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d222133863 | Graph-structured data are ubiquitous. However, graphs encode diverse types of information and thus play different roles in data representation. In this paper, we distinguish the representational and the correlational roles played by the graphs in node-level prediction tasks, and we investigate how Graph Neural Network (GNN) models can effectively leverage both types of information. Conceptually, the representational information provides guidance for the model to construct better node features; while the correlational information indicates the correlation between node outcomes conditional on node features. Through a simulation study, we find that many popular GNN models are incapable of effectively utilizing the correlational information. By leveraging the idea of the copula, a principled way to describe the dependence among multivariate random variables, we offer a general solution. The proposed Copula Graph Neural Network (CopulaGNN) can take a wide range of GNN models as base models and utilize both representational and correlational information stored in the graphs. Experimental results on two types of regression tasks verify the effectiveness of the proposed method. * | COPULAGNN: TOWARDS INTEGRATING REPRESEN- TATIONAL AND CORRELATIONAL ROLES OF GRAPHS IN GRAPH NEURAL NETWORKS |
d3281198 | In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL). By introducing the concept of a semantic descriptor, this framework unifies MDL and MTL as well as encompassing various classic and recent MTL/MDL algorithms by interpreting them as different ways of constructing semantic descriptors. Our interpretation provides an alternative pipeline for zero-shot learning (ZSL), where a model for a novel class can be constructed without training data. Moreover, it leads to a new and practically relevant problem setting of zero-shot domain adaptation (ZSDA), which is the analogous to ZSL but for novel domains: A model for an unseen domain can be generated by its semantic descriptor. Experiments across this range of problems demonstrate that our framework outperforms a variety of alternatives. | A UNIFIED PERSPECTIVE ON MULTI-DOMAIN AND MULTI-TASK LEARNING |
d17714865 | There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger.We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture. On one version of the dataset our ensemble even exceeds the human baseline provided by Facebook. We then show in our own human study that there is still space for further improvement. | Embracing data abundance: BookTest Dataset for Reading Comprehension |
d13619197 | In this paper we propose equipping Generative Adversarial Networks with the ability to produce direct energy estimates for samples. Specifically, we develop a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the discriminator to retain the density information at the global optimum. We derive the analytic form of the induced solution, and analyze its properties. In order to make the proposed framework trainable in practice, we introduce two effective approximation techniques. Empirically, the experiment results closely match our theoretical analysis, verifying that the discriminator is able to recover the energy of data distribution. | CALIBRATING ENERGY-BASED GENERATIVE ADVER- SARIAL NETWORKS |
d17684363 | We investigate the use of deep neural networks for the novel task of class-generic object detection. We show that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided. In addition, we show that bounding box labels yield a 1% performance increase on the ImageNet recognition challenge. | Deep learning for class-generic object detection |
d211204780 | Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience can be acquired. This property makes these algorithms appealing for real world problems such as robot control. In practice, however, standard off-policy algorithms fail in the batch setting for continuous control. In this paper, we propose a simple solution to this problem. It admits the use of data generated by arbitrary behavior policies and uses a learned prior -the advantage-weighted behavior model (ABM) -to bias the RL policy towards actions that have previously been executed and are likely to be successful on the new task. Our method can be seen as an extension of recent work on batch-RL that enables stable learning from conflicting data-sources. We find improvements on competitive baselines in a variety of RL tasks -including standard continuous control benchmarks and multitask learning for simulated and real-world robots. Videos are available at https: / | Published as a conference paper at ICLR 2020 KEEP DOING WHAT WORKED: BEHAVIOR MODELLING PRIORS FOR OFFLINE REIN- FORCEMENT LEARNING |
d257365515 | Invariant risk minimization (IRM) has received increasing attention as a way to acquire environment-agnostic data representations and predictions, and as a principled solution for preventing spurious correlations from being learned and for improving models' out-of-distribution generalization. Yet, recent works have found that the optimality of the originally-proposed IRM optimization (IRMV1) may be compromised in practice or could be impossible to achieve in some scenarios. Therefore, a series of advanced IRM algorithms have been developed that show practical improvement over IRMV1. In this work, we revisit these recent IRM advancements, and identify and resolve three practical limitations in IRM training and evaluation. First, we find that the effect of batch size during training has been chronically overlooked in previous studies, leaving room for further improvement. We propose small-batch training and highlight the improvements over a set of large-batch optimization techniques. Second, we find that improper selection of evaluation environments could give a false sense of invariance for IRM. To alleviate this effect, we leverage diversified test-time environments to precisely characterize the invariance of IRM when applied in practice. Third, we revisit Ahuja et al. (2020)'s proposal to convert IRM into an ensemble game and identify a limitation when a single invariant predictor is desired instead of an ensemble of individual predictors. We propose a new IRM variant to address this limitation based on a novel viewpoint of ensemble IRM games as consensus-constrained bilevel optimization. Lastly, we conduct extensive experiments (covering 7 existing IRM variants and 7 datasets) to justify the practical significance of revisiting IRM training and evaluation in a principled manner.Published as a conference paper at ICLR 2023 2021) revealed the theoretical failure of IRM in some cases. In particular, there exist scenarios where the optimal invariant predictor is impossible to achieve, and the IRM performance may fall behind even that of ERM. Practical studies also demonstrate that the performance of IRM rely on multiple factors, e.g., model size(Lin et al., 2022;Zhou et al., 2022b), environment difficulty(Dranker et al., 2021;Krueger et al., 2021), and dataset type (Gulrajani & Lopez-Paz, 2020).Therefore, key challenges remain in deploying IRM to real-world applications. In this work, we revisit recent IRM advancements and uncover and tackle several pitfalls in IRM training and evaluation, which have so far gone overlooked. We first identify the large-batch training issue in existing IRM algorithms, which prevents escape from bad local optima during IRM training. Next, we show that evaluation of IRM performance with a single test-time environment could lead to an inaccurate assessment of prediction invariance, even if this test environment differs significantly from training environments. Based on the above findings, we further develop a novel IRM variant, termed BLOC-IRM, by interpreting and advancing the IRM-GAME method (Ahuja et al., 2020) through the lens of BLO with Consensus prediction. Below, we list our contributions (-). | Published as a conference paper at ICLR 2023 WHAT IS MISSING IN IRM TRAINING AND EVALUA- TION? CHALLENGES AND SOLUTIONS |
d258461067 | Lottery Ticket Hypothesis (LTH) claims the existence of a winning ticket (i.e., a properly pruned sub-network together with original weight initialization) that can achieve competitive performance to the original dense network. A recent work, called UGS, extended LTH to prune graph neural networks (GNNs) for effectively accelerating GNN inference. UGS simultaneously prunes the graph adjacency matrix and the model weights using the same masking mechanism, but since the roles of the graph adjacency matrix and the weight matrices are very different, we find that their sparsifications lead to different performance characteristics. Specifically, we find that the performance of a sparsified GNN degrades significantly when the graph sparsity goes beyond a certain extent. Therefore, we propose two techniques to improve GNN performance when the graph sparsity is high. First, UGS prunes the adjacency matrix using a loss formulation which, however, does not properly involve all elements of the adjacency matrix; in contrast, we add a new auxiliary loss head to better guide the edge pruning by involving the entire adjacency matrix. Second, by regarding unfavorable graph sparsification as adversarial data perturbations, we formulate the pruning process as a min-max optimization problem to gain the robustness of lottery tickets when the graph sparsity is high. We further investigate the question: Can the "retrainable" winning ticket of a GNN be also effective for graph transferring learning? We call it the transferable graph lottery ticket (GLT) hypothesis. Extensive experiments were conducted which demonstrate the superiority of our proposed sparsification method over UGS, and which empirically verified our transferable GLT hypothesis. | Published as a conference paper at ICLR 2023 RETHINKING GRAPH LOTTERY TICKETS: GRAPH SPARSITY MATTERS |
d3655946 | Neural text generation models are often autoregressive language models or seq2seq models. These models generate text by sampling words sequentially, with each word conditioned on the previous word, and are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of the quality of the generated text. Additionally, these models are typically trained via maximum likelihood and teacher forcing. These methods are well-suited to optimizing perplexity but can result in poor sample quality since generating text requires conditioning on sequences of words that may have never been observed at training time. We propose to improve sample quality using Generative Adversarial Networks (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally designed to output differentiable values, so discrete language generation is challenging for them. We claim that validation perplexity alone is not indicative of the quality of text generated by a model. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic conditional and unconditional text samples compared to a maximum likelihood trained model. | MASKGAN: BETTER TEXT GENERATION VIA FILLING IN THE |
d231662383 | We study the robustness of reinforcement learning (RL) with adversarially perturbed state observations, which aligns with the setting of many adversarial attacks to deep reinforcement learning (DRL) and is also important for rolling out real-world RL agent under unpredictable sensing noise. With a fixed agent policy, we demonstrate that an optimal adversary to perturb state observations can be found, which is guaranteed to obtain the worst case agent reward. For DRL settings, this leads to a novel empirical adversarial attack to RL agents via a learned adversary that is much stronger than previous ones. To enhance the robustness of an agent, we propose a framework of alternating training with learned adversaries (ATLA), which trains an adversary online together with the agent using policy gradient following the optimal adversarial attack framework. Additionally, inspired by the analysis of state-adversarial Markov decision process (SA-MDP), we show that past states and actions (history) can be useful for learning a robust agent, and we empirically find a LSTM based policy can be more robust under adversaries. Empirical evaluations on a few continuous control environments show that ATLA achieves state-of-the-art performance under strong adversaries. Our code is available at https://github.com/huanzhang12/ATLA_robust_RL.arXiv:2101.08452v1 [cs.LG] 21 Jan 2021Published as a conference paper at ICLR 2021 (a) Path in unperturbed environment (found by policy iteration). Agent's reward = +1. Black arrows and numbers show actions and value function of the agent.(b) Path under the optimal adversary. Agent's reward = −∞. Red arrows and numbers show actions and value function of the optimal adversary (Section 3.1).(c) A robust POMDP policy solved by SARSOP(Kurniawati et al., 2008)under the same adversary. This policy is history dependent (Section 3.2). | Published as a conference paper at ICLR 2021 ROBUST REINFORCEMENT LEARNING ON STATE OB- SERVATIONS WITH LEARNED OPTIMAL ADVERSARY |
d251929231 | Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance, ground states preparation (GSP) is classically intractable for most large-scale Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern quantum machines, have emerged as a leading protocol to conquer this issue. As such, the performance enhancement of QNNs becomes the core in GSP. Empirical evidence showed that QNNs with handcraft symmetric ansätze generally experience better trainability than those with asymmetric ansätze, while theoretical explanations remain vague. To fill this knowledge gap, here we propose the effective quantum neural tangent kernel (EQNTK) and connect this concept with over-parameterization theory to quantify the convergence of QNNs towards the global optima. We uncover that the advance of symmetric ansätze attributes to their large EQNTK value with low effective dimension, which requests few parameters and quantum circuit depth to reach the over-parameterization regime permitting a benign loss landscape and fast convergence. Guided by EQNTK, we further devise a symmetric pruning (SP) scheme to automatically tailor a symmetric ansatz from an over-parameterized and asymmetric one to greatly improve the performance of QNNs when the explicit symmetry information of Hamiltonian is unavailable. Extensive numerical simulations are conducted to validate the analytical results of EQNTK and the effectiveness of SP.The law of quantum mechanics advocates that any quantum system can be described by a Hamiltonian, and many important physical properties are reflected by its ground state. For this reason, the ground state preparation (GSP) of Hamiltonians is the key to understanding and fabricating novel quantum matters. Due to the intrinsic hardness of GSP (Poulin & Wocjan, 2009; Carleo et al., 2019), the required computational resources of classical methods are unaffordable when the size of Hamiltonian becomes large. Quantum computers, whose operations can harness the strength of quantum mechanics, promise to tackle this problem with potential computational merits. In the noisy intermediate-scale quantum (NISQ) era (Preskill, 2018), quantum neural networks (QNNs) (Farhi & Neven, 2018; Cong et al., 2019; Cerezo et al., 2021a) are leading candidates toward this goal. The building blocks of QNNs, analogous to deep neural networks, consist of variational ansätze (also called parameterized quantum circuits) and classical optimizers. In order to enhance the power of QNNs in GSP, great efforts have been made to design advanced ansätze with varied circuit structures (Peruzzo et al., 2014; Wecker et al., 2015; Kandala et al., 2017).Despite the achievements aforementioned, recent progress has shown that QNNs may suffer from severe trainability issues when the circuit depth of ansätze is either shallow or deep. | Symmetric Pruning in Quantum Neural Net- works |
d209483537 | We propose precision gating (PG), an end-to-end trainable dynamic dual-precision quantization technique for deep neural networks. PG computes most features in a low precision and only a small proportion of important features in a higher precision to preserve accuracy. The proposed approach is applicable to a variety of DNN architectures and significantly reduces the computational cost of DNN execution with almost no accuracy loss. Our experiments indicate that PG achieves excellent results on CNNs, including statically compressed mobile-friendly networks such as ShuffleNet. Compared to the state-of-the-art prediction-based quantization schemes, PG achieves the same or higher accuracy with 2.4× less compute on ImageNet. PG furthermore applies to RNNs. Compared to 8-bit uniform quantization, PG obtains a 1.2% improvement in perplexity per word with 2.7× computational cost reduction on LSTM on the Penn Tree Bank dataset. | Published as a conference paper at ICLR 2020 PRECISION GATING: IMPROVING NEURAL NETWORK EFFICIENCY WITH DYNAMIC DUAL-PRECISION ACTI- VATIONS |
d166228264 | Adaptive gradient approaches that automatically adjust the learning rate on a perfeature basis have been very popular for training deep networks. This rich class of algorithms includes ADAGRAD, RMSPROP, ADAM, and recent extensions. All these algorithms have adopted diagonal matrix adaptation, due to the prohibitive computational burden of manipulating full matrices in high-dimensions. In this paper, we show that block-diagonal matrix adaptation can be a practical and powerful solution that can effectively utilize structural characteristics of deep learning architectures, and significantly improve convergence and out-of-sample generalization. We present a general framework with block-diagonal matrix updates via coordinate grouping, which includes counterparts of the aforementioned algorithms, prove their convergence in non-convex optimization, highlighting benefits compared to diagonal versions. In addition, we propose an efficient spectrum-clipping scheme that benefits from superior generalization performance of SGD. Extensive experiments reveal that block-diagonal approaches achieve state-of-the-art results on several deep learning tasks, and can outperform adaptive diagonal methods, vanilla SGD, as well as a modified version of full-matrix adaptation proposed very recently.Preprint. Under review. | Stochastic Gradient Methods with Block Diagonal Matrix Adaptation |
d210846466 | This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes. Secondly, we employ synchronous message passing networks to iteratively re-rank the soft correspondences to reach a matching consensus in local neighborhoods between graphs. We show, theoretically and empirically, that our message passing scheme computes a well-founded measure of consensus for corresponding neighborhoods, which is then used to guide the iterative re-ranking process. Our purely local and sparsity-aware architecture scales well to large, real-world inputs while still being able to recover global correspondences consistently. We demonstrate the practical effectiveness of our method on real-world tasks from the fields of computer vision and entity alignment between knowledge graphs, on which we improve upon the current state-of-theart. Our source code is available under https://github.com/rusty1s/ deep-graph-matching-consensus.Here, we propose a fully-differentiable graph matching procedure which aims to reach a data-driven neighborhood consensus between matched node pairs without the need to solve any optimization problem during inference. In addition, our approach is purely local, i.e., it operates on fixed-size neighborhoods around nodes, and is sparsity-aware, i.e., it takes the sparsity of the underlying structures into account. Hence, our approach scales well to large input domains, and can be trained in an end-to-end fashion to adapt to a given data distribution. Finally, our approach improves upon the state-of-the-art on several real-world applications from the fields of computer vision and entity alignment on knowledge graphs.PROBLEM DEFINITIONA graph G = (V, A, X, E) consists of a finite set of nodes V = {1, 2, . . .}, an adjacency matrix A ∈ {0, 1} |V|×|V| , a node feature matrix X ∈ R |V|×· , and an optional (sparse) edge feature matrix E ∈ R |V|×|V|×· . For a subset of nodes S ⊆ V, G[S] = (S, A S,S , X S,: , E S,S,: ) denotes the subgraph of G induced by S. We refer to N T (i) = {j ∈ V : d(i, j) ≤ T } as the T -hop neighborhood around node i ∈ V, where d : V × V → N denotes the shortest-path distance in G. A node coloring is a function V → Σ with arbitrary codomain Σ.The problem of graph matching refers to establishing node correspondences between two graphs. Formally, we are given two graphs, a source graph G s = (V s , A s , X s , E s ) and a target graphand are interested in finding a correspondence matrix S ∈ {0, 1} |Vs|×|Vt| which minimizes an objective subject to the one-to-one mapping constraints j∈Vt S i,j = 1 ∀i ∈ V s and i∈Vs S i,j ≤ 1 ∀j ∈ V t . As a result, S infers an injective mapping π : V s → V t which maps each node in G s to a node in G t . | Published as a conference paper at ICLR 2020 DEEP GRAPH MATCHING CONSENSUS |
d252873695 | This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Ridge Regression problem. Thus, we obtain the convergence to a Gaussian Process' posterior mean, which, in turn, allows us to easily transform gradient boosting into a sampler from the posterior to provide better knowledge uncertainty estimates through Monte-Carlo estimation of the posterior variance. We show that the proposed sampler allows for better knowledge uncertainty estimates leading to improved out-of-domain detection. | Published as a conference paper at ICLR 2023 GRADIENT BOOSTING PERFORMS GAUSSIAN PROCESS INFERENCE |
d257019961 | The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenario of online continual learning, wherein data comes strictly in a streaming manner, the plasticity of online continual learning is more vulnerable than offline continual learning because the training signal that can be obtained from a single data point is limited. To overcome the stability-plasticity dilemma in online continual learning, we propose an online continual learning framework named multi-scale feature adaptation network (MuFAN) that utilizes a richer context encoding extracted from different levels of a pre-trained network. Additionally, we introduce a novel structure-wise distillation loss and replace the commonly used batch normalization layer with a newly proposed stability-plasticity normalization module to train MuFAN that simultaneously maintains high plasticity and stability. Mu-FAN outperforms other state-of-the-art continual learning methods on the SVHN, CIFAR100, miniImageNet, and CORe50 datasets. Extensive experiments and ablation studies validate the significance and scalability of each proposed component: 1) multi-scale feature maps from a pre-trained encoder, 2) the structure-wise distillation loss, and 3) the stability-plasticity normalization module in MuFAN. | Published as a conference paper at ICLR 2023 NEW INSIGHTS FOR THE STABILITY-PLASTICITY DILEMMA IN ONLINE CONTINUAL LEARNING |
d184487860 | Exciting new work on generalization bounds for neural networks (NN) given by Bartlett et al. (2017); Neyshabur et al. (2018) closely depend on two parameterdependant quantities a) the Lipschitz constant upper bound and b) the stable rank (a softer version of rank). Even though these bounds typically have minimal practical utility, they facilitate questions on whether controlling such quantities together could improve the generalization behaviour of NNs in practice. To this end, we propose stable rank normalization (SRN), a novel, provably optimal, and computationally efficient weight-normalization scheme which minimizes the stable rank of a linear operator. Surprisingly we find that SRN, despite being non-convex, can be shown to have a unique optimal solution. We provide extensive analyses across a wide variety of NNs (DenseNet, WideResNet, ResNet, Alexnet, VGG), where applying SRN to their linear layers leads to improved classification accuracy, while simultaneously showing improvements in generalization, evaluated empirically using shattering experiments (Zhang et al., 2016);Published as a conference paper at ICLR 2020 simultaneously improves the generalization behaviour, while improving the classification performance of NNs. We observe improved training of Generative Adversarial Networks (GAN) Goodfellow et al. (2014) as well.To this end, we propose Stable Rank Normalization (SRN) which allows us to simultaneously control the Lipschitz constant and the stable rank of a linear operator. Note that the widely used Spectral Normalization (SN) (Miyato et al., 2018) allows explicit control over the Lipschitz constant, however, as will be discussed in the paper, it does not have any impact on the stable rank. We would like to emphasize that, as opposed to SN, the SRN solution is optimal and unique even in situations when it is non-convex. It is one of those rare cases where an optimal solution to a provably non-convex problem could be obtained. Computationally, our proposed SRN for NNs is no more complicated than SN, just requiring computation of the largest singular value which can be efficiently obtained using the power iteration method (Mises & Pollaczek-Geiringer, 1929).Experiments Although SRN is in principle applicable to any problem involving a sequence of affine transformations, considering recent interests, we show its effectiveness when applied to the linear layers of deep neural networks. We perform extensive experiments on a wide variety of NN architectures (DenseNet, WideResNet, ResNet, Alexnet, VGG) for the analyses and show that, SRN, while providing the best classification accuracy (compared against standard, or vanilla, training and SN), consistently shows improvement on the generalization behaviour. We also experiment with GANs and show that, SRN prefers learning discriminators with low empirical Lipschitz while providing improved Inception, FID and Neural Divergence scores (Gulrajani et al., 2019).We would like to note that although SN is being widely used for the training of GANs, its effect on the generalization behaviour over a wide variety of NNs has not yet been explored. To the best of our knowledge, we are the first to do so.Contributions• We propose SRN-a novel normalization scheme for simultaneously controlling the Lipschitz constant and the stable rank of a linear operator. • Optimal and unique solution to the provably non-convex stable rank normalization problem.• Efficient and easy to implement SRN algorithm for NNs., where k is the rank of the matrix. Stable rank is• a soft version of the rank operator and, unlike rank, is less sensitive to small perturbations. 2 e.g. ReLU, tanh, sigmoid, and maxout. 3 yj is the j-th element of vector y. Only one class is assigned as the ground-truth label to each x. | STABLE RANK NORMALIZATION FOR IMPROVED GEN- ERALIZATION IN NEURAL NETWORKS AND GANS |
d167217322 | Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image. However, because of the strong correlations in real-world image data, convolutional kernels are in effect re-learning redundant data. In this work, we show that this redundancy has made neural network training challenging, and propose network deconvolution, a procedure which optimally removes pixel-wise and channel-wise correlations before the data is fed into each layer. Network deconvolution can be efficiently calculated at a fraction of the computational cost of a convolution layer. We also show that the deconvolution filters in the first layer of the network resemble the center-surround structure found in biological neurons in the visual regions of the brain. Filtering with such kernels results in a sparse representation, a desired property that has been missing in the training of neural networks. Learning from the sparse representation promotes faster convergence and superior results without the use of batch normalization. We apply our network deconvolution operation to 10 modern neural network models by replacing batch normalization within each. Extensive experiments show that the network deconvolution operation is able to deliver performance improvement in all cases on the CIFAR-10, CIFAR-100, | Published as a conference paper at ICLR 2020 NETWORK DECONVOLUTION |
d30535508 | We propose a practical method for L 0 norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of L 0 regularization. However, since the L 0 norm of weights is non-differentiable, we cannot incorporate it directly as a regularization term in the objective function. We propose a solution through the inclusion of a collection of non-negative stochastic gates, which collectively determine which weights to set to zero. We show that, somewhat surprisingly, for certain distributions over the gates, the expected L 0 norm of the resulting gated weights is differentiable with respect to the distribution parameters. We further propose the hard concrete distribution for the gates, which is obtained by "stretching" a binary concrete distribution and then transforming its samples with a hard-sigmoid. The parameters of the distribution over the gates can then be jointly optimized with the original network parameters. As a result our method allows for straightforward and efficient learning of model structures with stochastic gradient descent and allows for conditional computation in a principled way. We perform various experiments to demonstrate the effectiveness of the resulting approach and regularizer. | LEARNING SPARSE NEURAL NETWORKS THROUGH L 0 REGULARIZATION |
d247762270 | It has been well recognized that neural network based image classifiers are easily fooled by images with tiny perturbations crafted by an adversary. There has been a vast volume of research to generate and defend such adversarial attacks. However, the following problem is left unexplored: How to reverse-engineer adversarial perturbations from an adversarial image? This leads to a new adversarial learning paradigm-Reverse Engineering of Deceptions (RED). If successful, RED allows us to estimate adversarial perturbations and recover the original images. However, carefully crafted, tiny adversarial perturbations are difficult to recover by optimizing a unilateral RED objective. For example, the pure image denoising method may overfit to minimizing the reconstruction error but hardly preserves the classification properties of the true adversarial perturbations. To tackle this challenge, we formalize the RED problem and identify a set of principles crucial to the RED approach design. Particularly, we find that prediction alignment and proper data augmentation (in terms of spatial transformations) are two criteria to achieve a generalizable RED approach. By integrating these RED principles with image denoising, we propose a new Class-Discriminative Denoising based RED framework, termed CDD-RED. Extensive experiments demonstrate the effectiveness of CDD-RED under different evaluation metrics (ranging from the pixel-level, prediction-level to the attribution-level alignment) and a variety of attack generation methods (e.g., FGSM, PGD, CW, AutoAttack, and adaptive attacks). Codes are available at link. * Equal contributions. | REVERSE ENGINEERING OF IMPERCEPTIBLE ADVER- SARIAL IMAGE PERTURBATIONS |
d247761945 | Training convolutional neural networks (CNNs) with a strict Lipschitz constraint under the l 2 norm is useful for provable adversarial robustness, interpretable gradients and stable training. While 1-Lipschitz CNNs can be designed by enforcing a 1-Lipschitz constraint on each layer, training such networks requires each layer to have an orthogonal Jacobian matrix (for all inputs) to prevent the gradients from vanishing during backpropagation. A layer with this property is said to be Gradient Norm Preserving (GNP). In this work, we introduce a procedure to certify the robustness of 1-Lipschitz CNNs by relaxing the orthogonalization of the last linear layer of the network that significantly advances the state of the art for both standard and provable robust accuracies on CIFAR-100 (gains of 4.80% and 4.71%, respectively). We further boost their robustness by introducing (i) a novel Gradient Norm preserving activation function called the Householder activation function (that includes every GroupSort activation) and (ii) a certificate regularization. On CIFAR-10, we achieve significant improvements over prior works in provable robust accuracy (5.81%) with only a minor drop in standard accuracy (−0.29%). Code for reproducing all experiments in the paper is available at https://github.com/singlasahil14/SOC. | |
d254096040 | Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data. However, the gradient of the log-likelihood function, i.e., the score function, is not properly defined for discrete spaces. This makes it non-trivial to adapt the score-based modeling to categorical data. In this paper, we extend diffusion models to discrete variables by introducing a stochastic jump process where the reverse process denoises via a continuous-time Markov chain. This formulation admits an analytical simulation during backward sampling. To learn the reverse process, we extend score matching to general categorical data, and show that an unbiased estimator can be obtained via simple matching of the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks. * Work done during an internship at Google. | SCORE-BASED CONTINUOUS-TIME DISCRETE DIFFU- SION MODELS |
d202888735 | We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training. This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours. We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage. In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks. * Google AI Resident . Beyond data and model parallelism for deep neural networks. CoRR, 2018. URL http://arxiv.org/abs/1807.05358.G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. | Published as a conference paper at ICLR 2020 REINFORCED GENETIC ALGORITHM LEARNING FOR OPTIMIZING COMPUTATION GRAPHS |
d257050894 | In this work, we provide a deterministic alternative to the stochastic variational training of generative autoencoders. We refer to these new generative autoencoders as AutoEncoders within Flows (AEF), since the encoder and decoder are defined as affine layers of an overall invertible architecture. This results in a deterministic encoding of the data, as opposed to the stochastic encoding of VAEs. The paper introduces two related families of AEFs. The first family relies on a partition of the ambient space and is trained by exact maximum-likelihood. The second family exploits a deterministic expansion of the ambient space and is trained by maximizing the log-probability in this extended space. This latter case leaves complete freedom in the choice of encoder, decoder and prior architectures, making it a drop-in replacement for the training of existing VAEs and VAE-style models. We show that these AEFs can have strikingly higher performance than architecturally identical VAEs in terms of log-likelihood and sample quality, especially for low dimensional latent spaces. Importantly, we show that AEF samples are substantially sharper than VAE samples. | Published as a conference paper at ICLR 2023 DETERMINISTIC TRAINING OF GENERATIVE AUTOEN- CODERS USING INVERTIBLE LAYERS |
d235614234 | Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not understood well, particularly when graphs are noisy. In this paper, we propose a self-supervised graph attention network (SuperGAT), an improved graph attention model for noisy graphs. Specifically, we exploit two attention forms compatible with a self-supervised task to predict edges, whose presence and absence contain the inherent information about the importance of the relationships between nodes. By encoding edges, SuperGAT learns more expressive attention in distinguishing mislinked neighbors. We find two graph characteristics influence the effectiveness of attention forms and self-supervision: homophily and average degree. Thus, our recipe provides guidance on which attention design to use when those two graph characteristics are known. Our experiment on 17 real-world datasets demonstrates that our recipe generalizes across 15 datasets of them, and our models designed by recipe show improved performance over baselines.arXiv:2204.04879v1 [cs.LG] 11 Apr 2022Published as a conference paper at ICLR 2021 node and its neighbors. Based on our analysis, we propose two variants of SuperGAT, scaled dotproduct (SD) and mixed GO and DP (MX), to emphasize the strength of GO and DP. | Published as a conference paper at ICLR 2021 HOW TO FIND YOUR FRIENDLY NEIGHBORHOOD: GRAPH ATTENTION DESIGN WITH SELF-SUPERVISION |
d4420783 | Understanding and characterizing the subspaces of adversarial examples aid in studying the robustness of deep neural networks (DNNs) to adversarial perturbations. Very recently,Ma et al. (2018)proposed to use local intrinsic dimensionality (LID) in layer-wise hidden representations of DNNs to study adversarial subspaces. It was demonstrated that LID can be used to characterize the adversarial subspaces associated with different attack methods, e.g., the Carlini and Wagner's (C&W) attack and the fast gradient sign attack. In this paper, we use MNIST and CIFAR-10 to conduct two new sets of experiments that are absent in existing LID analysis and report the limitation of LID in characterizing the corresponding adversarial subspaces, which are (i) oblivious attacks and LID analysis using adversarial examples with different confidence levels; and (ii) black-box transfer attacks. For (i), we find that the performance of LID is very sensitive to the confidence parameter deployed by an attack, and the LID learned from ensembles of adversarial examples with varying confidence levels surprisingly gives poor performance. For (ii), we find that when adversarial examples are crafted from another DNN model, LID is ineffective in characterizing their adversarial subspaces. These two findings together suggest the limited capability of LID in characterizing the subspaces of adversarial examples. | Workshop track -ICLR 2018 ON THE LIMITATION OF LOCAL INTRINSIC DIMEN- SIONALITY FOR CHARACTERIZING THE SUBSPACES OF ADVERSARIAL EXAMPLES |
d238583013 | Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations is particularly challenging as the latent variables are not uniquely recoverable in the most general case. In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures. We propose LEAP, a theoretically-grounded framework that extends Variational AutoEncoders (VAEs) by enforcing our conditions through proper constraints in causal process prior. Experimental results on various datasets demonstrate that temporally causal latent processes are reliably identified from observed variables under different dependency structures and that our approach considerably outperforms baselines that do not properly leverage history or nonstationarity information. This demonstrates that using temporal information to learn latent processes from their invertible nonlinear mixtures in an unsupervised manner, for which we believe our work is one of the first, seems promising even without sparsity or minimality assumptions. * Equal contribution. Code: https://github.com/weirayao/leap arXiv:2110.05428v4 [stat.ML] . Challenging common assumptions in the unsupervised learning of disentangled representations. In international conference on machine learning, pp. 4114-4124. PMLR, 2019. Kiyotoshi Matsuoka, Masahiro Ohoya, and Mitsuru Kawamoto. A neural net for blind separation of nonstationary signals. Neural networks, 8(3):411-419, 1995. Stanisław Mazur and Stanisław Ulam. Sur les transformations isométriques d'espaces vectoriels normés. CR Acad. Sci. Paris, 194(946-948):116, 1932. . A linear non-gaussian acyclic model for causal discovery. | LEARNING TEMPORALLY CAUSAL LATENT PRO- CESSES FROM GENERAL TEMPORAL DATA |
d247025766 | We are interested in training general-purpose reinforcement learning agents that can solve a wide variety of goals. Training such agents efficiently requires automatic generation of a goal curriculum. This is challenging as it requires (a) exploring goals of increasing difficulty, while ensuring that the agent (b) is exposed to a diverse set of goals in a sample efficient manner and (c) does not catastrophically forget previously solved goals. We propose Curriculum Self Play (CuSP), an automated goal generation framework that seeks to satisfy these desiderata by virtue of a multi-player game with 4 agents. We extend the asymmetric curricula learning in PAIRED (Dennis et al., 2020) to a symmetrized game that carefully balances cooperation and competition between two off-policy student learners and two regret-maximizing teachers. CuSP additionally introduces entropic goal coverage and accounts for the non-stationary nature of the students, allowing us to automatically induce a curriculum that balances progressive exploration with anticatastrophic exploitation. We demonstrate that our method succeeds at generating an effective curricula of goals for a range of control tasks, outperforming other methods at zero-shot test-time generalization to novel out-of-distribution goals. | IT TAKES FOUR TO TANGO: MULTIAGENT SELFPLAY FOR AUTOMATIC CURRICULUM GENERATION |
d3536221 | Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample complexity which hinders their use in real-world domains. Alternatively, model-based reinforcement learning promises to reduce sample complexity, but tends to require careful tuning and, to date, it has succeeded mainly in restrictive domains where simple models are sufficient for learning. In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and we show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training. To overcome this issue, we propose to use an ensemble of models to maintain the model uncertainty and regularize the learning process. We further show that the use of likelihood ratio derivatives yields much more stable learning than backpropagation through time. Altogether, our approach Model-Ensemble Trust-Region Policy Optimization (ME-TRPO) significantly reduces the sample complexity compared to model-free deep RL methods on challenging continuous control benchmark tasks 1 2 .Published as a conference paper at ICLR 2018 assumption in this approach, henceforth termed vanilla model-based RL, is that with enough data, the learned model will be accurate enough, such that a policy optimized on it will also perform well in the real environment.Although vanilla model-based RL can work well on low-dimensional tasks with relatively simple dynamics, we find that on more challenging continuous control tasks, performance was highly unstable. The reason is that the policy optimization tends to exploit regions where insufficient data is available to train the model, leading to catastrophic failures. Previous work has pointed out this issue as model biasWhile this issue can be regarded as a form of overfitting, we emphasize that standard countermeasures from the supervised learning literature, such as regularization or cross validation, are not sufficient here -supervised learning can guarantee generalization to states from the same distribution as the data, but the policy optimization stage steers the optimization exactly towards areas where data is scarce and the model is inaccurate. This problem is severely aggravated when expressive models such as deep neural networks are employed. | MODEL-ENSEMBLE TRUST-REGION POLICY OPTI- MIZATION |
d256358398 | We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video. Lying at its core is a particle-based latent space to encapsulate the hidden, Lagrangian vortical evolution underpinning the observable, Eulerian flow phenomena. Our differentiable vortex particles are coupled with a learnable, vortex-to-velocity dynamics mapping to effectively capture the complex flow features in a physically-constrained, low-dimensional space. This representation facilitates the learning of a fluid simulator tailored to the input video that can deliver robust, long-term future predictions. The value of our method is twofold: first, our learned simulator enables the inference of hidden physics quantities (e.g., velocity field) purely from visual observation; secondly, it also supports future prediction, constructing the input video's sequel along with its future dynamics evolution. We compare our method with a range of existing methods on both synthetic and real-world videos, demonstrating improved reconstruction quality, visual plausibility, and physical integrity. 1 | Published as a conference paper at ICLR 2023 LEARNING VORTEX DYNAMICS FOR FLUID INFERENCE AND PREDICTION |
d256616184 | The robustness of a deep classifier can be characterized by its margins: the decision boundary's distances to natural data points. However, it is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training. To understand this, we propose a continuous-time framework for quantifying the relative speed of the decision boundary with respect to each individual point. Through visualizing the moving speed of the decision boundary under Adversarial Training, one of the most effective robust training algorithms, a surprising moving-behavior is revealed: the decision boundary moves away from some vulnerable points but simultaneously moves closer to others, decreasing their margins. To alleviate these conflicting dynamics of the decision boundary, we propose Dynamics-Aware Robust Training (DyART), which encourages the decision boundary to engage in movement that prioritizes increasing smaller margins. In contrast to prior works, DyART directly operates on the margins rather than their indirect approximations, allowing for more targeted and effective robustness improvement. Experiments on the CIFAR-10 and Tiny-ImageNet datasets verify that DyART alleviates the conflicting dynamics of the decision boundary and obtains improved robustness under various perturbation sizes compared to the state-of-the-art defenses. Our code is available at https://github.com/ Yuancheng-Xu/Dynamics-Aware-Robust-Training.arXiv:2302.03015v2 [cs.LG] 15 Apr 2023Published as a conference paper at ICLR 2023 (a) The decision boundary, vulnerable (solid) and robust (hollow) points.StepStepLess RobustMore Robust (b) An update with conflicting impacts on robustness.Continuous movementStep (c) Continuous movement of the decision boundary. | Published as a conference paper at ICLR 2023 EXPLORING AND EXPLOITING DECISION BOUNDARY DYNAMICS FOR ADVERSARIAL ROBUSTNESS |
d14469187 | We study the problem of large scale, multi-label visual recognition with a large number of possible classes. We propose a method for augmenting a trained neural network classifier with auxiliary capacity in a manner designed to significantly improve upon an already well-performing model, while minimally impacting its computational footprint. Using the predictions of the network itself as a descriptor for assessing visual similarity, we define a partitioning of the label space into groups of visually similar entities. We then augment the network with auxilliary hidden layer pathways with connectivity only to these groups of label units. We report a significant improvement in mean average precision on a large-scale object recognition task with the augmented model, while increasing the number of multiply-adds by less than 3%. * Work done while DWF was an intern at Google. | Under review as a workshop contribution at ICLR 2015 SELF-INFORMED NEURAL NETWORK STRUCTURE LEARNING |
d3625614 | This paper presents a novel form of policy gradient for model-free reinforcement learning (RL) with improved exploration properties. Current policy-based methods use entropy regularization to encourage undirected exploration of the reward landscape, which is ineffective in high dimensional spaces with sparse rewards. We propose a more directed exploration strategy that promotes exploration of under-appreciated reward regions. An action sequence is considered under-appreciated if its log-probability under the current policy under-estimates its resulting reward. The proposed exploration strategy is easy to implement, requiring small modifications to the REINFORCE algorithm. We evaluate the approach on a set of algorithmic tasks that have long challenged RL methods. Our approach reduces hyper-parameter sensitivity and demonstrates significant improvements over baseline methods. The proposed algorithm successfully solves a benchmark multi-digit addition task and generalizes to long sequences, which, to our knowledge, is the first time that a pure RL method has solved addition using only reward feedback. * Work done as a member of the Google Brain Residency program (g.co/brainresidency) | Published as a conference paper at ICLR 2017 IMPROVING POLICY GRADIENT BY EXPLORING UNDER-APPRECIATED REWARDS |
d236318271 | The Shapley value is one of the most widely used measures of feature importance partly as it measures a feature's average effect on a model's prediction. We introduce joint Shapley values, which directly extend Shapley's axioms and intuitions: joint Shapley values measure a set of features' average contribution to a model's prediction. We prove the uniqueness of joint Shapley values, for any order of explanation. Results for games show that joint Shapley values present different insights from existing interaction indices, which assess the effect of a feature within a set of features. The joint Shapley values provide intuitive results in ML attribution problems. With binary features, we present a presence-adjusted global value that is more consistent with local intuitions than the usual approach.Published as a conference paper at ICLR 2022 Published as a conference paper at ICLR 2022 SY symmetry : If two agents add equal worth to all coalitions that they can both join then they receive equal value:. This is strictly weaker than anonymity.Now extend each of these axioms in natural ways to conditions on sets rather than singletons. Below, φ S (v) denotes an index for coalition S on game v.JLI joint linearity : φ is a linear function on G N , i.e. φ(v + w) = φ(v) + φ(w) and φ(av) = aφ(v) for any v, w ∈ G N and a ∈ R. (This axiom has not been modified.) | JOINT SHAPLEY VALUES: A MEASURE OF JOINT FEA- TURE IMPORTANCE |
d69370873 | We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required. Based on these contexts, we allow the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to an enhanced compression performance. Based on the experimental results, the proposed method outperforms the traditional image codecs, such as BPG and JPEG2000, as well as other previous artificial-neural-network (ANN) based approaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) index. The test code is publicly available at https://github.com/JooyoungLeeETRI/CA_Entropy_Model. | CONTEXT-ADAPTIVE ENTROPY MODEL FOR END-TO- END OPTIMIZED IMAGE COMPRESSION |
d6681692 | This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. We obtain state-of-the-art on the NYU-v2 depth dataset with an accuracy of 64.5%. We illustrate the labeling of indoor scenes in videos sequences that could be processed in real-time using appropriate hardware such as an FPGA. | Indoor Semantic Segmentation using depth information |
d252596168 | Low-rank approximation in data streams is a fundamental and significant task in computing science, machine learning and statistics. Multiple streaming algorithms have emerged over years and most of them are inspired by randomized algorithms, more specifically, sketching methods. However, many algorithms are not able to leverage information of data streams and consequently suffer from low accuracy. Existing data-driven methods improve accuracy but the training cost is expensive in practice. In this paper, from a subspace perspective, we propose a tensor-based sketching method for low-rank approximation of data streams. The proposed algorithm fully exploits the structure of data streams and obtains quasi-optimal sketching matrices by performing tensor decomposition on training data. A series of experiments are carried out and show that the proposed tensor-based method can be more accurate and much faster than the previous work.where d = 1, 2, · · · , D, · F represents the Frobenius norm, and r ∈ Z + is a user-specified target rank.Related work. A direct approach to solve problem (1.1) is to calculate the truncated rank-r singular value decomposition (SVD) of A d in turn, and the Eckart-Young theorem ensures that it is the best low-rank approximation[8]. However, it is too expensive to one by one calculate the truncated 36th Conference on Neural Information Processing Systems (NeurIPS 2022). | Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams |
d244920855 | Recent works indicate that convolutional neural networks (CNN) need large receptive fields (RF) to compete with visual transformers and their attention mechanism. In CNNs, RFs can simply be enlarged by increasing the convolution kernel sizes. Yet the number of trainable parameters, which scales quadratically with the kernel's size in the 2D case, rapidly becomes prohibitive, and the training is notoriously difficult. This paper presents a new method to increase the RF size without increasing the number of parameters. The dilated convolution (DC) has already been proposed for the same purpose. DC can be seen as a convolution with a kernel that contains only a few non-zero elements placed on a regular grid. Here we present a new version of the DC in which the spacings between the nonzero elements, or equivalently their positions, are no longer fixed but learnable via backpropagation thanks to an interpolation technique. We call this method "Dilated Convolution with Learnable Spacings" (DCLS) and generalize it to the n-dimensional convolution case. However, our main focus here will be on the 2D case for computer vision only. We first tried our approach on ResNet50: we drop-in replaced the standard convolutions with DCLS ones, which increased the accuracy of ImageNet1k classification at iso-parameters, but at the expense of the throughput. Next, we used the recent ConvNeXt state-of-the-art convolutional architecture and drop-in replaced the depthwise convolutions with DCLS ones. This not only increased the accuracy of ImageNet1k classification but also of typical downstream and robustness tasks, again at iso-parameters but this time with negligible cost on throughput, as ConvNeXt uses separable convolutions. Conversely, classic DC led to poor performance with both ResNet50 and ConvNeXt. The code of the method is based on PyTorch and available. | Published as a conference paper at ICLR 2023 DILATED CONVOLUTION WITH LEARNABLE SPACINGS |
d247939414 | We propose a probabilistic shape completion method extended to the continuous geometry of large-scale 3D scenes. Real-world scans of 3D scenes suffer from a considerable amount of missing data cluttered with unsegmented objects. The problem of shape completion is inherently ill-posed, and high-quality result requires scalable solutions that consider multiple possible outcomes. We employ the Generative Cellular Automata that learns the multi-modal distribution and transform the formulation to process large-scale continuous geometry. The local continuous shape is incrementally generated as a sparse voxel embedding, which contains the latent code for each occupied cell. We formally derive that our training objective for the sparse voxel embedding maximizes the variational lower bound of the complete shape distribution and therefore our progressive generation constitutes a valid generative model. Experiments show that our model successfully generates diverse plausible scenes faithful to the input, especially when the input suffers from a significant amount of missing data. We also demonstrate that our approach outperforms deterministic models even in less ambiguous cases with a small amount of missing data, which infers that probabilistic formulation is crucial for high-quality geometry completion on input scans exhibiting any levels of completeness. | Published as a conference paper at ICLR 2022 PROBABILISTIC IMPLICIT SCENE COMPLETION |
d3654210 | We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning problem of DNNs as a constrained nonconvex optimization problem, and then adopt the ADMM framework for systematic weight pruning. We show that ADMM is highly suitable for weight pruning due to the computational efficiency it offers. We achieve a much higher compression ratio compared with prior work while maintaining the same test accuracy, together with a faster convergence rate. Our models are released at https://github.com/KaiqiZhang/admm-pruning. | SYSTEMATIC WEIGHT PRUNING OF DNNS USING ALTERNATING DIRECTION METHOD OF MULTIPLIERS |
d15450945 | In this paper, we investigate a new framework for image classification that adaptively generates spatial representations. Our strategy is based on a sequential process that learns to explore the different regions of any image in order to infer its category. In particular, the choice of regions is specific to each image, directed by the actual content of previously selected regions.The capacity of the system to handle incomplete image information as well as its adaptive region selection allow the system to perform well in budgeted classification tasks by exploiting a dynamicly generated representation of each image. We demonstrate the system's abilities in a series of image-based exploration and classification tasks that highlight its learned exploration and inference abilities. our contributions. Section 3 gives the theoretical background of our sequential model, and Section 4 details the training algorithm. Finally, Section 5 reports classification scores on two challenging image datasets. 223 | Sequentially Generated Instance-Dependent Image Representations for Classification |
d211146562 | Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose DivideMix, a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques. In particular, DivideMix models the per-sample loss distribution with a mixture model to dynamically divide the training data into a labeled set with clean samples and an unlabeled set with noisy samples, and trains the model on both the labeled and unlabeled data in a semi-supervised manner. To avoid confirmation bias, we simultaneously train two diverged networks where each network uses the dataset division from the other network. During the semi-supervised training phase, we improve the MixMatch strategy by performing label co-refinement and label co-guessing on labeled and unlabeled samples, respectively. Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods. Code is available at | Published as a conference paper at ICLR 2020 DIVIDEMIX: LEARNING WITH NOISY LABELS AS SEMI-SUPERVISED LEARNING |
d252683964 | Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot navigation tasks in complex environments. However, these methods often require expert data for learning, which limits their application to scenarios where data generation is time-consuming. Recent developments have also led to physicsinformed deep neural models capable of representing complex dynamical Partial Differential Equations (PDEs). Inspired by these developments, we propose Neural Time Fields (NTFields) for robot motion planning in cluttered scenarios. Our framework represents a wave propagation model generating continuous arrival time to find path solutions informed by a nonlinear first-order PDE called the Eikonal equation. We evaluate our method in various cluttered 3D environments, including the Gibson dataset, and demonstrate its ability to solve motion planning problems for 4-DOF and 6-DOF robot manipulators where the traditional grid-based Eikonal planners often face the curse of dimensionality. Furthermore, the results show that our method exhibits high success rates and significantly lower computational times than the state-of-the-art methods, including NMPs that require training data from classical planners. Our code is released: https://github.com/ruiqini/ NTFields. | Published as a conference paper at ICLR 2023 NTFIELDS: NEURAL TIME FIELDS FOR PHYSICS- INFORMED ROBOT MOTION PLANNING |
d252968255 | The classical Perceptron algorithm of Rosenblatt can be used to find a linear threshold function to correctly classify n linearly separable data points, assuming the classes are separated by some margin γ > 0. A foundational result is that Perceptron converges after Ω(1/γ 2 ) iterations. There have been several recent works that managed to improve this rate by a quadratic factor, to Ω( √ log n/γ), with more sophisticated algorithms. In this paper, we unify these existing results under one framework by showing that they can all be described through the lens of solving min-max problems using modern acceleration techniques, mainly through optimistic online learning. We then show that the proposed framework also lead to improved results for a series of problems beyond the standard Perceptron setting. Specifically, a) For the margin maximization problem, we improve the state-of-the-art result from O(log t/t 2 ) to O(1/t 2 ), where t is the number of iterations; b) We provide the first result on identifying the implicit bias property of the classical Nesterov's accelerated gradient descent (NAG) algorithm, and show NAG can maximize the margin with an O(1/t 2 ) rate; c) For the classical p-norm Perceptron problem, we provide an algorithm with Ω( (p − 1) log n/γ) convergence rate, while existing algorithms suffer the Ω((p − 1)/γ 2 ) convergence rate. * An earlier version of this paper titled "Linear Separation via Optimism" is posted on: https://arxiv.org/abs/2011.08797. accelerated variants of the Perceptron with a faster Ω( √ log n/γ) iteration complexity, although with a slower per-iteration cost. These works model the problem in different ways, e.g., as a non-smooth optimization problem or an empirical risk minimization task, and they have established faster rates using sophisticated optimization tools. Soheili & Pena (2012) put forward the smooth Perceptron, framing the objective as a non-smooth strongly-concave maximization and then applying Nestrov's excessive gap technique (NEG, Nesterov, 2005). Yu et al. (2014) proposed the accelerated Perceptron by furnishing a convex-concave objective that can be solved via the mirror-prox method (Nemirovski, 2004). Ji et al. (2021) put forward a third interpretation, obtaining the accelerated rate by minimizing the empirical risk under exponential loss with a momentum-based normalized gradient descent algorithm.Following this line of research, in this paper, we present a unified analysis framework that reveals the exact relationship among these methods that share the same order of convergence rate. Moreover, we show that the proposed framework also leads to improved results for various problems beyond the standard Perceptron setting. Specifically, we consider a general zero-sum game that involves two players (Abernethy et al., 2018): a main player that chooses the classifier, and an auxiliary player that picks a distribution over data. The two players compete with each other by performing no-regret online learning algorithms (Hazan, 2016; Orabona, 2019), and the goal is to find the equilibrium of some convex-concave function. We show that, under this dynamic | On Accelerated Perceptrons and Beyond * |
d246823263 | We consider a context-dependent Reinforcement Learning (RL) setting, which is characterized by: a) an unknown finite number of not directly observable contexts; b) abrupt (discontinuous) context changes occurring during an episode; and c) Markovian context evolution. We argue that this challenging case is often met in applications and we tackle it using a Bayesian approach and variational inference. We adapt a sticky Hierarchical Dirichlet Process (HDP) prior for model learning, which is arguably best-suited for Markov process modeling. We then derive a context distillation procedure, which identifies and removes spurious contexts in an unsupervised fashion. We argue that the combination of these two components allows to infer the number of contexts from data thus dealing with the context cardinality assumption. We then find the representation of the optimal policy enabling efficient policy learning using off-the-shelf RL algorithms. Finally, we demonstrate empirically (using gym environments cart-pole swing-up, drone, intersection) that our approach succeeds where state-of-the-art methods of other frameworks fail and elaborate on the reasons for such failures. | Published as a conference paper at ICLR 2022 REINFORCEMENT LEARNING IN PRESENCE OF DIS- CRETE MARKOVIAN CONTEXT EVOLUTION |
d233714270 | To quickly solve new tasks in complex environments, intelligent agents need to build up reusable knowledge. For example, a learned world model captures knowledge about the environment that applies to new tasks. Similarly, skills capture general behaviors that can apply to new tasks. In this paper, we investigate how these two approaches can be integrated into a single reinforcement learning agent. Specifically, we leverage the idea of partial amortization for fast adaptation at test time. For this, actions are produced by a policy that is learned over time while the skills it conditions on are chosen using online planning. We demonstrate the benefits of our design decisions across a suite of challenging locomotion tasks and demonstrate improved sample efficiency in single tasks as well as in transfer from one task to another, as compared to competitive baselines. Videos are available at: https://sites.google.com/view/latent-skill-planning/ * Kevin and Homanga contributed equally to this work.arXiv:2011.13897v2 [cs.LG] 2 May 2021Published as a conference paper at ICLR 2021 search procedure such as Cross-Entropy Method (CEM; Rubinstein, 1997) on the learned models to determine which action to execute next. Since rollouts from the learned dynamics and reward models are not executed in the actual environment during training, these learned models are sometimes also referred to as imagination models(Hafner et al., 2018;. Fully amortized methods such as Dreamer(Hafner et al., 2019), train a reactive policy with many rollouts from the imagination model. They then execute the resulting policy in the environment. | Published as a conference paper at ICLR 2021 LATENT SKILL PLANNING FOR EXPLORATION AND TRANSFER |
d211126567 | Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query. However, prior work models queries as single points in the vector space, which is problematic because a complex query represents a potentially large set of its answer entities, but it is unclear how such a set can be represented as a single point. Furthermore, prior work can only handle queries that use conjunctions (∧) and existential quantifiers (∃). Handling queries with logical disjunctions (∨) remains an open problem. Here we propose QUERY2BOX, an embedding-based framework for reasoning over arbitrary queries with ∧, ∨, and ∃ operators in massive and incomplete KGs. Our main insight is that queries can be embedded as boxes (i.e., hyper-rectangles), where a set of points inside the box corresponds to a set of answer entities of the query. We show that conjunctions can be naturally represented as intersections of boxes and also prove a negative result that handling disjunctions would require embedding with dimension proportional to the number of KG entities. However, we show that by transforming queries into a Disjunctive Normal Form, QUERY2BOX is capable of handling arbitrary logical queries with ∧, ∨, ∃ in a scalable manner. We demonstrate the effectiveness of QUERY2BOX on three large KGs and show that QUERY2BOX achieves up to 25% relative improvement over the state of the art. * Equal contributions. Project website with data and code: http://snap.stanford.edu/ query2box | Published as a conference paper at ICLR 2020 QUERY2BOX: REASONING OVER KNOWLEDGE GRAPHS IN VECTOR SPACE USING BOX EMBEDDINGS |
d17801454 | Most representation learning algorithms for language and image processing are local, in that they identify features for a data point based on surrounding points. Yet in language processing, the correct meaning of a word often depends on its global context. As a step toward incorporating global context into representation learning, we develop a representation learning algorithm that incorporates joint prediction into its technique for producing features for a word. We develop efficient variational methods for learning Factorial Hidden Markov Models from large texts, and use variational distributions to produce features for each word that are sensitive to the entire input sequence, not just to a local context window. Experiments on part-of-speech tagging and chunking indicate that the features are competitive with or better than existing state-of-the-art representation learning methods. | Factorial Hidden Markov Models for Learning Representations of Natural Language |
d239024500 | Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data. Most existing offline RL algorithms use regularization or constraints to suppress extrapolation error for actions outside the dataset. In this paper, we adopt a different framework, which learns the V -function instead of the Q-function to naturally keep the learning procedure within the support of an offline dataset. To enable effective generalization while maintaining proper conservatism in offline learning, we propose Expectile V -Learning (EVL), which smoothly interpolates between the optimal value learning and behavior cloning. Further, we introduce implicit planning along offline trajectories to enhance learned V -values and accelerate convergence. Together, we present a new offline method called Value-based Episodic Memory (VEM). We provide theoretical analysis for the convergence properties of our proposed VEM method, and empirical results in the D4RL benchmark show that our method achieves superior performance in most tasks, particularly in sparse-reward tasks. | OFFLINE REINFORCEMENT LEARNING WITH VALUE- BASED EPISODIC MEMORY |
d14082107 | This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local features, in particular, by taking geometric invariance into explicit account, i.e. positions, scales and spatial consistency. In our experiments using five standard image retrieval datasets, we demonstrate that generic ConvNet image representations can outperform other state-of-the-art methods if they are extracted appropriately. | Paper Visual Instance Retrieval with Deep Convolutional Networks |
d11772257 | We describe a general framework for online adaptation of optimization hyperparameters by 'hot swapping' their values during learning. We investigate this approach in the context of adaptive learning rate selection using an explore-exploit strategy from the multi-armed bandit literature. Experiments on a benchmark neural network show that the hot swapping approach leads to consistently better solutions compared to well-known alternatives such as AdaDelta and stochastic gradient with exhaustive hyperparameter search. | Under review as a workshop contribution at ICLR 2015 HOT SWAPPING FOR ONLINE ADAPTATION OF OPTIMIZATION HYPERPARAMETERS |
d249097739 | Time-dependent data-generating distributions have proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previously learned knowledge. Despite the progress in the field of continual learning to overcome this forgetting, we show that a set of common state-of-the-art methods still suffers from substantial forgetting upon starting to learn new tasks, except that this forgetting is temporary and followed by a phase of performance recovery. We refer to this intriguing but potentially problematic phenomenon as the stability gap. The stability gap had likely remained under the radar due to standard practice in the field of evaluating continual learning models only after each task. Instead, we establish a framework for continual evaluation that uses per-iteration evaluation and we define a new set of metrics to quantify worst-case performance. Empirically we show that experience replay, constraintbased replay, knowledge-distillation, and parameter regularization methods are all prone to the stability gap; and that the stability gap can be observed in class-, task-, and domain-incremental learning benchmarks. Additionally, a controlled experiment shows that the stability gap increases when tasks are more dissimilar. Finally, by disentangling gradients into plasticity and stability components, we propose a conceptual explanation for the stability gap. | Published as a conference paper at ICLR 2023 CONTINUAL EVALUATION FOR LIFELONG LEARNING: IDENTIFYING THE STABILITY GAP |
d251643400 | Estimating conditional average treatment effects (CATEs) from observational data is relevant in many fields such as personalized medicine. However, in practice, the treatment assignment is usually confounded by unobserved variables and thus introduces bias. A remedy to remove the bias is the use of instrumental variables (IVs). Such settings are widespread in medicine (e.g., trials where the treatment assignment is used as binary IV). In this paper, we propose a novel, multiply robust machine learning framework, called MRIV, for estimating CATEs using binary IVs and thus yield an unbiased CATE estimator. Different from previous work for binary IVs, our framework estimates the CATE directly via a pseudo outcome regression.(1) We provide a theoretical analysis where we show that our framework yields multiple robust convergence rates: our CATE estimator achieves fast convergence even if several nuisance estimators converge slowly.(2) We further show that our framework asymptotically outperforms state-of-the-art plug-in IV methods for CATE estimation, in the sense that it achieves a faster rate of convergence if the CATE is smoother than the individual outcome surfaces. (3) We build upon our theoretical results and propose a tailored deep neural network architecture called MRIV-Net for CATE estimation using binary IVs. Across various computational experiments, we demonstrate empirically that our MRIV-Net achieves state-of-theart performance. To the best of our knowledge, our MRIV is the first multiply robust machine learning framework tailored to estimating CATEs in the binary IV setting. arXiv:2208.08544v3 [stat.ME] . Estimating counterfactual treatment outcomes over time through adversarially balanced representations. In ICLR, 2020a.Ioana Bica, Ahmed M. Alaa, and Mihaela van der Schaar. Time series deconfounder: Estimating treatment effects over time in the presence of hidden confounders. In ICML, 2020b. | Published as a conference paper at ICLR 2023 ESTIMATING INDIVIDUAL TREATMENT EFFECTS UN- DER UNOBSERVED CONFOUNDING USING BINARY IN- STRUMENTS |
d251647801 | The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate. This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN and labelling the generated images with an automatic annotator. In particular, we formulate the annotator learning as a learning-to-learn problem. Given a pre-trained GAN, the annotator learns to label object parts in a set of randomly generated images such that a part segmentation model trained on these synthetic images with their predicted labels obtains low segmentation error on a small validation set of manually labelled images. We further reduce this nested-loop optimization problem to a simple gradient matching problem and efficiently solve it with an iterative algorithm. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images. Our method is evaluated with semi-supervised part segmentation tasks and significantly outperforms other semi-supervised competitors when the amount of labelled examples is extremely limited. | Published as a conference paper at ICLR 2022 LEARNING TO ANNOTATE PART SEGMENTATION WITH GRADIENT MATCHING |
d249097488 | In this work we propose a Reinforcement Learning (RL) agent that can discover complex behaviours in a rich environment with a simple reward function. We define diversity in terms of state-action occupancy measures, since policies with different occupancy measures visit different states on average. More importantly, defining diversity in this way allows us to derive an intrinsic reward function for maximizing the diversity directly. Our agent, DOMiNO, stands for Diversity Optimization Maintaining Near Optimally. It is based on maximizing a reward function with two components: the extrinsic reward and the diversity intrinsic reward, which are combined with Lagrange multipliers to balance the quality-diversity trade-off. Any RL algorithm can be used to maximize this reward and no other changes are needed. We demonstrate that given a simple reward functions in various control domains, like height (stand) and forward velocity (walk), DOMiNO discovers diverse and meaningful behaviours. We also perform extensive analysis of our approach, compare it with other multi-objective baselines, demonstrate that we can control both the quality and the diversity of the set via interpretable hyperparameters, and show that the set is robust to perturbations of the environment. | Published as a conference paper at ICLR 2023 DISCOVERING POLICIES WITH DOMINO: DIVERSITY OPTIMIZATION MAINTAINING NEAR OPTIMALITY |
d249674757 | We present Masked Frequency Modeling (MFM), a unified frequency-domainbased approach for self-supervised pre-training of visual models. Instead of randomly inserting mask tokens to the input embeddings in the spatial domain, in this paper, we shift the perspective to the frequency domain. Specifically, MFM first masks out a portion of frequency components of the input image and then predicts the missing frequencies on the frequency spectrum. Our key insight is that predicting masked components in the frequency domain is more ideal to reveal underlying image patterns rather than predicting masked patches in the spatial domain, due to the heavy spatial redundancy. Our findings suggest that with the right configuration of mask-and-predict strategy, both the structural information within high-frequency components and the low-level statistics among low-frequency counterparts are useful in learning good representations. For the first time, MFM demonstrates that, for both ViT and CNN, a simple non-Siamese framework can learn meaningful representations even using none of the following: (i) extra data, (ii) extra model, (iii) mask token. Experimental results on image classification and semantic segmentation, as well as several robustness benchmarks show the competitive performance and advanced robustness of MFM compared with recent masked image modeling approaches. Furthermore, we also comprehensively investigate the effectiveness of classical image restoration tasks for representation learning from a unified frequency perspective and reveal their intriguing relations with our MFM approach. Project page: httpsGrave. Are large-scale datasets necessary for self-supervised pre-training? arXiv preprint arXiv:2112.10740, 2021. Mike Guo, et al. The many faces of robustness: A critical analysis of out-of-distribution generalization. In ICCV, 2021a. | Published as a conference paper at ICLR 2023 MASKED FREQUENCY MODELING FOR SELF-SUPERVISED VISUAL PRE-TRAINING |
d3520830 | The huge size of deep networks hinders their use in small computing devices. In this paper, we consider compressing the network by weight quantization. We extend a recently proposed loss-aware weight binarization scheme to ternarization, with possibly different scaling parameters for the positive and negative weights, and m-bit (where m > 2) quantization. Experiments on feedforward and recurrent neural networks show that the proposed scheme outperforms state-of-the-art weight quantization algorithms, and is as accurate (or even more accurate) than the full-precision network.arXiv:1802.08635v2 [cs.LG] 10 May 2018Published as a conference paper at ICLR 2018(2016)and added scaling to the ternarized weights, and DoReFa-Net (Zhou et al., 2016) further extended quantization to more than three levels. However, these methods do not consider the effect of quantization on the loss, and rely on heuristics in their procedures (Zhou et al., 2016;. Recently, a loss-aware low-bit quantized neural network is proposed in(Leng et al., 2017). However, it uses full-precision weights in the forward pass and the extra-gradient method (Vasilyev et al., 2010) for update, both of which are expensive.In this paper, we propose an efficient and disciplined ternarization scheme for network compression. Inspired by(Hou et al., 2017), we explicitly consider the effect of ternarization on the loss. This is formulated as an optimization problem which is then solved efficiently by the proximal Newton algorithm. When the loss surface's curvature is ignored, the proposed method reduces to that of(Li & Liu, 2016), and is also related to the projection step of (Leng et al., 2017). Next, we extend it to (i) allow the use of different scaling parameters for the positive and negative weights; and (ii) the use of m bits (where m > 2) for weight quantization. Experiments on both feedforward and recurrent neural networks show that the proposed quantization scheme outperforms state-of-the-art algorithms. | LOSS-AWARE WEIGHT QUANTIZATION OF DEEP NET- WORKS |
d256459705 | As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). While quantization is a powerful approach to reducing GNNs complexity, most previous works on GNNs quantization fail to exploit the unique characteristics of GNNs, suffering from severe accuracy degradation. Through an in-depth analysis of the topology of GNNs, we observe that the topology of the graph leads to significant differences between nodes, and most of the nodes in a graph appear to have a small aggregation value. Motivated by this, in this paper, we propose the Aggregation-Aware mixed-precision Quantization (A 2 Q) for GNNs, where an appropriate bitwidth is automatically learned and assigned to each node in the graph. To mitigate the vanishing gradient problem caused by sparse connections between nodes, we propose a Local Gradient method to serve the quantization error of the node features as the supervision during training. We also develop a Nearest Neighbor Strategy to deal with the generalization on unseen graphs. Extensive experiments on eight public node-level and graph-level datasets demonstrate the generality and robustness of our proposed method. Compared to the FP32 models, our method can achieve up to a 18.6x (i.e., 1.70bit) compression ratio with negligible accuracy degradation. Morever, compared to the state-of-theart quantization method, our method can achieve up to 11.4% and 9.5% accuracy improvements on the node-level and graph-level tasks, respectively, and up to 2x speedup on a dedicated hardware accelerator. . Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks. | A 2 Q: AGGREGATION-AWARE QUANTIZATION FOR GRAPH NEURAL NETWORKS |
d231879554 | Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce, as such libraries require years of concentrated effort by human specialists to be built. This is particularly challenging when applying large Transformer language models to tactic prediction, because the scaling of performance with respect to model size is quickly disrupted in the data-scarce, easily-overfitted regime. We propose PACT (Proof Artifact Co-Training), a general methodology for extracting abundant self-supervised data from kernel-level proof terms for joint training alongside the usual tactic prediction objective. We apply this methodology to Lean, a proof assistant host to some of the most sophisticated formalized mathematics to date. We instrument Lean with a neural theorem prover driven by a Transformer language model and show that PACT improves theorem proving success rate on a held-out suite of test theorems from 32% to 48%. | Published as a conference paper at ICLR 2022 PROOF ARTIFACT CO-TRAINING FOR THEOREM PROV- ING WITH LANGUAGE MODELS |
d208309981 | A recent line of research on deep learning focuses on the extremely overparameterized setting, and shows that when the network width is larger than a high degree polynomial of the training sample size n and the inverse of the target error ´1 , deep neural networks learned by (stochastic) gradient descent enjoy nice optimization and generalization guarantees. Very recently, it is shown that under certain margin assumptions on the training data, a polylogarithmic width condition suffices for two-layer ReLU networks to converge and generalize (Ji and Telgarsky, 2020). However, whether deep neural networks can be learned with such a mild over-parameterization is still an open question. In this work, we answer this question affirmatively and establish sharper learning guarantees for deep ReLU networks trained by (stochastic) gradient descent. In specific, under certain assumptions made in previous work, our optimization and generalization guarantees hold with network width polylogarithmic in n and ´1 . Our results push the study of over-parameterized deep neural networks towards more practical settings. * Equal contribution. | Published as a conference paper at ICLR 2021 HOW MUCH OVER-PARAMETERIZATION IS SUFFI- CIENT TO LEARN DEEP RELU NETWORKS? |
d231662087 | Identifying harmful instances, whose absence in a training dataset improves model performance, is important for building better machine learning models. Although previous studies have succeeded in estimating harmful instances under supervised settings, they cannot be trivially extended to generative adversarial networks (GANs). This is because previous approaches require that (i) the absence of a training instance directly affects the loss value and that (ii) the change in the loss directly measures the harmfulness of the instance for the performance of a model. In GAN training, however, neither of the requirements is satisfied. This is because, (i) the generator's loss is not directly affected by the training instances as they are not part of the generator's training steps, and (ii) the values of GAN's losses normally do not capture the generative performance of a model. To this end, (i) we propose an influence estimation method that uses the Jacobian of the gradient of the generator's loss with respect to the discriminator's parameters (and vice versa) to trace how the absence of an instance in the discriminator's training affects the generator's parameters, and (ii) we propose a novel evaluation scheme, in which we assess harmfulness of each training instance on the basis of how GAN evaluation metric (e.g., inception score) is expected to change due to the removal of the instance. We experimentally verified that our influence estimation method correctly inferred the changes in GAN evaluation metrics. We also demonstrated that the removal of the identified harmful instances effectively improved the model's generative performance with respect to various GAN evaluation metrics.arXiv:2101.08367v3 [stat.ML] 19 Oct 2022Published as a conference paper at ICLR 2021 (Spotlight) | INFLUENCE ESTIMATION FOR GENERATIVE ADVER- SARIAL NETWORKS |
d258835696 | Neural fields are receiving increased attention as a geometric representation due to their ability to compactly store detailed and smooth shapes and easily undergo topological changes. Compared to classic geometry representations, however, neural representations do not allow the user to exert intuitive control over the shape. Motivated by this, we leverage boundary sensitivity to express how perturbations in parameters move the shape boundary. This allows to interpret the effect of each learnable parameter and study achievable deformations. With this, we perform geometric editing: finding a parameter update that best approximates a globally prescribed deformation. Prescribing the deformation only locally allows the rest of the shape to change according to some prior, such as semantics or deformation rigidity. Our method is agnostic to the model its training and updates the NN in-place. Furthermore, we show how boundary sensitivity helps to optimize and constrain objectives (such as surface area and volume), which are difficult to compute without first converting to another representation, such as a mesh. | Published as a conference paper at ICLR 2023 NEURAL IMPLICIT SHAPE EDITING USING BOUNDARY SENSITIVITY |
d231933963 | In this work we consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process, such as in the design of materials, vehicles, or neural network architectures. Because the available data typically only covers a small manifold of the possible space of inputs, a principal challenge is to be able to construct algorithms that can reason about uncertainty and out-of-distribution values, since a naive optimizer can easily exploit an estimated model to return adversarial inputs. We propose to tackle this problem by leveraging the normalized maximum-likelihood (NML) estimator, which provides a principled approach to handling uncertainty and out-of-distribution inputs. While in the standard formulation NML is intractable, we propose a tractable approximation that allows us to scale our method to high-capacity neural network models. We demonstrate that our method can effectively optimize high-dimensional design problems in a variety of disciplines such as chemistry, biology, and materials engineering.Published as a conference paper at ICLR 2021 vides a flexible approach to constructing conservative and robust estimators using high-dimensional models such as neural networks. | OFFLINE MODEL-BASED OPTIMIZATION VIA NOR- MALIZED MAXIMUM LIKELIHOOD ESTIMATION |
d254854609 | Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanding industries. In the attempt to unpack them, many works observe or impact internal variables to improve the comprehensibility and invertibility of the black-box models. However, existing methods rely on intuitive assumptions and lack mathematical guarantees. To bridge this gap, we introduce Bort, an optimizer for improving model explainability with Boundedness and orthogonality constraints on model parameters, derived from the sufficient conditions of model comprehensibility and invertibility. We perform reconstruction and backtracking on the model representations optimized by Bort and observe a clear improvement in model explainability. Based on Bort, we are able to synthesize explainable adversarial samples without additional parameters and training. Surprisingly, we find Bort constantly improves the classification accuracy of various architectures including ResNet and DeiT on MNIST, CIFAR-10, and ImageNet. Code: https://github.com/zbr17/Bort. | Published as a conference paper at ICLR 2023 BORT: TOWARDS EXPLAINABLE NEURAL NETWORKS WITH BOUNDED ORTHOGONAL CONSTRAINT |
d202889044 | Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models. In this work, we propose a novel adversarial training algorithm -FreeLB, that promotes higher robustness and invariance in the embedding space, by adding adversarial perturbations to word embeddings and minimizing the resultant adversarial risk inside different regions around input samples. To validate the effectiveness of the proposed approach, we apply it to Transformerbased models for natural language understanding and commonsense reasoning tasks. Experiments on the GLUE benchmark show that when applied only to the finetuning stage, it is able to improve the overall test scores of BERT-based model from 78.3 to 79.4, and RoBERTa-large model from 88.5 to 88.8. In addition, the proposed approach achieves state-of-the-art single-model test accuracies of 85.44% and 67.75% on ARC-Easy and ARC-Challenge. Experiments on Com-monsenseQA benchmark further demonstrate that FreeLB can be generalized and boost the performance of RoBERTa-large model on other tasks as well. | FREELB: ENHANCED ADVERSARIAL TRAINING FOR LANGUAGE UNDERSTANDING |
d16605700 | With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal(LeCun et al., 2015). However, most current research focuses instead on important but narrow applications, such as image classification or machine translation. We believe this to be largely due to the lack of objective ways to measure progress towards broad machine intelligence. In order to fill this gap, we propose here a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.DESIDERATA FOR THE EVALUATION OF MACHINE INTELLIGENCERather than trying to define intelligence in abstract terms, we take a pragmatic approach: we would like to develop AIs that are useful for us. This naturally leads to the following desiderata.Communication through natural language An AI will be useful to us only if we are able to communicate with it: assigning it tasks, understanding the information it returns, and teaching it new skills. Since natural language is by far the easiest way for us to communicate, we require our useful AI to be endowed with basic linguistic abilities. The language the machine is exposed to in the testing environment will inevitably be very limited. However, given that we want the machine to also be a powerful, fast learner (see next point), humans should later be able to teach it more sophisticated language skills as they become important to instruct the machine in new domains. In concrete, the environment should not only expose the machine to a set of tasks, but provide instructions and feedback about the tasks in simple natural language. The machine should rely on this form of linguistic interaction to efficiently solve the tasks. | COMMAI: EVALUATING THE FIRST STEPS TOWARDS A USEFUL GENERAL AI |
d233386821 | We present a principled approach to incorporating labels in variational autoencoders (VAEs) that captures the rich characteristic information associated with those labels. While prior work has typically conflated these by learning latent variables that directly correspond to label values, we argue this is contrary to the intended effect of supervision in VAEs-capturing rich label characteristics with the latents. For example, we may want to capture the characteristics of a face that make it look young, rather than just the age of the person. To this end, we develop the characteristic capturing VAE (CCVAE), a novel VAE model and concomitant variational objective which captures label characteristics explicitly in the latent space, eschewing direct correspondences between label values and latents. Through judicious structuring of mappings between such characteristic latents and labels, we show that the CCVAE can effectively learn meaningful representations of the characteristics of interest across a variety of supervision schemes. In particular, we show that the CCVAE allows for more effective and more general interventions to be performed, such as smooth traversals within the characteristics for a given label, diverse conditional generation, and transferring characteristics across datapoints 1 . * work done while at Oxford † equal contribution 1 Link to code: https://github.com/thwjoy/ccvae | Published as a conference paper at ICLR 2021 CAPTURING LABEL CHARACTERISTICS IN VAEs |
d247594436 | Recently, prefix-tuning has gained increasing attention as a parameter-efficient finetuning method for large-scale pretrained language models. The method keeps the pretrained models fixed and only updates the prefix token parameters for each downstream task. Despite being lightweight and modular, prefix-tuning still lacks robustness to textual adversarial attacks. However, most currently developed defense techniques necessitate auxiliary model update and storage, which inevitably hamper the modularity and low storage of prefix-tuning. In this work, we propose a robust prefix-tuning framework that preserves the efficiency and modularity of prefix-tuning. The core idea of our framework is leveraging the layerwise activations of the language model by correctly-classified training data as the standard for additional prefix finetuning. During the test phase, an extra batch-level prefix is tuned for each batch and added to the original prefix for robustness enhancement. Extensive experiments on three text classification benchmarks show that our framework substantially improves robustness over several strong baselines against five textual attacks of different types while maintaining comparable accuracy on clean texts. We also interpret our robust prefix-tuning framework from the optimal control perspective and pose several directions for future research 1 . | Published as a conference paper at ICLR 2022 ON ROBUST PREFIX-TUNING FOR TEXT CLASSIFICA- TION |
d212657453 | A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs). Such GCNs utilize isotropic kernels and are therefore insensitive to the relative orientation of vertices and thus to the geometry of the mesh as a whole. We propose Gauge Equivariant Mesh CNNs which generalize GCNs to apply anisotropic gauge equivariant kernels. Since the resulting features carry orientation information, we introduce a geometric message passing scheme defined by parallel transporting features over mesh edges. Our experiments validate the significantly improved expressivity of the proposed model over conventional GCNs and other methods. * Equal Contribution | GAUGE EQUIVARIANT MESH CNNS ANISOTROPIC CONVOLUTIONS ON GEOMETRIC GRAPHS |
d16134629 | We present an approach to sensorimotor control in immersive environments. Our approach utilizes a high-dimensional sensory stream and a lower-dimensional measurement stream. The cotemporal structure of these streams provides a rich supervisory signal, which enables training a sensorimotor control model by interacting with the environment. The model is trained using supervised learning techniques, but without extraneous supervision. It learns to act based on raw sensory input from a complex three-dimensional environment. The presented formulation enables learning without a fixed goal at training time, and pursuing dynamically changing goals at test time. We conduct extensive experiments in threedimensional simulations based on the classical first-person game Doom. The results demonstrate that the presented approach outperforms sophisticated prior formulations, particularly on challenging tasks. The results also show that trained models successfully generalize across environments and goals. A model trained using the presented approach won the Full Deathmatch track of the Visual Doom AI Competition, which was held in previously unseen environments.Published as a conference paper at ICLR 2017 vided by experience itself: by acting and observing the effects of different actions in the context of changing sensory inputs and goals. | Published as a conference paper at ICLR 2017 LEARNING TO ACT BY PREDICTING THE FUTURE |
d252668761 | We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AUDIOGEN, an auto-regressive generative model that generates audio samples conditioned on text inputs. AUDIOGEN operates on a learnt discrete audio representation. The task of text-to-audio generation poses multiple challenges. Due to the way audio travels through a medium, differentiating "objects" can be a difficult task (e.g., separating multiple people simultaneously speaking). This is further complicated by real-world recording conditions (e.g., background noise, reverberation, etc.). Scarce text annotations impose another constraint, limiting the ability to scale models. Finally, modeling high-fidelity audio requires encoding audio at high sampling rate, leading to extremely long sequences. To alleviate the aforementioned challenges we propose an augmentation technique that mixes different audio samples, driving the model to internally learn to separate multiple sources. We curated 10 datasets containing different types of audio and text annotations to handle the scarcity of text-audio data points. For faster inference, we explore the use of multi-stream modeling, allowing the use of shorter sequences while maintaining a similar bitrate and perceptual quality. We apply classifier-free guidance to improve adherence to text. Comparing to the evaluated baselines, AUDIOGEN outperforms over both objective and subjective metrics. Finally, we explore the ability of the proposed method to generate audio continuation conditionally and unconditionally. | Published as a conference paper at ICLR 2023 AUDIOGEN: TEXTUALLY GUIDED AUDIO GENERA- TION |
d236493649 | Unsupervised skill learning objectives (Eysenbach et al., 2019; Gregor et al., 2016) allow agents to learn rich repertoires of behavior in the absence of extrinsic rewards. They work by simultaneously training a policy to produce distinguishable latent-conditioned trajectories, and a discriminator to evaluate distinguishability by trying to infer latents from trajectories. The hope is for the agent to explore and master the environment by encouraging each skill (latent) to reliably reach different states. However, an inherent exploration problem lingers: when a novel state is actually encountered, the discriminator will necessarily not have seen enough training data to produce accurate and confident skill classifications, leading to low intrinsic reward for the agent and effective penalization of the sort of exploration needed to actually maximize the objective. To combat this inherent pessimism towards exploration, we derive an information gain auxiliary objective that involves training an ensemble of discriminators and rewarding the policy for their disagreement. Our objective directly estimates the epistemic uncertainty that comes from the discriminator not having seen enough training examples, thus providing an intrinsic reward more tailored to the true objective compared to pseudocount-based methods (Burda et al., 2019). We call this exploration bonus discriminator disagreement intrinsic reward, or DISDAIN. We demonstrate empirically that DISDAIN improves skill learning both in a tabular grid world (Four Rooms) and the 57 games of the Atari Suite (from pixels). Thus, we encourage researchers to treat pessimism with DISDAIN. * equal contribution Unifying count-based exploration and intrinsic motivation. In Neural Information Processing Systems (NIPS), 2016.Ronen I Brafman and Moshe Tennenholtz. R-max -a general polynomial time algorithm for nearoptimal reinforcement learning. | Published as a conference paper at ICLR 2022 LEARNING MORE SKILLS THROUGH OPTIMISTIC EXPLORATION |
d226236703 | Recent progress in deep learning has enabled many advances in sound separation and visual scene understanding. However, extracting sound sources which are apparent in natural videos remains an open problem. In this work, we present AudioScope, a novel audio-visual sound separation framework that can be trained without supervision to isolate on-screen sound sources from real in-the-wild videos. Prior audio-visual separation work assumed artificial limitations on the domain of sound classes (e.g., to speech or music), constrained the number of sources, and required strong sound separation or visual segmentation labels. AudioScope overcomes these limitations, operating on an open domain of sounds, with variable numbers of sources, and without labels or prior visual segmentation. The training procedure for AudioScope uses mixture invariant training (MixIT) to separate synthetic mixtures of mixtures (MoMs) into individual sources, where noisy labels for mixtures are provided by an unsupervised audio-visual coincidence model. Using the noisy labels, along with attention between video and audio features, AudioScope learns to identify audio-visual similarity and to suppress off-screen sounds. We demonstrate the effectiveness of our approach using a dataset of video clips extracted from open-domain YFCC100m video data. This dataset contains a wide diversity of sound classes recorded in unconstrained conditions, making the application of previous methods unsuitable. For evaluation and semi-supervised experiments, we collected human labels for presence of on-screen and off-screen sounds on a small subset of clips. | Published as a conference paper at ICLR 2021 INTO THE WILD WITH AUDIOSCOPE: UNSUPERVISED AUDIO-VISUAL SEPARATION OF ON-SCREEN SOUNDS |
d3531856 | We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters. We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramér GAN critic. Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs. In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance. We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training. * These authors contributed equally.Published as a conference paper at ICLR 2018 are infinite, meaning that they provide no useful gradient for the generator to follow. This helps to explain some of the instability of GAN training.The lack of sensitivity to distance, meaning that nearby but non-overlapping regions of high probability mass are not considered similar, is a long-recognized problem for KL divergence-based discrepancy measures (e.g. Gneiting & Raftery, 2007, Section 4.2). It is natural to address this problem using Integral Probability Metrics (IPMs; Müller, 1997): these measure the distance between probability measures via the largest discrepancy in expectation over a class of "well behaved" witness functions. Thus, IPMs are able to signal proximity in the probability mass of the generator and reference distributions. (Section 2 describes this framework in more detail.) proposed to use the Wasserstein distance between distributions as the discriminator, which is an integral probability metric constructed from the witness class of 1-Lipschitz functions. To implement the Wasserstein critic, Arjovsky et al. originally proposed weight clipping of the discriminator network, to enforce k-Lipschitz smoothness. Gulrajani et al. (2017) improved on this result by directly constraining the gradient of the discriminator network at points between the generator and reference samples. This new Wasserstein GAN implementation, called WGAN-GP, is more stable and easier to train.A second integral probability metric used in GAN variants is the maximum mean discrepancy (MMD), for which the witness function class is a unit ball in a reproducing kernel Hilbert space (RKHS). Generative adversarial models based on minimizing the MMD were first considered by Li et al. (2015) and Dziugaite et al. (2015). These works optimized a generator to minimize the MMD with a fixed kernel, either using a generic kernel on image pixels or by modeling autoencoder representations instead of images directly. Sutherland et al.(2017)instead minimized the statistical power of an MMD-based test with a fixed kernel. Such approaches struggle with complex natural images, where pixel distances are of little value, and fixed representations can easily be tricked, as in the adversarial examples of Szegedy et al. (2014). A. Krizhevsky. Learning multiple layers of features from tiny images, 2009. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. | Published as a conference paper at ICLR 2018 DEMYSTIFYING MMD GANS |
d231639398 | The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manner that assigns a uniform size to all features has two issues. First, the numerous features inevitably lead to a gigantic embedding table that causes a high memory usage cost. Second, it is likely to cause the over-fitting problem for those features that do not require too large representation capacity. Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost. In this paper, we proposed a novel approach, named PEP 1 (short for Plug-in Embedding Pruning), to reduce the size of the embedding table while obviating a drop in accuracy and computational optimization. PEP prunes embedding parameter where the pruning threshold(s) can be adaptively learned from data. Therefore we can automatically obtain a mixed-dimension embeddingscheme by pruning redundant parameters for each feature. PEP is a general framework that can plug in various base recommendation models. Extensive experiments demonstrate it can efficiently cut down embedding parameters and boost the base model's performance. Specifically, it achieves strong recommendation performance while reducing 97-99% parameters. As for the computation cost, PEP only brings an additional 20-30% time cost compared with base models. | Published as a conference paper at ICLR 2021 LEARNABLE EMBEDDING SIZES FOR RECOMMENDER SYSTEMS |
d231698861 | In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, implicitly or explicitly, always represented using a graph shift operator (GSO) with the most common choices being the adjacency, Laplacian matrices and their normalisations. In this paper, a novel parametrised GSO (PGSO) is proposed, where specific parameter values result in the most commonly used GSOs and message-passing operators in graph neural network (GNN) frameworks. The PGSO is suggested as a replacement of the standard GSOs that are used in state-of-the-art GNN architectures and the optimisation of the PGSO parameters is seamlessly included in the model training. It is proved that the PGSO has real eigenvalues and a set of real eigenvectors independent of the parameter values and spectral bounds on the PGSO are derived. PGSO parameters are shown to adapt to the sparsity of the graph structure in a study on stochastic blockmodel networks, where they are found to automatically replicate the GSO regularisation found in the literature. On several real-world datasets the accuracy of state-of-theart GNN architectures is improved by the inclusion of the PGSO in both nodeand graph-classification tasks. * Equal contribution.Published as a conference paper at ICLR 2021On different tasks and datasets, the choice between the different representations encoded by the different graph shift operator matrices has shown to be a consequential decision. Due to the past successful approaches that use different GSOs for different tasks and datasets, it is natural to assume that there is no single optimal representation for all scenarios. Finding an optimal representation of network data could contribute positively to a range of learning tasks such as node and graph classification or community detection. Fundamental to this search is an answer to Question 1. In addition, we pose the following second research question.Question 2: Can we learn such an optimal representation to encode graph structure in a numerically stable and computationally efficient way?The utilisation of a GSO as a topology representation is currently a hand-engineered choice of normalised variants of the adjacency matrix. Thus, the learnable representation of node interactions is transferred into either convolutional filters(Kipf & Welling, 2017;Hamilton et al., 2017a)or attention weights(Veličković et al., 2018), keeping the used GSO constant. In this work, we suggest a parametrisation of the GSO. Specific parameter values in our proposed parametrised (and differentiable) GSO result in the most commonly used GSOs, namely the adjacency, unnormalised Laplacian and both normalised Laplacian matrices, and GNN aggregation functions, e.g., the averaging and summation message passing operations. The beauty of this innovation is that it can be seamlessly included in both message passing and convolutional GNN architectures. Optimising the operator parameters will allow us to find answers to our two research questions. | Published as a conference paper at ICLR 2021 LEARNING PARAMETRISED GRAPH SHIFT OPERATORS |
d2868054 | We present flattened convolutional neural networks that are designed for fast feedforward execution. The redundancy of the parameters, especially weights of the convolutional filters in convolutional neural networks has been extensively studied and different heuristics have been proposed to construct a low rank basis of the filters after training. In this work, we train flattened networks that consist of consecutive sequence of one-dimensional filters across all directions in 3D space to obtain comparable performance as conventional convolutional networks. We tested flattened model on different datasets and found that the flattened layer can effectively substitute for the 3D filters without loss of accuracy. The flattened convolution pipelines provide around two times speed-up during feedforward pass compared to the baseline model due to the significant reduction of learning parameters. Furthermore, the proposed method does not require efforts in manual tuning or post processing once the model is trained.Recent success on fast implementation of convolutional neural networks (CNNs), and new techniques such as dropout enable researchers to train large networks that were not possible before. These large CNNs show great promise in visual and audio understanding which make them useful for applications in autonomous robots, security systems, mobile phones, automobiles and wearable supports. These applications require networks with high degree of accuracies, but also networks that can be executed in real-time. However, CNNs are computationally very expensive and require high performance servers or graphics processing units (GPUs).To accelerate forward and backward passes of CNNs, there has been extensive work for efficient implementation of CNNs on GPUs (Krizhevsky et al., 2012;Chetlur et al., 2014)and CPUs (Vanhoucke et al., 2011), including linear quantization of network weights and inputs. For mobile platforms, like smartphones, computation of these big networks is still demanding and takes place on off-site servers because of their limited computing power and battery life. However, that requires a necessity to a reliable connectivity between the mobile device and off-site servers. Because this is not the case always, custom architectures have been explored for power and speed efficient implementation of CNNs(Jin et al., 2014;Merolla et al., 2014).Another approach to speed up evaluation of CNNs is to reduce the number of parameters in the network representation. The work byDenil et al. (2013)is a good example to show that these networks have high redundancy in them. Considering that state of the art CNNs require hundreds of filters each layer and consist of three to five convolutional layers in general, finding essential representation with smaller parameters brings significant performance boost in terms of time and memory.Jaderberg et al. (2014);Denton et al. (2014)exploit the redundancy within convolutional layer after training and could obtain speedup by keeping the accuracy within 1% of the original models.In this work, we take a similar approach to decrease the redundancy of the filters in convolutional neural networks. We achieve that in the training phase by separating the conventional 3D convolution filters into three consecutive 1D filters: convolution across channels (lateral), vertical and 1 arXiv:1412.5474v2 [cs.NE] Under review as a conference paper at ICLR 2015 horizontal direction. We report similar or better accuracies on well-known datasets with the baseline network which has about ten times more parameters. | FLATTENED CONVOLUTIONAL NEURAL NETWORKS FOR FEEDFORWARD ACCELERATION |
d6666117 | We propose a unified framework for neural net normalization, regularization and optimization, which includes Path-SGD and Batch-Normalization and interpolates between them across two different dimensions. Through this framework we investigate the issue of invariance of the optimization, data dependence and the connection with natural gradients.One particular advantage of the approximate steepest descent approach (DDP-SGD) over the normalization approach is that it is invariant to weight rebalancing (discussed in Section 6). This is true regardless of the amount of data-dependence used. That is, it operates more directly on the model (the function defined by the weights) rather than the parametrization (the values of the weights themselves). This brings us to a more general discussion of parametrization invariance in feedforward networks (Section 7).Our unified framework and study of in invariances also allows us to relate the different optimization approaches to Natural Gradients(Amari, 1998). In particular, we show that DDP-SGD with full data-dependence can be seen as an efficient approximation of the natural gradient using only the diagonal of the Fisher information matrix (Section 5). | DATA-DEPENDENT PATH NORMALIZATION IN NEURAL NETWORKS |
d14498604 | Learning invariant representations from images is one of the hardest challenges facing computer vision. Spatial pooling is widely used to create invariance to spatial shifting, but it is restricted to convolutional models. In this paper, we propose a novel pooling method that can learn soft clustering of features from image sequences. It is trained to improve the temporal coherence of features, while keeping the information loss at minimum. Our method does not use spatial information, so it can be used with non-convolutional models too. Experiments on images extracted from natural videos showed that our method can cluster similar features together. When trained by convolutional features, auto-pooling outperformed traditional spatial pooling on an image classification task, even though it does not use the spatial topology of features. | Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences |
d22191393 | The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning. This model, however, was originally designed to be learned with the presence of both training and test data. Moreover, the recursive neighborhood expansion across layers poses time and memory challenges for training with large, dense graphs. To relax the requirement of simultaneous availability of test data, we interpret graph convolutions as integral transforms of embedding functions under probability measures. Such an interpretation allows for the use of Monte Carlo approaches to consistently estimate the integrals, which in turn leads to a batched training scheme as we propose in this work-FastGCN. Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference. We show a comprehensive set of experiments to demonstrate its effectiveness compared with GCN and related models. In particular, training is orders of magnitude more efficient while predictions remain comparably accurate. * These two authors contribute equally. | Published as a conference paper at ICLR 2018 FASTGCN: FAST LEARNING WITH GRAPH CONVOLU- TIONAL NETWORKS VIA IMPORTANCE SAMPLING |
d235266218 | Large-batch training has become a commonly used technique when training neural networks with a large number of GPU/TPU processors. As batch size increases, stochastic optimizers tend to converge to sharp local minima, leading to degraded test performance. Current methods usually use extensive data augmentation to increase the batch size, but we found the performance gain with data augmentation decreases as batch size increases, and data augmentation will become insufficient after certain point. In this paper, we propose to use adversarial learning to increase the batch size in large-batch training. Despite being a natural choice for smoothing the decision surface and biasing towards a flat region, adversarial learning has not been successfully applied in large-batch training since it requires at least two sequential gradient computations at each step, which will at least double the running time compared with vanilla training even with a large number of processors. To overcome this issue, we propose a novel Concurrent Adversarial Learning (ConAdv) method that decouple the sequential gradient computations in adversarial learning by utilizing staled parameters. Experimental results demonstrate that ConAdv can successfully increase the batch size on ResNet-50 training on ImageNet while maintaining high accuracy. In particular, we show ConAdv along can achieve 75.3% top-1 accuracy on ImageNet ResNet-50 training with 96K batch size, and the accuracy can be further improved to 76.2% when combining ConAdv with data augmentation. This is the first work successfully scales ResNet-50 training batch size to 96K. | CONCURRENT ADVERSARIAL LEARNING FOR LARGE- BATCH TRAINING |
d1996665 | Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification (also called "semantic image segmentation"). We show that responses at the final layer of DCNNs are not sufficiently localized for accurate object segmentation. This is due to the very invariance properties that make DCNNs good for high level tasks. We overcome this poor localization property of deep networks by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). Qualitatively, our "DeepLab" system is able to localize segment boundaries at a level of accuracy which is beyond previous methods. Quantitatively, our method sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 71.6% IOU accuracy in the test set. We show how these results can be obtained efficiently: Careful network re-purposing and a novel application of the 'hole' algorithm from the wavelet community allow dense computation of neural net responses at 8 frames per second on a modern GPU. | Published as a conference paper at ICLR 2015 SEMANTIC IMAGE SEGMENTATION WITH DEEP CON- VOLUTIONAL NETS AND FULLY CONNECTED CRFS |
d244729433 | Multi-view projection methods have demonstrated promising performance on 3D understanding tasks like 3D classification and segmentation. However, it remains unclear how to combine such multi-view methods with the widely available 3D point clouds. Previous methods use unlearned heuristics to combine features at the point level. To this end, we introduce the concept of the multi-view point cloud (Voint cloud), representing each 3D point as a set of features extracted from several view-points. This novel 3D Voint cloud representation combines the compactness of 3D point cloud representation with the natural view-awareness of multi-view representation. Naturally, we can equip this new representation with convolutional and pooling operations. We deploy a Voint neural network (VointNet) to learn representations in the Voint space. Our novel representation achieves state-of-the-artperformance on 3D classification, shape retrieval, and robust 3D part segmentation on standard benchmarks ( ScanObjectNN, ShapeNet Core55, and ShapeNet Parts). 1Figure 1: 3D Voint Clouds. We propose the multi-view point cloud (Voint cloud), a novel 3D representation that is compact and naturally descriptive of view projections of a 3D point cloud. Each point in the 3D cloud is tagged with a Voint, which accumulates view-features for that point. Note that not all 3D points are visible from all views. The set of Voints constructs a Voint cloud.clouds with a single feature per point suitable for typical point cloud processing pipelines. Previous multi-view works rely on heuristics (e.g. average or label mode pooling) after mapping pixels to points (Kundu et al., 2020;Wang et al., 2019a), or multi-view fusion with voxels (Dai & Nießner, 2018). Such setups might not be optimal for a few reasons. (i) Such heuristics may aggregate information of misleading projections that are obtained from arbitrary view-points. For example, looking at an object from the bottom and processing that view independently can carry wrong information about the object's content when combined with other views. (ii) The views lack geometric 3D information.To this end, we propose a new hybrid 3D data structure that inherits the merits of point clouds (i.e. compactness, flexibility, and 3D descriptiveness) and leverages the benefits of rich perceptual features of multi-view projections. We call this new representation multi-view point cloud (or Voint cloud) and illustrate it inFigure 1. A Voint cloud is a set of Voints, where each Voint is a set of view-dependent features (view-features) that correspond to the same point in the 3D point cloud. The cardinality of these view-features may differ from one Voint to another. InTable 1, we compare some of the widely used 3D representations and our Voint cloud representation. Voint clouds inherit the characteristics of the parent explicit 3D point clouds, which facilitates learning Voint representations for a variety of vision applications (e.g. point cloud classification and segmentation). To deploy deep learning on the new Voint space, we define basic operations on Voints, such as pooling and convolution. Based on these operations, we define a practical way of building Voint neural networks that we dub VointNet. VointNet takes a Voint cloud and outputs point cloud features for 3D point cloud processing. We show how learning this Voint cloud representation leads to strong performance and gained robustness for the tasks of 3D classification, 3D object retrieval, and 3D part segmentation on standard benchmarks like ScanObjectNN (Uy et al., 2019), and ShapeNet (Chang et al., 2015).Contributions: (i)We propose a novel multi-view 3D point cloud representation (denoted as Voint cloud), which represents each point (namely a Voint) as a set of features from different view-points. (ii) We define pooling and convolutional operations at the Voint level to construct a Voint Neural Network (VointNet) capable of learning to aggregate information from multiple views in the Voint space. (iii) Our VointNet reaches state-ofthe-artperformance on several 3D understanding tasks, including 3D shape classification, retrieval, and robust part segmentation. Further, VointNet achieves robustness improvement to occlusion and rotation. Center (VCC) funding.ReferencesGary Bradski and Stephen Grossberg. Recognition of 3-d objects from multiple 2-d views by a self-organizing neural architecture. In From Statistics . Encoder-decoder with atrous separable convolution for semantic image segmentation. In . 3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation. In | Published as a conference paper at ICLR 2023 Voint Cloud: Multi-View Point Cloud Rep- resentation for 3D Understanding |
d257254835 | Out-of-distribution (OOD) detection is a critical task for reliable machine learning. Recent advances in representation learning give rise to distance-based OOD detection, where testing samples are detected as OOD if they are relatively far away from the centroids or prototypes of in-distribution (ID) classes. However, prior methods directly take off-the-shelf contrastive losses that suffice for classifying ID samples, but are not optimally designed when test inputs contain OOD samples. In this work, we propose CIDER, a novel representation learning framework that exploits hyperspherical embeddings for OOD detection. CIDER jointly optimizes two losses to promote strong ID-OOD separability: a dispersion loss that promotes large angular distances among different class prototypes, and a compactness loss that encourages samples to be close to their class prototypes. We analyze and establish the unexplored relationship between OOD detection performance and the embedding properties in the hyperspherical space, and demonstrate the importance of dispersion and compactness. CIDER establishes superior performance, outperforming the latest rival by 13.33% in FPR95. Code is available at https://github.com/deeplearning-wisc/cider.Arguably, the efficacy of distance-based approaches can depend largely on the quality of feature embeddings. Recent works including SSD+ (Sehwag et al., 2021) and KNN+ (Sun et al., 2022) directly employ off-the-shelf contrastive losses for OOD detection. In particular, these works use the supervised contrastive loss (SupCon) (Khosla et al., 2020) for learning the embeddings, which are then used for OOD detection with either parametric Mahalanobis distance(Lee et al., 2018;Sehwag et al., 2021)or non-parametric KNN distance (Sun et al., 2022). However, existing training objectives produce embeddings that suffice for classifying ID samples, but remain sub-optimal for OOD detection. For example, when trained on CIFAR-10 using SupCon loss, the average angular distance between ID and OOD data is only 29.86 degrees in the embedding space, which is too small for effective ID-OOD separation. This raises the important question: arXiv:2203.04450v3 [cs.CV] | Published as a conference paper at ICLR 2023 HOW TO EXPLOIT HYPERSPHERICAL EMBEDDINGS FOR OUT-OF-DISTRIBUTION DETECTION? |
d208100714 | In this paper we delve deep in the Transformer architecture by investigating two of its core components: self-attention and contextual embeddings. In particular, we study the identifiability of attention weights and token embeddings, and the aggregation of context into hidden tokens. We show that, for sequences longer than the attention head dimension, attention weights are not identifiable. We propose effective attention as a complementary tool for improving explanatory interpretations based on attention. Furthermore, we show that input tokens retain to a large degree their identity across the model. We also find evidence suggesting that identity information is mainly encoded in the angle of the embeddings and gradually decreases with depth. Finally, we demonstrate strong mixing of input information in the generation of contextual embeddings by means of a novel quantification method based on gradient attribution. Overall, we show that self-attention distributions are not directly interpretable and present tools to better understand and further investigate Transformer models. * Equal contribution with authors in alphabetical order. Yang Liu initiated the transformer models study, perceived and performed the study of attention identifiability and effective attention, i.e., Section 3 and Appendix A, and contributed to the token attribution discussions and calculations. | Published as a conference paper at ICLR 2020 ON IDENTIFIABILITY IN TRANSFORMERS |
d257427168 | Recently, a few self-supervised representation learning (SSL) methods have outperformed the ImageNet classification pre-training for vision tasks such as object detection. However, its effects on 3D human body pose and shape estimation (3DHPSE) are open to question, whose target is fixed to a unique class, the human, and has an inherent task gap with SSL. We empirically study and analyze the effects of SSL and further compare it with other pre-training alternatives for 3DH-PSE. The alternatives are 2D annotation-based pre-training and synthetic data pretraining, which share the motivation of SSL that aims to reduce the labeling cost. They have been widely utilized as a source of weak-supervision or fine-tuning, but have not been remarked as a pre-training source. SSL methods underperform the conventional ImageNet classification pre-training on multiple 3DHPSE benchmarks by 7.7% on average. In contrast, despite a much less amount of pre-training data, the 2D annotation-based pre-training improves accuracy on all benchmarks and shows faster convergence during fine-tuning. Our observations challenge the naive application of the current SSL pre-training to 3DHPSE and relight the value of other data types in the pre-training aspect. | RETHINKING SELF-SUPERVISED VISUAL REPRESEN- TATION LEARNING IN PRE-TRAINING FOR 3D HUMAN POSE AND SHAPE ESTIMATION |
d3652033 | Which Generative Adversarial Networks (GANs) generates the most plausible images?" has been a frequently asked question among researchers. To address this problem, we first propose an incomplete U-statistics estimate of maximum mean discrepancy MMD inc to measure the distribution discrepancy between generated and real images. MMD inc enjoys the advantages of asymptotic normality, computation efficiency, and model agnosticity. We then propose a GANs analysis framework to select and test the "best" member in GANs family using the Post Selection Inference (PSI) with MMD inc . In the experiments, we adopt the proposed framework on 7 GANs variants and compare their MMD inc scores. * Equal contribution. Random author ordering. | Workshop track -ICLR 2018 SELECTING THE BEST IN GANS FAMILY: A POST SE- LECTION INFERENCE FRAMEWORK |
d256503666 | Probabilistic dynamics model ensemble is widely used in existing model-based reinforcement learning methods as it outperforms a single dynamics model in both asymptotic performance and sample efficiency. In this paper, we provide both practical and theoretical insights on the empirical success of the probabilistic dynamics model ensemble through the lens of Lipschitz continuity. We find that, for a value function, the stronger the Lipschitz condition is, the smaller the gap between the true dynamics-and learned dynamics-induced Bellman operators is, thus enabling the converged value function to be closer to the optimal value function. Hence, we hypothesize that the key functionality of the probabilistic dynamics model ensemble is to regularize the Lipschitz condition of the value function using generated samples. To test this hypothesis, we devise two practical robust training mechanisms through computing the adversarial noise and regularizing the value network's spectral norm to directly regularize the Lipschitz condition of the value functions. Empirical results show that combined with our mechanisms, model-based RL algorithms with a single dynamics model outperform those with an ensemble of probabilistic dynamics models. These findings not only support the theoretical insight, but also provide a practical solution for developing computationally efficient model-based RL algorithms. § Equal contribution Published as a conference paper at ICLR 2023 | IS MODEL ENSEMBLE NECESSARY? MODEL-BASED RL VIA A SINGLE MODEL WITH LIPSCHITZ REGULAR- IZED VALUE FUNCTION |
d238407870 | Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry. We introduce Steerable E(3) Equivariant Graph Neural Networks (SEGNNs) that generalise equivariant graph networks, such that node and edge attributes are not restricted to invariant scalars, but can contain covariant information, such as vectors or tensors. This model, composed of steerable MLPs, is able to incorporate geometric and physical information in both the message and update functions. Through the definition of steerable node attributes, the MLPs provide a new class of activation functions for general use with steerable feature fields. We discuss ours and related work through the lens of equivariant non-linear convolutions, which further allows us to pin-point the successful components of SEGNNs: non-linear message aggregation improves upon classic linear (steerable) point convolutions; steerable messages improve upon recent equivariant graph networks that send invariant messages. We demonstrate the effectiveness of our method on several tasks in computational physics and chemistry and provide extensive ablation studies. Duits. Roto-translation covariant convolutional networks for medical image analysis. In International conference on medical image computing and computer-assisted intervention, pp. 440-448. Springer, 2018.Lukas Biewald. Experiment tracking with weights and biases, 2020. . Open catalyst 2020 (oc20) dataset and community challenges. ACS Catalysis, 0(0):6059-6072, 2020. A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups. arXiv preprint arXiv:2104.09459, 2021. William T Freeman, Edward H Adelson, et al. The design and use of steerable filters. IEEE Transactions on Pattern analysis and machine intelligence, 13(9):891-906, 1991. (3)-transformers: 3d roto-translation equivariant attention networks. arXiv preprint arXiv:2006.10503, 2020. | Published as a conference paper at ICLR 2022 GEOMETRIC AND PHYSICAL QUANTITIES IMPROVE E(3) EQUIVARIANT MESSAGE PASSING |
d211126517 | We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB. This representation enables neural KB inference modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to model multi-hop inferences, and scalable enough to use with realistically large KBs. The sparse-matrix reified KB can be distributed across multiple GPUs, can scale to tens of millions of entities and facts, and is orders of magnitude faster than naive sparse-matrix implementations. The reified KB enables very simple end-to-end architectures to obtain competitive performance on several benchmarks representing two families of tasks: KB completion, and learning semantic parsers from denotations.Published as a conference paper at ICLR 2020x: an entity X: weighted set of entities x: vector encoding X NE: # entities in KB r: an relation R: weighted set of relations r: vector encoding R NR: # relations in KB Mr: matrix for r MR: weighted sum of Mr's, see Eq 1 follow(x, r): see Eq 2 NT : # triples in KB M subj , M obj , M rel : the reified KB, encoded as matrices mapping triple id to subject, object, and relation ids | Published as a conference paper at ICLR 2020 SCALABLE NEURAL METHODS FOR REASONING WITH A SYMBOLIC KNOWLEDGE BASE |
d3140413 | Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens. Skim-RNN gives computational advantage over an RNN that always updates the entire hidden state. Skim-RNN uses the same input and output interfaces as a standard RNN and can be easily used instead of RNNs in existing models. In our experiments, we show that Skim-RNN can achieve significantly reduced computational cost without losing accuracy compared to standard RNNs across five different natural language tasks. In addition, we demonstrate that the trade-off between accuracy and speed of Skim-RNN can be dynamically controlled during inference time in a stable manner. Our analysis also shows that Skim-RNN running on a single CPU offers lower latency compared to standard RNNs on GPUs. * Equal contribution. | Under review as a conference paper at ICLR 2018 NEURAL SPEED READING VIA SKIM-RNN |
d2401161 | One of the long-standing challenges in Artificial Intelligence for goal-directed behavior is to build a single agent which can solve multiple tasks. Recent progress in multi-task learning for goal-directed sequential tasks has been in the form of distillation based learning wherein a student network learns from multiple task-specific expert networks by mimicking the task-specific policies of the expert networks. While such approaches offer a promising solution to the multitask learning problem, they require supervision from large task-specific (expert) networks which require extensive training. We propose a simple yet efficient multi-task learning framework which solves multiple goal-directed tasks in an online or active learning setup without the need for expert supervision. | Online Multi-Task Learning Using Active Sampling |
d247026059 | In this paper, we study the problem of finding mixed Nash equilibrium for meanfield two-player zero-sum games. Solving this problem requires optimizing over two probability distributions. We consider a quasistatic Wasserstein gradient flow dynamics in which one probability distribution follows the Wasserstein gradient flow, while the other one is always at the equilibrium. Theoretical analysis are conducted on this dynamics, showing its convergence to the mixed Nash equilibrium under mild conditions. Inspired by the continuous dynamics of probability distributions, we derive a quasistatic Langevin gradient descent method with inner-outer iterations, and test the method on different problems, including training mixture of GANs. | Published as a conference paper at ICLR 2022 PROVABLY CONVERGENT QUASISTATIC DYNAMICS FOR MEAN-FIELD TWO-PLAYER ZERO-SUM GAMES |
d257255267 | The translational equivariant nature of Convolutional Neural Networks (CNNs) is a reason for its great success in computer vision. However, networks do not enjoy more general equivariance properties such as rotation or scaling, ultimately limiting their generalization performance. To address this limitation, we devise a method that endows CNNs with simultaneous equivariance with respect to translation, rotation, and scaling. Our approach defines a convolution-like operation and ensures equivariance based on our proposed scalable Fourier-Argand representation. The method maintains similar efficiency as a traditional network and hardly introduces any additional learnable parameters, since it does not face the computational issue that often occurs in group-convolution operators. We validate the efficacy of our approach in the image classification task, demonstrating its robustness and the generalization ability to both scaled and rotated inputs. | Published as a conference paper at ICLR 2023 EMPOWERING NETWORKS WITH SCALE AND ROTA- TION EQUIVARIANCE USING A SIMILARITY CONVO- LUTION |
d254043800 | Neural sequence models, especially transformers, exhibit a remarkable capacity for in-context learning. They can construct new predictors from sequences of labeled examples (x, f (x)) presented in the input without further parameter updates. We investigate the hypothesis that transformer-based in-context learners implement standard learning algorithms implicitly, by encoding smaller models in their activations, and updating these implicit models as new examples appear in the context. Using linear regression as a prototypical problem, we offer three sources of evidence for this hypothesis. First, we prove by construction that transformers can implement learning algorithms for linear models based on gradient descent and closed-form ridge regression. Second, we show that trained in-context learners closely match the predictors computed by gradient descent, ridge regression, and exact least-squares regression, transitioning between different predictors as transformer depth and dataset noise vary, and converging to Bayesian estimators for large widths and depths. Third, we present preliminary evidence that in-context learners share algorithmic features with these predictors: learners' late layers non-linearly encode weight vectors and moment matrices. These results suggest that in-context learning is understandable in algorithmic terms, and that (at least in the linear case) learners may rediscover standard estimation algorithms. | Published as a conference paper at ICLR 2023 WHAT LEARNING ALGORITHM IS IN-CONTEXT LEARN- ING? INVESTIGATIONS WITH LINEAR MODELS |
d246411377 | Finding multiple solutions of non-convex optimization problems is a ubiquitous yet challenging task. Most past algorithms either apply single-solution optimization methods from multiple random initial guesses or search in the vicinity of found solutions using ad hoc heuristics. We present an end-to-end method to learn the proximal operator of a family of training problems so that multiple local minima can be quickly obtained from initial guesses by iterating the learned operator, emulating the proximal-point algorithm that has fast convergence. The learned proximal operator can be further generalized to recover multiple optima for unseen problems at test time, enabling applications such as object detection. The key ingredient in our formulation is a proximal regularization term, which elevates the convexity of our training loss: by applying recent theoretical results, we show that for weakly-convex objectives with Lipschitz gradients, training of the proximal operator converges globally with a practical degree of over-parameterization. We further present an exhaustive benchmark for multi-solution optimization to demonstrate the effectiveness of our method. | Published as a conference paper at ICLR 2023 LEARNING PROXIMAL OPERATORS TO DISCOVER MULTIPLE OPTIMA |
d211133088 | Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and time-consuming. Second, realistic segmentation datasets are highly unbalanced: some categories are much more abundant than others, biasing the performance to the most represented ones. In this paper, we are interested in focusing human labelling effort on a small subset of a larger pool of data, minimizing this effort while maximizing performance of a segmentation model on a hold-out set. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). An agent learns a policy to select a subset of small informative image regions -opposed to entire images -to be labeled, from a pool of unlabeled data. The region selection decision is made based on predictions and uncertainties of the segmentation model being trained. Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems. We test the proof of concept in CamVid and provide results in the large-scale dataset Cityscapes. On Cityscapes, our deep RL region-based DQN approach requires roughly 30% less additional labeled data than our most competitive baseline to reach the same performance. Moreover, we find that our method asks for more labels of under-represented categories compared to the baselines, improving their performance and helping to mitigate class imbalance. * Work done while interning at ElementAI . The cityscapes dataset for semantic urban scene understanding. In CVPR, 2016.Ido Dagan and Sean P. Engelson. Committee-based sampling for training probabilistic classifiers. In ICML, 1995. | Published as a conference paper at ICLR 2020 REINFORCED ACTIVE LEARNING FOR IMAGE SEGMENTATION |
d239009955 | Deep Generative Networks (DGNs) are extensively employed in Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and their variants to approximate the data manifold and distribution. However, training samples are often distributed in a non-uniform fashion on the manifold, due to costs or convenience of collection. For example, the CelebA dataset contains a large fraction of smiling faces. These inconsistencies will be reproduced when sampling from the trained DGN, which is not always preferred, e.g., for fairness or data augmentation. In response, we develop MaGNET, a novel and theoretically motivated latent space sampler for any pre-trained DGN, that produces samples uniformly distributed on the learned manifold. We perform a range of experiments on various datasets and DGNs, e.g., for the state-of-the-art StyleGAN2 trained on FFHQ dataset, uniform sampling via MaGNET increases distribution precision and recall by 4.1% & 3.0% and decreases gender bias by 41.2%, without requiring labels or retraining. As uniform distribution does not imply uniform semantic distribution, we also explore separately how semantic attributes of generated samples vary under MaGNET sampling. Figure 1: Random batches of StyleGAN2 (ψ = 0.5) samples with 1024 × 1024 resolution, generated using standard sampling (left), uniform sampling via MaGNET on the learned pixel-space manifold (middle), and uniform sampling on the style-space manifold (right) of the same model. MaGNET sampling yields a higher number of young faces, better gender balance, and greater background/accessory variation, without the need for labels or retraining. Images are sorted by gender-age and color coded red-green (female-male) according to Microsoft Cognitive API predictions. Larger batches of images and attribute distributions are furnished in Appendix E. | MaGNET : UNIFORM SAMPLING FROM DEEP GENERA- TIVE NETWORK MANIFOLDS WITHOUT RETRAINING |
d247154647 | This paper addresses the unsupervised learning of content-style decomposed representation. We first give a definition of style and then model the content-style representation as a token-level bipartite graph. An unsupervised framework, named Retriever, is proposed to learn such representations. First, a cross-attention module is employed to retrieve permutation invariant (P.I.) information, defined as style, from the input data. Second, a vector quantization (VQ) module is used, together with man-induced constraints, to produce interpretable content tokens. Last, an innovative link attention module serves as the decoder to reconstruct data from the decomposed content and style, with the help of the linking keys. Being modal-agnostic, the proposed Retriever is evaluated in both speech and image domains. The state-of-the-art zero-shot voice conversion performance confirms the disentangling ability of our framework. Top performance is also achieved in the part discovery task for images, verifying the interpretability of our representation. In addition, the vivid part-based style transfer quality demonstrates the potential of Retriever to support various fascinating generative tasks. Project | Technology of China, 2 HKUST, 3 Microsoft Research Asia |
d256194286 | Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, due to their high latency, the deployment of DNNs hinges on the development of compression techniques such as quantization which consists in lowering the number of bits used to encode the weights and activations. Growing concerns for privacy and security have motivated the development of data-free techniques, at the expanse of accuracy. In this paper, we identity the uniformity of the quantization operator as a limitation of existing approaches, and propose a data-free non-uniform method. More specifically, we argue that to be readily usable without dedicated hardware and implementation, non-uniform quantization shall not change the nature of the mathematical operations performed by the DNN. This leads to search among the continuous automorphisms of (R * + , ×), which boils down to the power functions defined by their exponent. To find this parameter, we propose to optimize the reconstruction error of each layer: in particular, we show that this procedure is locally convex and admits a unique solution. At inference time, we show that our approach, dubbed PowerQuant, only require simple modifications in the quantized DNN activation functions. As such, with only negligible overhead, it significantly outperforms existing methods in a variety of configurations. arXiv:2301.09858v1 [cs.CV] 24 Jan 2023 Arxiv version W8/A8 W6/A6 W4/A4 PowerQuant SPIQ SQuant OCS DFQ 80 70 60 50 40 | Arxiv version POWERQUANT: AUTOMORPHISM SEARCH FOR NON- UNIFORM QUANTIZATION |
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