance. Nevertheless, the improved results are attained at the price of bigger models, more power consumption, and slower inference, which hinder their applicability to low-resource (memory and computation) platforms. Knowledge distillation (KD) has been demonstrated as an effective framework for compressing such big models. However, large-scale neural network systems are prone to memorize training instances, and thus tend to make inconsistent predictions when the data distribution is altered slightly. Moreover, the student model has few opportunities to request useful information from the teacher model when there is limited task-specific data available. To address these issues, we propose MixKD, a data-agnostic distillation framework that leverages mixup, a simple yet efficient data augmentation approach, to endow the resulting model with stronger generalization ability. Concretely, in addition to the original training examples, the student model is encouraged to mimic the teacher's behavior on the linear interpolation of example pairs as well. We prove, from a theoretical perspective, that under reasonable conditions MixKD gives rise to a smaller gap between the generalization error and the empirical error. To verify its effectiveness, we conduct experiments on the GLUE benchmark, where MixKD consistently leads to significant gains over the standard KD training, and outperforms several competitive baselines. Experiments under a limited-data setting and ablation studies further demonstrate the advantages of the proposed approach. * Equal contribution 1 arXiv:2011.00593v1 [cs.CL] 1 Nov 2020 | Pre-print MIXKD: TOWARDS EFFICIENT DISTILLATION OF LARGE-SCALE LANGUAGE MODELS |
d247025836 | Audio-visual navigation task requires an agent to find a sound source in a realistic, unmapped 3D environment by utilizing egocentric audio-visual observations. Existing audio-visual navigation works assume a clean environment that solely contains the target sound, which, however, would not be suitable in most realworld applications due to the unexpected sound noise or intentional interference. In this work, we design an acoustically complex environment in which, besides the target sound, there exists a sound attacker playing a zero-sum game with the agent. More specifically, the attacker can move and change the volume and category of the sound to make the agent suffer from finding the sounding object while the agent tries to dodge the attack and navigate to the goal under the intervention. Under certain constraints to the attacker, we can improve the robustness of the agent towards unexpected sound attacks in audio-visual navigation. For better convergence, we develop a joint training mechanism by employing the property of a centralized critic with decentralized actors. Experiments on two real-world 3D scan datasets, Replica, and Matterport3D, verify the effectiveness and the robustness of the agent trained under our designed environment when transferred to the clean environment or the one containing sound attackers with random policy. Project: https://yyf17.github.io/SAAVN. | Published as a conference paper at ICLR 2022 SOUND ADVERSARIAL AUDIO-VISUAL NAVIGATION |
d246430782 | Implicit neural representations with multi-layer perceptrons (MLPs) have recently gained prominence for a wide variety of tasks such as novel view synthesis and 3D object representation and rendering. However, a significant challenge with these representations is that both training and inference with an MLP over a large number of input coordinates to learn and represent an image, video, or 3D object, require large amounts of computation and incur long processing times. In this work, we aim to accelerate inference and training of coordinate-based MLPs for implicit neural representations by proposing a new split MLP architecture, Co-ordX. With CoordX, the initial layers are split to learn each dimension of the input coordinates separately. The intermediate features are then fused by the last layers to generate the learned signal at the corresponding coordinate point. This significantly reduces the amount of computation required and leads to large speedups in training and inference, while achieving similar accuracy as the baseline MLP. This approach thus aims at first learning functions that are a decomposition of the original signal and then fusing them to generate the learned signal. Our proposed architecture can be generally used for many implicit neural representation tasks with no additional memory overheads. We demonstrate a speedup of up to 2.92x compared to the baseline model for image, video, and 3D shape representation and rendering tasks. | COORDX: ACCELERATING IMPLICIT NEURAL REPRE- SENTATION WITH A SPLIT MLP ARCHITECTURE |
d246240044 | In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), making their evaluation challenging. Currently, the standard process for evaluating GGMs suffers from three critical limitations: i) it does not produce a single score which makes model selection challenging, ii) in many cases it fails to consider underlying edge and node features, and iii) it is prohibitively slow to perform. In this work, we mitigate these issues by searching for scalar, domainagnostic, and scalable metrics for evaluating and ranking GGMs. To this end, we study existing GGM metrics and neural-network-based metrics emerging from generative models of images that use embeddings extracted from a task-specific network. Motivated by the power of Graph Neural Networks (GNNs) to extract meaningful graph representations without any training, we introduce several metrics based on the features extracted by an untrained random GNN. We design experiments to thoroughly test and objectively score metrics on their ability to measure the diversity and fidelity of generated graphs, as well as their sample and computational efficiency. Depending on the quantity of samples, we recommend one of two metrics from our collection of random-GNN-based metrics. We show these two metrics to be more expressive than pre-existing and alternative random-GNNbased metrics using our objective scoring. While we focus on applying these metrics to GGM evaluation, in practice this enables the ability to easily compute the dissimilarity between any two sets of graphs regardless of domain. Our code is released at: | Published as a conference paper at ICLR 2022 ON EVALUATION METRICS FOR GRAPH GENERATIVE MODELS |
d250279999 | Hamiltonian Monte Carlo (HMC) is a popular method in sampling. While there are quite a few works of studying this method on various aspects, an interesting question is how to choose its integration time to achieve acceleration. In this work, we consider accelerating the process of sampling from a distribution π(x) ∝ exp(−f (x)) via HMC via time-varying integration time. When the potential f is L-smooth and m-strongly convex, i.e. for sampling from a log-smooth and strongly log-concave target distribution π, it is known that under a constant integration time, the number of iterations that ideal HMC takes to get an Wasserstein-2 distance to the target π is O(κ log 1 ), where κ := L m is the condition number. We propose a scheme of time-varying integration time based on the roots of Chebyshev polynomials. We show that in the case of quadratic potential f , i.e. when the target π is a Gaussian distribution, ideal HMC with this choice of integration time only takes O( √ κ log 1 ) number of iterations to reach Wasserstein-2 distance less than ; this improvement on the dependence on condition number is akin to acceleration in optimization. The design and analysis of HMC with the proposed integration time is built on the tools of Chebyshev polynomials. Experiments find the advantage of adopting our scheme of time-varying integration time even for sampling from distributions with smooth strongly convex potentials that are not quadratic. | Published as a conference paper at ICLR 2023 ACCELERATING HAMILTONIAN MONTE CARLO VIA CHEBYSHEV INTEGRATION TIME |
d239616317 | Pruning neural networks reduces inference time and memory costs.On standard hardware, these benefits will be especially prominent if coarse-grained structures, like feature maps, are pruned.We devise two novel saliency-based methods for second-order structured pruning (SOSP) which include correlations among all structures and layers.Our main method SOSP-H employs an innovative second-order approximation, which enables saliency evaluations by fast Hessian-vector products.SOSP-H thereby scales like a first-order method despite taking into account the full Hessian.We validate SOSP-H by comparing it to our second method SOSP-I that uses a well-established Hessian approximation, and to numerous state-of-the-art methods.While SOSP-H performs on par or better in terms of accuracy, it has clear advantages in terms of scalability and efficiency.This allowed us to scale SOSP-H to large-scale vision tasks, even though it captures correlations across all layers of the network.To underscore the global nature of our pruning methods, we evaluate their performance not only by removing structures from a pretrained network, but also by detecting architectural bottlenecks.We show that our algorithms allow to systematically reveal architectural bottlenecks, which we then widen to further increase the accuracy of the networks. | SOSP: EFFICIENTLY CAPTURING GLOBAL CORRELA-TIONS BY SECOND-ORDER STRUCTURED PRUNING |
d225066968 | We present a new methodology for detecting out-of-distribution (OOD) images by utilizing norms of the score estimates at multiple noise scales. A score is defined to be the gradient of the log density with respect to the input data. Our methodology is completely unsupervised and follows a straight forward training scheme. First, we train a deep network to estimate scores for L levels of noise. Once trained, we calculate the noisy score estimates for N in-distribution samples and take the L2norms across the input dimensions (resulting in an N xL matrix). Then we train an auxiliary model (such as a Gaussian Mixture Model) to learn the in-distribution spatial regions in this L-dimensional space. This auxiliary model can now be used to identify points that reside outside the learned space. Despite its simplicity, our experiments show that this methodology significantly outperforms the stateof-the-art in detecting out-of-distribution images. For example, our method can effectively separate CIFAR-10 (inlier) and SVHN (OOD) images, a setting which has been previously shown to be difficult for deep likelihood models. | MULTISCALE SCORE MATCHING FOR OUT-OF- DISTRIBUTION DETECTION |
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