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d252519395 | The universal approximation property (UAP) of neural networks is fundamental for deep learning, and it is well known that wide neural networks are universal approximators of continuous functions within both the L p norm and the continuous/uniform norm. However, the exact minimum width, w min , for the UAP has not been studied thoroughly. Recently, using a decoder-memorizer-encoder scheme, Park et al.(2021)found that w min = max(d x + 1, d y ) for both the L p -UAP of ReLU networks and the C-UAP of ReLU+STEP networks, where d x , d y are the input and output dimensions, respectively. In this paper, we consider neural networks with an arbitrary set of activation functions. We prove that both C-UAP and L p -UAP for functions on compact domains share a universal lower bound of the minimal width; that is, w * min = max(d x , d y ). In particular, the critical width, w * min , for L p -UAP can be achieved by leaky-ReLU networks, provided that the input or output dimension is larger than one. Our construction is based on the approximation power of neural ordinary differential equations and the ability to approximate flow maps by neural networks. The nonmonotone or discontinuous activation functions case and the one-dimensional case are also discussed.Published as a conference paper at ICLR 2023 2, d y + 1). A summary of known upper/lower bounds on minimum width for the UAP can be found in Park et al. (2021). | Achieve the Minimum Width of Neural Net- works for Universal Approximation |
d4167933 | Large CNNs have delivered impressive performance in various computer vision applications. But the storage and computation requirements make it problematic for deploying these models on mobile devices. Recently, tensor decompositions have been used for speeding up CNNs. In this paper, we further develop the tensor decomposition technique. We propose a new algorithm for computing the low-rank tensor decomposition for removing the redundancy in the convolution kernels. The algorithm finds the exact global optimizer of the decomposition and is more effective than iterative methods. Based on the decomposition, we further propose a new method for training low-rank constrained CNNs from scratch. Interestingly, while achieving a significant speedup, sometimes the lowrank constrained CNNs delivers significantly better performance than their nonconstrained counterparts. On the CIFAR-10 dataset, the proposed low-rank NIN model achieves 91.31% accuracy (without data augmentation), which also improves upon state-of-the-art result. We evaluated the proposed method on CIFAR-10 and ILSVRC12 datasets for a variety of modern CNNs, including AlexNet, NIN, VGG and GoogleNet with success. For example, the forward time of VGG-16 is reduced by half while the performance is still comparable. Empirical success suggests that low-rank tensor decompositions can be a very useful tool for speeding up large CNNs. | CONVOLUTIONAL NEURAL NETWORKS WITH LOW- RANK REGULARIZATION |
d17991431 | Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief (Dean et al., 2012) implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over 96% accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-theart, achieving 97.84% accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over 90% accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to transcribing synthetic distorted text from a popular CAPTCHA service, reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text as one of the cues to distinguish humans from bots. With the proposed approach we report a 99.8% accuracy on transcribing the hardest category of reCAPTCHA puzzles. Our evaluations on both tasks, the street number recognition as well as reCAPTCHA puzzle transcription, indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators. | Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks |
d4535830 | Deep neural networks (DNNs) had great success on NLP tasks such as language modeling, machine translation and certain question answering (QA) tasks. However, the success is limited at more knowledge intensive tasks such as QA from a big corpus. Existing end-to-end deep QA models(Miller et al., 2016;Weston et al., 2014)need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size. This is prohibitive for practical tasks such as QA from Wikipedia, a novel, or the Web. We propose to solve this scalability issue by using symbolic meaning representations, which can be indexed and retrieved efficiently with complexity that is independent of the text size. More specifically, we use sequence-to-sequence models to encode knowledge symbolically and generate programs to answer questions from the encoded knowledge. We apply our approach, called the N-Gram Machine (NGM), to the bAbI tasks (Weston et al., 2015) and a special version of them ("life-long bAbI") which has stories of up to 10 million sentences. Our experiments show that NGM can successfully solve both of these tasks accurately and efficiently. Unlike fully differentiable memory models, NGM's time complexity and answering quality are not affected by the story length. The whole system of NGM is trained end-to-end with REINFORCE (Williams, 1992). To avoid high variance in gradient estimation, which is typical in discrete latent variable models, we use beam search instead of sampling. To tackle the exponentially large search space, we use a stabilized auto-encoding objective and a structure tweak procedure to iteratively reduce and refine the search space.Under review as a conference paper at ICLR 2018 stories of up to 10 million lines long(Figure 4). The system we present here illustrates how end-to-end learning and scalability can be made possible through a symbolic knowledge storage. | Under review as a conference paper at ICLR 2018 LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES |
d205514 | Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some degree, its content and dynamics. This is why pixel-space video prediction may be viewed as a promising avenue for unsupervised feature learning. In addition, while optical flow has been a very studied problem in computer vision for a long time, future frame prediction is rarely approached. Still, many vision applications could benefit from the knowledge of the next frames of videos, that does not require the complexity of tracking every pixel trajectory. In this work, we train a convolutional network to generate future frames given an input sequence. To deal with the inherently blurry predictions obtained from the standard Mean Squared Error (MSE) loss function, we propose three different and complementary feature learning strategies: a multi-scale architecture, an adversarial training method, and an image gradient difference loss function. We compare our predictions to different published results based on recurrent neural networks on the UCF101 dataset. | DEEP MULTI-SCALE VIDEO PREDICTION BEYOND MEAN SQUARE ERROR |
d257102959 | kNN- MT (Khandelwal et al., 2021) is a straightforward yet powerful approach for fast domain adaptation, which directly plugs pre-trained neural machine translation (NMT) models with domain-specific token-level k-nearest-neighbor (kNN) retrieval to achieve domain adaptation without retraining. Despite being conceptually attractive, kNN-MT is burdened with massive storage requirements and high computational complexity since it conducts nearest neighbor searches over the entire reference corpus. In this paper, we propose a simple and scalable nearest neighbor machine translation framework to drastically promote the decoding and storage efficiency of kNN-based models while maintaining the translation performance. To this end, we dynamically construct an extremely small datastore for each input via sentence-level retrieval to avoid searching the entire datastore in vanilla kNN-MT, based on which we further introduce a distance-aware adapter to adaptively incorporate the kNN retrieval results into the pre-trained NMT models. Experiments on machine translation in two general settings, static domain adaptation, and online learning, demonstrate that our proposed approach not only achieves almost 90% speed as the NMT model without performance degradation, but also significantly reduces the storage requirements of kNN-MT. | Published as a conference paper at ICLR 2023 SIMPLE AND SCALABLE NEAREST NEIGHBOR MA- CHINE TRANSLATION |
d238408095 | Structural locality is a ubiquitous feature of real-world datasets, wherein data points are organized into local hierarchies. Some examples include topical clusters in text or project hierarchies in source code repositories. In this paper, we explore utilizing this structural locality within non-parametric language models, which generate sequences that reference retrieved examples from an external source. We propose a simple yet effective approach for adding locality information into such models by adding learned parameters that improve the likelihood of retrieving examples from local neighborhoods. Experiments on two different domains, Java source code and Wikipedia text, demonstrate that locality features improve model efficacy over models without access to these features, with interesting differences. We also perform an analysis of how and where locality features contribute to improved performance and why the traditionally used contextual similarity metrics alone are not enough to grasp the locality structure. | Published as a conference paper at ICLR 2022 CAPTURING STRUCTURAL LOCALITY IN NON-PARAMETRIC LANGUAGE MODELS |
d202888986 | Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems, we present two parameterreduction techniques to lower memory consumption and increase the training speed of BERT (Devlin et al., 2019). Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and SQuAD benchmarks while having fewer parameters compared to BERT-large. The code and the pretrained models are available at https://github.com/google-research/ALBERT.Published as a conference paper at ICLR 2020 These solutions address the memory limitation problem, but not the communication overhead. In this paper, we address all of the aforementioned problems, by designing A Lite BERT (ALBERT) architecture that has significantly fewer parameters than a traditional BERT architecture.ALBERT incorporates two parameter reduction techniques that lift the major obstacles in scaling pre-trained models. The first one is a factorized embedding parameterization. By decomposing the large vocabulary embedding matrix into two small matrices, we separate the size of the hidden layers from the size of vocabulary embedding. This separation makes it easier to grow the hidden size without significantly increasing the parameter size of the vocabulary embeddings. The second technique is cross-layer parameter sharing. This technique prevents the parameter from growing with the depth of the network. Both techniques significantly reduce the number of parameters for BERT without seriously hurting performance, thus improving parameter-efficiency. An ALBERT configuration similar to BERT-large has 18x fewer parameters and can be trained about 1.7x faster. The parameter reduction techniques also act as a form of regularization that stabilizes the training and helps with generalization.To further improve the performance of ALBERT, we also introduce a self-supervised loss for sentence-order prediction (SOP). SOP primary focuses on inter-sentence coherence and is designed to address the ineffectiveness of the next sentence prediction (NSP) loss proposed in the original BERT.As a result of these design decisions, we are able to scale up to much larger ALBERT configurations that still have fewer parameters than BERT-large but achieve significantly better performance. We establish new state-of-the-art results on the well-known GLUE, SQuAD, and RACE benchmarks for natural language understanding. Specifically, we push the RACE accuracy to 89.4%, the GLUE benchmark to 89.4, and the F1 score of SQuAD 2.0 to 92.2. | Published as a conference paper at ICLR 2020 ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS |
d248987351 | Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the change. The advancement of this area is challenged by: 1) characterizing data distribution drift and its impacts on models, 2) expressiveness in tracking the model dynamics, and 3) theoretical guarantee on the performance. To address them, we propose a Temporal Domain Generalization with Drift-Aware Dynamic Neural Network (DRAIN) framework. Specifically, we formulate the problem into a Bayesian framework that jointly models the relation between data and model dynamics. We then build a recurrent graph generation scenario to characterize the dynamic graph-structured neural networks learned across different time points. It captures the temporal drift of model parameters and data distributions and can predict models in the future without the presence of future data. In addition, we explore theoretical guarantees of the model performance under the challenging temporal DG setting and provide theoretical analysis, including uncertainty and generalization error. Finally, extensive experiments on several real-world benchmarks with temporal drift demonstrate the proposed method's effectiveness and efficiency. * Equal contribution.Published as a conference paper at ICLR 2023 Figure 1: An illustrative example of temporal domain generalization. Consider training a model for some classification tasks based on the annual Twitter dataset such that the trained model can generalize to the future domains (e.g., 2023). The temporal drift of data distribution can influence the prediction model such as the rotation of the decision boundary in this case. correspondingly to counter the trend of data distribution shifting across time. It can also "predict" what the model parameters should look like in an arbitrary (not too far) future time point. This requires the power of temporal domain generalization. However, as an extension of traditional DG, temporal DG is extremely challenging yet promising. Existing DG methods that treat the domain indices as a categorical variable may not be suitable for temporal DG as they require the domain boundary as apriori to learn the mapping from source to target domains (Muandet et al., 2013; Motiian et al., 2017; Balaji et al., 2018; Arjovsky et al., 2019). Until now, temporal domain indices have been well explored only in DA (Hoffman et al., 2014; Ortiz-Jimenez et al., 2019; Wang et al., 2020) but not DG. There are very few existing works in temporal DG due to its big challenges. One relevant work is Sequential Learning Domain Generalization (S-MLDG) (Li et al., 2020) that proposed a DG framework over sequential domains via meta-learning (Finn et al., 2017). S-MLDG meta-trains the target model on all possible permutations of source domains, with one source domain left for meta-test. However, S-MLDG in fact still treats domain index as a categorical variable, and the method was only tested on categorical DG dataset. A more recent paper called Gradient Interpolation (GI) (Nasery et al., 2021) proposes a temporal DG algorithm to encourage a model to learn functions that can extrapolate to the near future by supervising the first-order Taylor expansion of the learned function. However, GI has very limited power in characterizing model dynamics because it can only learn how the activation function changes along time while making all the remaining parameters fixed across time. | Published as a conference paper at ICLR 2023 TEMPORAL DOMAIN GENERALIZATION WITH DRIFT- AWARE DYNAMIC NEURAL NETWORKS |
d211126807 | Distances are pervasive in machine learning. They serve as similarity measures, loss functions, and learning targets; it is said that a good distance measure solves a task. When defining distances, the triangle inequality has proven to be a useful constraint, both theoretically-to prove convergence and optimality guaranteesand empirically-as an inductive bias. Deep metric learning architectures that respect the triangle inequality rely, almost exclusively, on Euclidean distance in the latent space. Though effective, this fails to model two broad classes of subadditive distances, common in graphs and reinforcement learning: asymmetric metrics, and metrics that cannot be embedded into Euclidean space. To address these problems, we introduce novel architectures that are guaranteed to satisfy the triangle inequality. We prove our architectures universally approximate norm-induced metrics on R n , and present a similar result for modified Input Convex Neural Networks. We show that our architectures outperform existing metric approaches when modeling graph distances and have a better inductive bias than non-metric approaches when training data is limited in the multi-goal reinforcement learning setting.Published as a conference paper at ICLR 2020M3 (Subadditivity). d(x, z) ≤ d(x, y) + d(y, z). M4 (Symmetry). d(x, y) = d(y, x).Since we care mostly about the triangle inequality (M3), we relax other axioms and define a quasimetric as a function that is M1 and M3, but not necessarily M2 or M4. Given weighted graph G = (V, E) with non-negative weights, shortest path lengths define a quasi-metric between vertices.When X is a vector space (we assume over R), many common metrics, e.g., Euclidean and Manhattan distances, are induced by a norm. A norm is a function · : X → R satisfying, ∀x, y ∈ X , α ∈ R + :An asymmetric norm is N1-N3, but not necessarily N4. An (asymmetric) semi-norm is nonnegative, N2 and N3 (and N4), but not necessarily N1. We will use the fact that any asymmetric semi-norm · induces a quasi-metric using the rule, d(x, y) = x − y , and first construct models of asymmetric semi-norms. Any induced quasi-metric d is translation invariant-d(x, y) = d(x + z, y + z)-and positive homogeneous-d(αx, αy) = αd(x, y) for α ≥ 0. If · is symmetric (N4), so is d (M4). If · is N1, d is M2. Metrics that are not translation invariant (e.g., Bi et al. (2015)) or positive homogeneous (e.g., our Neural Metrics in Section 3) cannot be induced by a norm.A convex function f : X → R is a function satisfying C1: ∀x, y ∈ X , α ∈ [0, 1]: f (αx+(1−α)y) ≤ αf (x) + (1 − α)f (y). The commonly used ReLU activation, relu(x) = max(0, x), is convex.DEEP NORMSIt is easy to see that any N2 and N3 function is convex-thus, all asymmetric semi-norms are convex. This motivates modeling norms as constrained convex functions, using the following proposition.Proposition 1. All positive homogeneous convex functions are subadditive; i.e., C1 ∧ N2 ⇒ N3. | Published as a conference paper at ICLR 2020 AN INDUCTIVE BIAS FOR DISTANCES: NEURAL NETS THAT RESPECT THE TRIANGLE INEQUALITY |
d17732879 | Deep learning embeddings have been successfully used for many natural language processing problems. Embeddings are mostly computed for word forms although a number of recent papers have extended this to other linguistic units like morphemes and phrases. In this paper, we argue that learning embeddings for discontinuous linguistic units should also be considered. In an experimental evaluation on coreference resolution, we show that such embeddings perform better than word form embeddings. | Deep Learning Embeddings for Discontinuous Linguistic Units |
d210902499 | A weakly supervised learning based clustering framework is proposed in this paper. As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count (ucc), which is the number of unique classes among all instances inside the bag. In this task, no annotations on individual instances inside the bag are needed during training of the models. We mathematically prove that with a perfect ucc classifier, perfect clustering of individual instances inside the bags is possible even when no annotations on individual instances are given during training. We have constructed a neural network based ucc classifier and experimentally shown that the clustering performance of our framework with our weakly supervised ucc classifier is comparable to that of fully supervised learning models where labels for all instances are known. Furthermore, we have tested the applicability of our framework to a real world task of semantic segmentation of breast cancer metastases in histological lymph node sections and shown that the performance of our weakly supervised framework is comparable to the performance of a fully supervised Unet model. | Published as a conference paper at ICLR 2020 WEAKLY SUPERVISED CLUSTERING BY EXPLOITING UNIQUE CLASS COUNT |
d211092828 | We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work together to achieve a goal they could not individually. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own. Thus, we propose to incentivize agents to take (joint) actions whose effects cannot be predicted via a composition of the predicted effect for each individual agent. We study two instantiations of this idea, one based on the true states encountered, and another based on a dynamics model trained concurrently with the policy. While the former is simpler, the latter has the benefit of being analytically differentiable with respect to the action taken. We validate our approach in robotic bimanual manipulation and multi-agent locomotion tasks with sparse rewards; we find that our approach yields more efficient learning than both 1) training with only the sparse reward and 2) using the typical surprise-based formulation of intrinsic motivation, which does not bias toward synergistic behavior. Videos are available on the project webpage: https://sites.google.com/view/iclr2020-synergistic. * Work done during an internship at Facebook AI Research. | Published as a conference paper at ICLR 2020 INTRINSIC MOTIVATION FOR ENCOURAGING SYNERGISTIC BEHAVIOR |
d11492613 | Mixtures of Experts combine the outputs of several "expert" networks, each of which specializes in a different part of the input space. This is achieved by training a "gating" network that maps each input to a distribution over the experts. Such models show promise for building larger networks that are still cheap to compute at test time, and more parallelizable at training time. In this this work, we extend the Mixture of Experts to a stacked model, the Deep Mixture of Experts, with multiple sets of gating and experts. This exponentially increases the number of effective experts by associating each input with a combination of experts at each layer, yet maintains a modest model size. On a randomly translated version of the MNIST dataset, we find that the Deep Mixture of Experts automatically learns to develop location-dependent ("where") experts at the first layer, and class-specific ("what") experts at the second layer. In addition, we see that the different combinations are in use when the model is applied to a dataset of speech monophones. These demonstrate effective use of all expert combinations. * Marc'Aurelio Ranzato currently works at the Facebook AI Group. | Learning Factored Representations in a Deep Mixture of Experts |
d14016036 | We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics. To this end, we employ the Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a distributional objective on raw text and a relational objective on WordNet. Preliminary results on knowledge base completion, analogy tests, and parsing show that word representations trained on both objectives can give improvements in some cases. | INCORPORATING BOTH DISTRIBUTIONAL AND RELA- TIONAL SEMANTICS IN WORD REPRESENTATIONS |
d11758569 | In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks -demonstrating their applicability as general image representations.arXiv:1511.06434v2 [cs.LG] 7 Jan 2016Under review as a conference paper at ICLR 2016• We show that the generators have interesting vector arithmetic properties allowing for easy manipulation of many semantic qualities of generated samples. | UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS |
d255186085 | Video representation learning has been successful in video-text pre-training for zero-shot transfer, where each sentence is trained to be close to the paired video clips in a common feature space. For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly. However, such unit-level comparison may ignore global temporal context, which inevitably limits the generalization ability. In this paper, we propose a contrastive learning framework TempCLR to compare the full video and the paragraph explicitly. As the video/paragraph is formulated as a sequence of clips/sentences, under the constraint of their temporal order, we use dynamic time warping to compute the minimum cumulative cost over sentence-clip pairs as the sequence-level distance. To explore the temporal dynamics, we break the consistency of temporal succession by shuffling video clips w.r.t. temporal granularity. Then, we obtain the representations for clips/sentences, which perceive the temporal information and thus facilitate the sequence alignment. In addition to pre-training on the video and paragraph, our approach can also generalize on the matching between video instances. We evaluate our approach on video retrieval, action step localization, and few-shot action recognition, and achieve consistent performance gain over all three tasks. Detailed ablation studies are provided to justify the approach design. † Equal contribution. Code Link: https://github.com/yyuncong/TempCLR | TEMPCLR: TEMPORAL ALIGNMENT REPRESENTA- TION WITH CONTRASTIVE LEARNING |
d27716347 | The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness. In this paper, we provide a theoretical justification for converting robustness analysis into a local Lipschitz constant estimation problem, and propose to use the Extreme Value Theory for efficient evaluation. Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness. The proposed CLEVER score is attack-agnostic and computationally feasible for large neural networks. Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that (i) CLEVER is aligned with the robustness indication measured by the 2 and ∞ norms of adversarial examples from powerful attacks, and (ii) defended networks using defensive distillation or bounded ReLU indeed achieve better CLEVER scores. To the best of our knowledge, CLEVER is the first attack-independent robustness metric that can be applied to any neural | EVALUATING THE ROBUSTNESS OF NEURAL NET- WORKS: AN EXTREME VALUE THEORY APPROACH |
d248887665 | Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, that is, how to design proper pretext tasks for vision dictionary look-up. MIM recently dominates this line of research with state-of-theart performance on vision Transformers (ViTs), where the core is to enhance the patch-level visual context capturing of the network via denoising auto-encoding mechanism. Rather than tailoring image tokenizers with extra training stages as in previous works, we unleash the great potential of contrastive learning on denoising auto-encoding and introduce a pure MIM method, ConMIM, to produce simple intra-image inter-patch contrastive constraints as the sole learning objectives for masked patch prediction. We further strengthen the denoising mechanism with asymmetric designs, including image perturbations and model progress rates, to improve the network pre-training. ConMIM-pretrained models with various scales achieve competitive results on downstream image classification, semantic segmentation, object detection, and instance segmentation tasks, e.g., on ImageNet-1K classification, we achieve 83.9% top-1 accuracy with ViT-Small and 85.3% with ViT-Base without extra data for pre-training. Code will be available at https://github.com/TencentARC/ConMIM. | Published as a conference paper at ICLR 2023 MASKED IMAGE MODELING WITH DENOISING CONTRAST |
d56895485 | We consider the problem of training speech recognition systems without using any labeled data, under the assumption that the learner can only access to the input utterances and a phoneme language model estimated from a non-overlapping corpus. We propose a fully unsupervised learning algorithm that alternates between solving two sub-problems: (i) learn a phoneme classifier for a given set of phoneme segmentation boundaries, and (ii) refining the phoneme boundaries based on a given classifier. To solve the first sub-problem, we introduce a novel unsupervised cost function named Segmental Empirical Output Distribution Matching, which generalizes the work in(Liu et al., 2017)to segmental structures. For the second sub-problem, we develop an approximate MAP approach to refining the boundaries obtained from Wang et al.(2017). Experimental results on TIMIT dataset demonstrate the success of this fully unsupervised phoneme recognition system, which achieves a phone error rate (PER) of 41.6%. Although it is still far away from the state-of-the-art supervised systems, we show that with oracle boundaries and matching language model, the PER could be improved to 32.5%. This performance approaches the supervised system of the same model architecture, demonstrating the great potential of the proposed method. * The work was done during an internship at Tencent AI Lab, Bellevue, WA. 1 A lexicon in ASR is a pre-defined dictionary that maps word sequences into phoneme sequences. arXiv:1812.09323v1 [eess.AS] | UNSUPERVISED SPEECH RECOGNITION VIA SEGMEN- TAL EMPIRICAL OUTPUT DISTRIBUTION MATCHING |
d247763056 | The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs). Thereby, robustifying ML models against adversarial attacks is now a major focus of research. However, nearly all existing defense methods, particularly for robust training, made the white-box assumption that the defender has the access to the details of an ML model (or its surrogate alternatives if available), e.g., its architectures and parameters. Beyond existing works, in this paper we aim to address the problem of black-box defense: How to robustify a black-box model using just input queries and output feedback? Such a problem arises in practical scenarios, where the owner of the predictive model is reluctant to share model information in order to preserve privacy. To this end, we propose a general notion of defensive operation that can be applied to black-box models, and design it through the lens of denoised smoothing (DS), a first-order (FO) certified defense technique. To allow the design of merely using model queries, we further integrate DS with the zeroth-order (gradient-free) optimization. However, a direct implementation of zeroth-order (ZO) optimization suffers a high variance of gradient estimates, and thus leads to ineffective defense. To tackle this problem, we next propose to prepend an autoencoder (AE) to a given (black-box) model so that DS can be trained using variance-reduced ZO optimization. We term the eventual defense as ZO-AE-DS. In practice, we empirically show that ZO-AE-DS can achieve improved accuracy, certified robustness, and query complexity over existing baselines. And the effectiveness of our approach is justified under both image classification and image reconstruction tasks. Codes are available at https://github.com/damon-demon/Black-Box-Defense. | Published as a conference paper at ICLR 2022 HOW TO ROBUSTIFY BLACK-BOX ML MODELS? A ZEROTH-ORDER OPTIMIZATION PERSPECTIVE |
d14724343 | Reinforcement learning (RL) makes it possible to train agents capable of achieving sophisticated goals in complex and uncertain environments. A key difficulty in reinforcement learning is specifying a reward function for the agent to optimize. Traditionally, imitation learning in RL has been used to overcome this problem. Unfortunately, hitherto imitation learning methods tend to require that demonstrations are supplied in the first-person: the agent is provided with a sequence of states and a specification of the actions that it should have taken. While powerful, this kind of imitation learning is limited by the relatively hard problem of collecting first-person demonstrations. Humans address this problem by learning from third-person demonstrations: they observe other humans perform tasks, infer the task, and accomplish the same task themselves. In this paper, we present a method for unsupervised third-person imitation learning. Here third-person refers to training an agent to correctly achieve a simple goal in a simple environment when it is provided a demonstration of a teacher achieving the same goal but from a different viewpoint; and unsupervised refers to the fact that the agent receives only these third-person demonstrations, and is not provided a correspondence between teacher states and student states. Our methods primary insight is that recent advances from domain confusion can be utilized to yield domain agnostic features which are crucial during the training process. To validate our approach, we report successful experiments on learning from third-person demonstrations in a pointmass domain, a reacher domain, and inverted pendulum. | THIRD-PERSON IMITATION LEARNING |
d211126665 | Skip connections are an essential component of current state-of-the-art deep neural networks (DNNs) such as ResNet, WideResNet, DenseNet, and ResNeXt. Despite their huge success in building deeper and more powerful DNNs, we identify a surprising security weakness of skip connections in this paper. Use of skip connections allows easier generation of highly transferable adversarial examples. Specifically, in ResNet-like (with skip connections) neural networks, gradients can backpropagate through either skip connections or residual modules. We find that using more gradients from the skip connections rather than the residual modules according to a decay factor, allows one to craft adversarial examples with high transferability. Our method is termed Skip Gradient Method (SGM). We conduct comprehensive transfer attacks against state-of-the-art DNNs including ResNets, DenseNets, Inceptions, Inception-ResNet, Squeeze-and-Excitation Network (SENet) and robustly trained DNNs. We show that employing SGM on the gradient flow can greatly improve the transferability of crafted attacks in almost all cases. Furthermore, SGM can be easily combined with existing black-box attack techniques, and obtain high improvements over state-of-the-art transferability methods. Our findings not only motivate new research into the architectural vulnerability of DNNs, but also open up further challenges for the design of secure DNN architectures. | Published as a conference paper at ICLR 2020 SKIP CONNECTIONS MATTER: ON THE TRANSFER- ABILITY OF ADVERSARIAL EXAMPLES GENERATED WITH RESNETS |
d252683172 | Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs. Previous methods either generate logical forms that can be executed over KBs to obtain final answers or predict answers directly. Empirical results show that the former often produces more accurate answers, but it suffers from non-execution issues due to potential syntactic and semantic errors in the generated logical forms. In this work, we propose a novel framework DECAF that jointly generates both logical forms and direct answers, and then combines the merits of them to get the final answers. Moreover, different from most of the previous methods, DECAF is based on simple free-text retrieval without relying on any entity linking tools -this simplification eases its adaptation to different datasets. DECAF achieves new stateof-the-art accuracy on WebQSP, FreebaseQA, and GrailQA benchmarks, while getting competitive results on the ComplexWebQuestions benchmark. 1 * Work done during internship at AWS AI Labs 1 Our code is available at https | Published as a conference paper at ICLR 2023 DECAF: JOINT DECODING OF ANSWERS AND LOGICAL FORMS FOR QUESTION ANSWERING OVER KNOWLEDGE BASES |
d211296301 | The learning of hierarchical representations for image classification has experienced an impressive series of successes due in part to the availability of largescale labeled data for training. On the other hand, the trained classifiers have traditionally been evaluated on small and fixed sets of test images, which are deemed to be extremely sparsely distributed in the space of all natural images. It is thus questionable whether recent performance improvements on the excessively re-used test sets generalize to real-world natural images with much richer content variations. Inspired by efficient stimulus selection for testing perceptual models in psychophysical and physiological studies, we present an alternative framework for comparing image classifiers, which we name the MAximum Discrepancy (MAD) competition. Rather than comparing image classifiers using fixed test images, we adaptively sample a small test set from an arbitrarily large corpus of unlabeled images so as to maximize the discrepancies between the classifiers, measured by the distance over WordNet hierarchy. Human labeling on the resulting model-dependent image sets reveals the relative performance of the competing classifiers, and provides useful insights on potential ways to improve them. We report the MAD competition results of eleven Ima-geNet classifiers while noting that the framework is readily extensible and costeffective to add future classifiers into the competition. Codes can be found at https://github.com/TAMU-VITA/MAD. | Published as a conference paper at ICLR 2020 I AM GOING MAD: MAXIMUM DISCREPANCY COM- PETITION FOR COMPARING CLASSIFIERS ADAPTIVELY |
d1185652 | We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrixspace models for compositionality, and show they are special cases of the multiplicative recurrent net. Our experiments show that these models perform comparably or better than Elman-type additive recurrent neural networks and outperform matrix-space models on a standard fine-grained sentiment analysis corpus. Furthermore, they yield comparable results to structural deep models on the recently published Stanford Sentiment Treebank without the need for generating parse trees. | MODELING COMPOSITIONALITY WITH MULTIPLICATIVE RECURRENT NEURAL NETWORKS |
d227126449 | For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow. "Road rules" include rules that drivers are required to follow by law -such as the requirement that vehicles stop at red lights -as well as more subtle social rules -such as the implicit designation of fast lanes on the highway. In this paper, we provide empirical evidence that suggests that -instead of hard-coding road rules into self-driving algorithms -a scalable alternative may be to design multi-agent environments in which road rules emerge as optimal solutions to the problem of maximizing traffic flow. We analyze what ingredients in driving environments cause the emergence of these road rules and find that two crucial factors are noisy perception and agents' spatial density. We provide qualitative and quantitative evidence of the emergence of seven social driving behaviors, ranging from obeying traffic signals to following lanes, all of which emerge from training agents to drive quickly to destinations without colliding. Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving. | Published as a conference paper at ICLR 2021 EMERGENT ROAD RULES IN MULTI-AGENT DRIVING ENVIRONMENTS |
d53210080 | The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this "weight transport problem"(Grossberg, 1987), two more biologically plausible algorithms, proposed byLiao et al. (2016)andLillicrap et al. (2016), relax BP's weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study byBartunov et al. (2018)evaluate variants of target-propagation (TP) and feedback alignment (FA) on MINIST, CIFAR, and ImageNet datasets, and find that although many of the proposed algorithms perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry algorithm(Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights share signs but not magnitudes. We examine the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet, RetinaNet for MS COCO). Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks. These results complement the study byBartunov et al. (2018), and establish a new benchmark for future biologically plausible learning algorithms on more difficult datasets and more complex architectures.ABSTRACTThe backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this "weight transport problem"(Grossberg, 1987), two biologically-plausible algorithms, proposed byLiao et al. (2016)andLillicrap et al. (2016), relax BP's weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study byBartunov et al. (2018)finds that although feedback alignment (FA) and some variants of target-propagation (TP) perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry (SS) algorithm(Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights do not share magnitudes but share signs. We examined the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet; RetinaNet for MS COCO). Surprisingly, networks trained with signsymmetry can attain classification performance approaching that of BP-trained networks. These results complement the study byBartunov et al. (2018)and establish a new benchmark for future biologically-plausible learning algorithms on more difficult datasets and more complex architectures. | Biologically-Plausible Learning Algorithms Can Scale to Large Datasets BIOLOGICALLY-PLAUSIBLE LEARNING ALGORITHMS CAN SCALE TO LARGE DATASETS |
d252222370 | Pre-trained image-text models, like CLIP, have demonstrated the strong power of vision-language representation learned from a large scale of web-collected image-text data. In light of the well-learned visual features, there are works that transfer image representation to the video domain and achieve good results. However, adapting image-text pre-trained models to video-text pre-training (i.e., post-pretraining) has not demonstrated a significant advantage yet. In this paper, we tackle this challenge by raising and addressing two questions: 1) what are the factors hindering post-pretraining CLIP from improving performance on video-text tasks, and 2) how to mitigate the impact of these factors. Through a series of comparative experiments and analyses, we find that the data scale and domain gap between language sources have large impacts. By these observations, we propose an Omnisource Cross-modal Learning method equipped with a Video Proxy mechanism on the basis of CLIP, namely CLIP-ViP. Extensive results show that our approach improves the performance of CLIP on video-text retrieval by a large margin. Our model achieves state-of-the-art results on a variety of datasets, including MSR-VTT, DiDeMo, LSMDC, and Ac-tivityNet. We release our code and pre-trained CLIP-ViP models at https: //github.com/microsoft/XPretrain/tree/main/CLIP-ViP. | Published as a conference paper at ICLR 2023 CLIP-VIP: ADAPTING PRE-TRAINED IMAGE-TEXT MODEL TO VIDEO-LANGUAGE ALIGNMENT |
d251881568 | Neural surface reconstruction aims to reconstruct accurate 3D surfaces based on multi-view images. Previous methods based on neural volume rendering mostly train a fully implicit model with MLPs, which typically require hours of training for a single scene. Recent efforts explore the explicit volumetric representation to accelerate the optimization via memorizing significant information with learnable voxel grids. However, existing voxel-based methods often struggle in reconstructing fine-grained geometry, even when combined with an SDF-based volume rendering scheme. We reveal that this is because 1) the voxel grids tend to break the color-geometry dependency that facilitates fine-geometry learning, and 2) the under-constrained voxel grids lack spatial coherence and are vulnerable to local minima. In this work, we present Voxurf, a voxel-based surface reconstruction approach that is both efficient and accurate. Voxurf addresses the aforementioned issues via several key designs, including 1) a two-stage training procedure that attains a coherent coarse shape and recovers fine details successively, 2) a dual color network that maintains color-geometry dependency, and 3) a hierarchical geometry feature to encourage information propagation across voxels. Extensive experiments show that Voxurf achieves high efficiency and high quality at the same time. On the DTU benchmark, Voxurf achieves higher reconstruction quality with a 20x training speedup compared to previous fully implicit methods.Corresponding authors. Our code is available at https://github.com/wutong16/Voxurf. | Published as a conference paper at ICLR 2023 VOXURF: VOXEL-BASED EFFICIENT AND ACCURATE NEURAL SURFACE RECONSTRUCTION |
d231632506 | Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often only partially conditioned. While the true posteriors depend, e.g., on the entire sequence of observations, approximate posteriors are only informed by past observations. This mimics the Bayesian filter-a mixture of smoothing posteriors. Yet, we show that the ELBO objective forces partially-conditioned amortised posteriors to approximate products of smoothing posteriors instead. Consequently, the learned generative model is compromised. We demonstrate these theoretical findings in three scenarios: traffic flow, handwritten digits, and aerial vehicle dynamics. Using fully-conditioned approximate posteriors, performance improves in terms of generative modelling and multi-step prediction. * Equal contribution. | Published as a conference paper at ICLR 2021 MIND THE GAP WHEN CONDITIONING AMORTISED INFERENCE IN SEQUENTIAL LATENT-VARIABLE MODELS |
d210180949 | Neural networks can approximate complex functions, but they struggle to perform exact arithmetic operations over real numbers. The lack of inductive bias for arithmetic operations leaves neural networks without the underlying logic necessary to extrapolate on tasks such as addition, subtraction, and multiplication. We present two new neural network components: the Neural Addition Unit (NAU), which can learn exact addition and subtraction; and the Neural Multiplication Unit (NMU) that can multiply subsets of a vector. The NMU is, to our knowledge, the first arithmetic neural network component that can learn to multiply elements from a vector, when the hidden size is large. The two new components draw inspiration from a theoretical analysis of recently proposed arithmetic components. We find that careful initialization, restricting parameter space, and regularizing for sparsity is important when optimizing the NAU and NMU. Our proposed units NAU and NMU, compared with previous neural units, converge more consistently, have fewer parameters, learn faster, can converge for larger hidden sizes, obtain sparse and meaningful weights, and can extrapolate to negative and small values. 1 | Published as a conference paper at ICLR 2020 NEURAL ARITHMETIC UNITS |
d253708434 | Partitioning a set of elements into subsets of a priori unknown sizes is essential in many applications. These subset sizes are rarely explicitly learned -be it the cluster sizes in clustering applications or the number of shared versus independent generative latent factors in weakly-supervised learning. Probability distributions over correct combinations of subset sizes are non-differentiable due to hard constraints, which prohibit gradient-based optimization. In this work, we propose the differentiable hypergeometric distribution. The hypergeometric distribution models the probability of different group sizes based on their relative importance. We introduce reparameterizable gradients to learn the importance between groups and highlight the advantage of explicitly learning the size of subsets in two typical applications: weakly-supervised learning and clustering. In both applications, we outperform previous approaches, which rely on suboptimal heuristics to model the unknown size of groups. | Published as a conference paper at ICLR 2023 LEARNING GROUP IMPORTANCE USING THE DIFFER- ENTIABLE HYPERGEOMETRIC DISTRIBUTION |
d16969557 | Within the framework of ADABOOST.MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem to K binary one-againstall classifications.The key element of the method is a vector-valued decision stump, factorized into an input-independent vector of length K and label-independent scalar classifier.At inner tree nodes, the label-dependent vector is discarded and the binary classifier can be used for partitioning the input space into two regions.The algorithm retains the conceptual elegance, power, and computational efficiency of binary ADABOOST.In experiments it is on par with support vector machines and with the best existing multi-class boosting algorithm AOSOLOGITBOOST, and it is significantly better than other known implementations of ADABOOST.MH. | The return of ADABOOST.MH: multi-class Hamming trees |
d23294944 | Phenomenally successful in practical inference problems, convolutional neural networks (CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The number of parameters needed in CNNs, however, are often large and undesirable. Consequently, various methods have been developed to prune a CNN once it is trained. Nevertheless, the resulting CNNs offer limited benefits. While pruning the fully connected layers reduces a CNN's size considerably, it does not improve inference speed noticeably as the compute heavy parts lie in convolutions. Pruning CNNs in a way that increase inference speed often imposes specific sparsity structures, thus limiting the achievable sparsity levels. We present a method to realize simultaneously size economy and speed improvement while pruning CNNs. Paramount to our success is an efficient general sparse-with-dense matrix multiplication implementation that is applicable to convolution of feature maps with kernels of arbitrary sparsity patterns. Complementing this, we developed a performance model that predicts sweet spots of sparsity levels for different layers and on different computer architectures. Together, these two allow us to demonstrate 3.1-7.3× convolution speedups over dense convolution in AlexNet, on Intel Atom, Xeon, and Xeon Phi processors, spanning the spectrum from mobile devices to supercomputers. We also open source our project at https://github.comPublished as a conference paper at ICLR 2017 less, the inference speed gains in pruned networks is not nearly as impressive as the size reduction. In this sense, the benefits of CNN pruning seem not fully realized. | FASTER CNNS WITH DIRECT SPARSE CONVOLUTIONS AND GUIDED PRUNING * |
d6715185 | Deep neural networks have achieved impressive supervised classification performance in many tasks including image recognition, speech recognition, and sequence to sequence learning. However, this success has not been translated to applications like question answering that may involve complex arithmetic and logic reasoning. A major limitation of these models is in their inability to learn even simple arithmetic and logic operations. For example, it has been shown that neural networks fail to learn to add two binary numbers reliably. In this work, we propose Neural Programmer, a neural network augmented with a small set of basic arithmetic and logic operations that can be trained end-to-end using backpropagation. Neural Programmer can call these augmented operations over several steps, thereby inducing compositional programs that are more complex than the built-in operations. The model learns from a weak supervision signal which is the result of execution of the correct program, hence it does not require expensive annotation of the correct program itself. The decisions of what operations to call, and what data segments to apply to are inferred by Neural Programmer. Such decisions, during training, are done in a differentiable fashion so that the entire network can be trained jointly by gradient descent. We find that training the model is difficult, but it can be greatly improved by adding random noise to the gradient. On a fairly complex synthetic table-comprehension dataset, traditional recurrent networks and attentional models perform poorly while Neural Programmer typically obtains nearly perfect accuracy. * Work done during an internship at Google. | Published as a conference paper at ICLR 2016 NEURAL PROGRAMMER: INDUCING LATENT PROGRAMS WITH GRADIENT DESCENT |
d1470238 | Induction of common sense knowledge about prototypical sequences of events has recently received much attention (e.g.,(Chambers & Jurafsky, 2008;Regneri et al., 2010)). Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods. | Learning Semantic Script Knowledge with Event Embeddings |
d255522632 | Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possible actions. In this work, we introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Value Theory (EVT), drawing inspiration from economics. By doing so, we avoid computing Q-values using out-of-distribution actions which is often a substantial source of error. Our key insight is to introduce an objective that directly estimates the optimal soft-value functions (LogSumExp) in the maximum entropy RL setting without needing to sample from a policy. Using EVT, we derive our Extreme Q-Learning framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms, that do not explicitly require access to a policy or its entropy. Our method obtains consistently strong performance in the D4RL benchmark, outperforming prior works by 10+ points on the challenging Franka Kitchen tasks while offering moderate improvements over SAC and TD3 on online DM Control tasks. Visualizations and code can be found on our website 1 . * Equal Contribution | EXTREME Q-LEARNING: MAXENT RL WITHOUT EN- TROPY |
d10419594 | We propose to deal with sequential processes where only partial observations are available by learning a latent representation space on which policies may be accurately learned. | Learning States Representations in POMDP |
d647797 | Discovering causal relations is fundamental to reasoning and intelligence. In particular, observational causal discovery algorithms estimate the cause-effect relation between two random entities X and Y , given n samples from P (X, Y ). In this paper, we develop a framework to estimate the cause-effect relation between two static entities x and y: for instance, an art masterpiece x and its fraudulent copy y. To this end, we introduce the notion of proxy variables, which allow the construction of a pair of random entities (A, B) from the pair of static entities (x, y). Then, estimating the cause-effect relation between A and B using an observational causal discovery algorithm leads to an estimation of the cause-effect relation between x and y. For example, our framework detects the causal relation between unprocessed photographs and their modifications, and orders in time a set of shuffled frames from a video.As our main case study, we introduce a humanelicited dataset of 10,000 pairs of casually-linked pairs of words from natural language. Our methods discover 75% of these causal relations. Finally, we discuss the role of proxy variables in machine learning, as a general tool to incorporate static knowledge into prediction tasks. | Causal Discovery Using Proxy Variables |
d231592391 | Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images. In this paper, we study the few-shot image synthesis task for GAN with minimum computing cost. We propose a light-weight GAN structure that gains superior quality on 1024 × 1024 resolution. Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with less than 100 training samples. Two technique designs constitute our work, a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder. With thirteen datasets covering a wide variety of image domains 1 , we show our model's superior performance compared to the state-of-the-art StyleGAN2, when data and computing budget are limited. 1 The datasets and code are available at: https://github.com/odegeasslbc/FastGAN-pytorch 1 arXiv:2101.04775v1 [cs.CV] 12 Jan 2021Published as a conference paper at ICLR 2021 Moreover, It was shown that in most cases artists want to train their models with datasets of less than 100 images(Elgammal et al., 2020). Dynamic data-augmentation (Karras et al., 2020a; smooths the gap and stabilizes GAN training with fewer images. However, the computing cost from the SOTA models such as StyleGAN2 (Karras et al., 2020b) and BigGAN (Brock et al., 2018) remain to be high, especially when trained with the image resolution on 1024 × 1024.In this paper, our goal is to learn an unconditional GAN on high-resolution images, with low computational cost and few training samples. As summarized inFig. 2, these training conditions expose the model to a high risk of overfitting and mode-collapseZhang & Khoreva, 2018). To train a GAN given the demanding training conditions, we need a generator G that can learn fast, and a discriminator D that can continuously provide useful signals to train G. To address these challenges, we summarize our contribution as:• We design the Skip-Layer channel-wise Excitation (SLE) module, which leverages lowscale activations to revise the channel responses on high-scale feature-maps. SLE allows a more robust gradient flow throughout the model weights for faster training. It also leads to an automated learning of a style/content disentanglement like StyleGAN2. | Published as a conference paper at ICLR 2021 TOWARDS FASTER AND STABILIZED GAN TRAINING FOR HIGH-FIDELITY FEW-SHOT IMAGE SYNTHESIS |
d1282393 | Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpora. In contrast, here we mainly aim to complete a knowledge base by predicting additional true relationships between entities, based on generalizations that can be discerned in the given knowledgebase. We introduce a neural tensor network (NTN) model which predicts new relationship entries that can be added to the database. This model can be improved by initializing entity representations with word vectors learned in an unsupervised fashion from text, and when doing this, existing relations can even be queried for entities that were not present in the database. Our model generalizes and outperforms existing models for this problem, and can classify unseen relationships in WordNet with an accuracy of 75.8%. | Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors |
d260554498 | Our world can be succinctly and compactly described as structured scenes of objects and relations. A typical room, for example, contains salient objects such as tables, chairs and books, and these objects typically relate to each other by their underlying causes and semantics. This gives rise to correlated features, such as position, function and shape. Humans exploit knowledge of objects and their relations for learning a wide spectrum of tasks, and more generally when learning the structure underlying observed data. In this work, we introduce relation networks (RNs) -a general purpose neural network architecture for object-relation reasoning. We show that RNs are capable of learning object relations from scene description data. Furthermore, we show that RNs can act as a bottleneck that induces the factorization of objects from entangled scene description inputs, and from distributed deep representations of scene images provided by a variational autoencoder. The model can also be used in conjunction with differentiable memory mechanisms for implicit relation discovery in one-shot learning tasks. Our results suggest that relation networks are a potentially powerful architecture for solving a variety of problems that require object relation reasoning. * Denotes equal contribution. | Workshop track -ICLR 2017 DISCOVERING OBJECTS AND THEIR RELATIONS FROM ENTANGLED SCENE REPRESENTATIONS |
d3608234 | We show that generating English Wikipedia articles can be approached as a multidocument summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoderdecoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations. * Joint first-authors. Ordered randomly. † Work done as a member of the Google Brain Residency (g.co/brainresidency) | Published as a conference paper at ICLR 2018 GENERATING WIKIPEDIA BY SUMMARIZING LONG SEQUENCES |
d15995898 | Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification. Such adversarial examples have been extensively studied in the context of computer vision applications. In this work, we show adversarial attacks are also effective when targeting neural network policies in reinforcement learning. Specifically, we show existing adversarial example crafting techniques can be used to significantly degrade test-time performance of trained policies. Our threat model considers adversaries capable of introducing small perturbations to the raw input of the policy. We characterize the degree of vulnerability across tasks and training algorithms, for a subclass of adversarialexample attacks in white-box and black-box settings. Regardless of the learned task or training algorithm, we observe a significant drop in performance, even with small adversarial perturbations that do not interfere with human perception. Videos are available at http://rll.berkeley.edu/adversarial. | Adversarial Attacks on Neural Network Policies |
d232147505 | We present a probabilistic 3D generative model, named Generative Cellular Automata, which is able to produce diverse and high quality shapes. We formulate the shape generation process as sampling from the transition kernel of a Markov chain, where the sampling chain eventually evolves to the full shape of the learned distribution. The transition kernel employs the local update rules of cellular automata, effectively reducing the search space in a high-resolution 3D grid space by exploiting the connectivity and sparsity of 3D shapes. Our progressive generation only focuses on the sparse set of occupied voxels and their neighborhood, thus enabling the utilization of an expressive sparse convolutional network. We propose an effective training scheme to obtain the local homogeneous rule of generative cellular automata with sequences that are slightly different from the sampling chain but converge to the full shapes in the training data. Extensive experiments on probabilistic shape completion and shape generation demonstrate that our method achieves competitive performance against recent methods. Figure 1: Sampling chain of shape generation. Full generation processes are included in the supplementary material.Generative Cellular Automata (GCA). When the rule is distributed into the group of occupied cells of an arbitrary starting shape, the sequence of local transitions eventually evolves into an instance among the diverse shapes from the multi-modal distribution. The local update rules of CA greatly reduce the search space of voxel occupancy, exploiting the sparsity and connectivity of 3D shapes.We suggest a simple, progressive training procedure to learn the distribution of local transitions of which repeated application generates the shape of the data distribution. We represent the shape in terms of surface points and store it within a 3D grid, and the transition rule is trained only on the occupied cells by employing a sparse CNN (Graham et al.(2018)). The sparse representation can capture the high-resolution context information, and yet learn the effective rule enjoying the expressive power of deep CNN as demonstrated in various computer vision tasks (Krizhevsky et al. (2012); He et al. (2017)). Inspired by Bordes et al. (2017), our model learns sequences that are slightly different from the sampling chain but converge to the full shapes in the training data. The network successfully learns the update rules of CA, such that a single inference samples from the distribution of diverse modes along the surface.The contributions of the paper are highlighted as follows: (1) We propose Generative Cellular Automata (GCA), a Markov chain based 3D generative model that iteratively mends the shape to a learned distribution, generating diverse and high-fidelity shapes. (2) Our work is the first to learn the local update rules of cellular automata for 3D shape generation in voxel representation. This enables the use of an expressive sparse CNN and reduces the search space of voxel occupancy by fully exploiting sparsity and connectivity of 3D shapes. (3) Extensive experiments show that our method has competitive performance against the state-of-the-art models in probabilistic shape completion and shape generation. | Published as a conference paper at ICLR 2021 LEARNING TO GENERATE 3D SHAPES WITH GENERATIVE CELLULAR AUTOMATA |
d257255149 | The information-theoretic framework promises to explain the predictive power of neural networks. In particular, the information plane analysis, which measures mutual information (MI) between input and representation as well as representation and output, should give rich insights into the training process. This approach, however, was shown to strongly depend on the choice of estimator of the MI. The problem is amplified for deterministic networks if the MI between input and representation is infinite. Thus, the estimated values are defined by the different approaches for estimation, but do not adequately represent the training process from an information-theoretic perspective. In this work, we show that dropout with continuously distributed noise ensures that MI is finite. We demonstrate in a range of experiments 1 that this enables a meaningful information plane analysis for a class of dropout neural networks that is widely used in practice. | Published as a conference paper at ICLR 2023 INFORMATION PLANE ANALYSIS FOR DROPOUT NEURAL NETWORKS |
d231632580 | Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model. When using deep neural networks as forecasting models, we hypothesize that exploiting the pairwise information among multiple (multivariate) time series also improves their forecast. If an explicit graph structure is known, graph neural networks (GNNs) have been demonstrated as powerful tools to exploit the structure. In this work, we propose learning the structure simultaneously with the GNN if the graph is unknown. We cast the problem as learning a probabilistic graph model through optimizing the mean performance over the graph distribution. The distribution is parameterized by a neural network so that discrete graphs can be sampled differentiably through reparameterization. Empirical evaluations show that our method is simpler, more efficient, and better performing than a recently proposed bilevel learning approach for graph structure learning, as well as a broad array of forecasting models, either deep or non-deep learning based, and graph or non-graph based. | DISCRETE GRAPH STRUCTURE LEARNING FOR FORE- CASTING MULTIPLE TIME SERIES |
d203951494 | Biological evolution has distilled the experiences of many learners into the general learning algorithms of humans. Our novel meta reinforcement learning algorithm MetaGenRL is inspired by this process. MetaGenRL distills the experiences of many complex agents to meta-learn a low-complexity neural objective function that decides how future individuals will learn. Unlike recent meta-RL algorithms, MetaGenRL can generalize to new environments that are entirely different from those used for meta-training. In some cases, it even outperforms humanengineered RL algorithms. MetaGenRL uses off-policy second-order gradients during meta-training that greatly increase its sample efficiency. 1 Code is available at Published as a conference paper at ICLR 2020 Compared to RL 2 we find that MetaGenRL does not overfit and is able to train randomly initialized agents using meta-learned learning rules on entirely different environments. Compared to EPG we find that MetaGenRL is more sample efficient, and outperforms significantly under a fixed budget of environment interactions. The results of an ablation study and additional analysis provide further insight into the benefits of our approach. | Published as a conference paper at ICLR 2020 IMPROVING GENERALIZATION IN META REINFORCE- MENT LEARNING USING LEARNED OBJECTIVES |
d244346093 | This paper presents a new pre-trained language model, DeBERTaV3, which improves the original DeBERTa model by replacing masked language modeling (MLM) with replaced token detection (RTD), a more sample-efficient pre-training task. Our analysis shows that vanilla embedding sharing in ELECTRA hurts training efficiency and model performance, because the training losses of the discriminator and the generator pull token embeddings in different directions, creating the "tugof-war" dynamics. We thus propose a new gradient-disentangled embedding sharing method that avoids the tug-of-war dynamics, improving both training efficiency and the quality of the pre-trained model. We have pre-trained DeBERTaV3 using the same settings as DeBERTa to demonstrate its exceptional performance on a wide range of downstream natural language understanding (NLU) tasks. Taking the GLUE benchmark with eight tasks as an example, the DeBERTaV3 Large model achieves a 91.37% average score, which is 1.37% higher than DeBERTa and 1.91% higher than ELECTRA, setting a new state-of-the-art (SOTA) among the models with a similar structure. Furthermore, we have pre-trained a multilingual model mDeBERTaV3 and observed a larger improvement over strong baselines compared to English models. For example, the mDeBERTaV3 Base achieves a 79.8% zero-shot cross-lingual accuracy on XNLI and a 3.6% improvement over XLM-R Base, creating a new SOTA on this benchmark. Our models and code are publicly available at https | Published as a conference paper at ICLR 2023 DEBERTAV3: IMPROVING DEBERTA USING ELECTRA-STYLE PRE-TRAINING WITH GRADIENT- DISENTANGLED EMBEDDING SHARING |
d222134108 | While unsupervised domain translation (UDT) has seen a lot of success recently, we argue that allowing its translation to be mediated via categorical semantic features could enable wider applicability. In particular, we argue that categorical semantics are important when translating between domains with multiple object categories possessing distinctive styles, or even between domains that are simply too different but still share high-level semantics. We propose a method to learn, in an unsupervised manner, categorical semantic features (such as object labels) that are invariant of the source and target domains. We show that conditioning the style of a unsupervised domain translation methods on the learned categorical semantics leads to a considerably better high-level features preservation on tasks such as MNIST↔SVHN and to a more realistic stylization on Sketches→Reals. | INTEGRATING CATEGORICAL SEMANTICS INTO UNSU- PERVISED DOMAIN TRANSLATION |
d235458224 | As reinforcement learning (RL) has achieved great success and been even adopted in safety-critical domains such as autonomous vehicles, a range of empirical studies have been conducted to improve its robustness against adversarial attacks. However, how to certify its robustness with theoretical guarantees still remains challenging. In this paper, we present the first unified framework CROP (Certifying Robust Policies for RL) to provide robustness certification on both action and reward levels. In particular, we propose two robustness certification criteria: robustness of per-state actions and lower bound of cumulative rewards. We then develop a local smoothing algorithm for policies derived from Q-functions to guarantee the robustness of actions taken along the trajectory; we also develop a global smoothing algorithm for certifying the lower bound of a finite-horizon cumulative reward, as well as a novel local smoothing algorithm to perform adaptive search in order to obtain tighter reward certification. Empirically, we apply CROP to evaluate several existing empirically robust RL algorithms, including adversarial training and different robust regularization, in four environments (two representative Atari games, Highway, and CartPole). Furthermore, by evaluating these algorithms against adversarial attacks, we demonstrate that our certifications are often tight. All experiment results are available at website https://crop-leaderboard.github.io. | CROP: CERTIFYING ROBUST POLICIES FOR RE- INFORCEMENT LEARNING THROUGH FUNCTIONAL SMOOTHING |
d90258012 | Recurrent neural networks (RNNs) can model natural language by sequentially "reading" input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts to speed up this inference, known as "neural speed reading", either ignore or skim over part of the input. We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference. The model consists of a standard LSTM and two agents: one capable of skipping single words when reading, and one capable of exploiting punctuation structure (sub-sentence separators (,:), sentence end symbols (.!?), or end of text markers) to jump ahead after reading a word. A comprehensive experimental evaluation of our model against all five state-of-the-art neural reading models shows that Structural-Jump-LSTM achieves the best overall floating point operations (FLOP) reduction (hence is faster), while keeping the same accuracy or even improving it compared to a vanilla LSTM that reads the whole text. | NEURAL SPEED READING WITH STRUCTURAL-JUMP- LSTM |
d246706248 | While the class of Polynomial Nets demonstrates comparable performance to neural networks (NN), it currently has neither theoretical generalization characterization nor robustness guarantees. To this end, we derive new complexity bounds for the set of Coupled CP-Decomposition (CCP) and Nested Coupled CP-decomposition (NCP) models of Polynomial Nets in terms of the ∞ -operator-norm and the 2operator norm. In addition, we derive bounds on the Lipschitz constant for both models to establish a theoretical certificate for their robustness. The theoretical results enable us to propose a principled regularization scheme that we also evaluate experimentally in six datasets and show that it improves the accuracy as well as the robustness of the models to adversarial perturbations. We showcase how this regularization can be combined with adversarial training, resulting in further improvements. arXiv:2202.05068v1 [cs.LG] 10 Feb 2022Published as a conference paper at ICLR 2022 (Szegedy et al., 2014; Goodfellow et al., 2015) via a worst-case analysis c.f. Scaman and Virmaux (2018). Most importantly, the bounds themselves provide a principled way to regularize the hypothesis class and improve their accuracy or robustness.Indeed, such schemes have been observed in practice to improve the performance of Deep Neural Networks. Unfortunately, similar regularization schemes for Polynomial Nets do not exist due to the lack of analogous bounds. Hence, it is possible that PNs are not yet being used to their fullest potential. We believe that theoretical advances in their understanding might lead to more resilient and accurate models. In this work, we aim to fill the gap in the theoretical understanding of PNs. We summarize our main contributions as follows:Rademacher Complexity Bounds. We derive bounds on the Rademacher Complexity of the Coupled CP-decomposition model (CCP) and Nested Coupled CP-decomposition model (NCP) of PNs, under the assumption of a unit ∞ -norm bound on the input(Theorems 1 and 3), a natural assumption in image-based applications. Analogous bounds for the 2 -norm are also provided (Appendices E.3 and F.3). Such bounds lead to the first known generalization error bounds for this class of models.Lipschitz constant Bounds. To complement our understanding of the CCP and NCP models, we derive upper bounds on their ∞ -Lipschitz constants (Theorems 2 and 4), which are directly related to their robustness against ∞ -bounded adversarial perturbations, and provide formal guarantees. Analogous results hold for any p -norm (Appendices E.4 and F.4).Regularization schemes. We identify key quantities that simultaneously control both Rademacher Complexity and Lipschitz constant bounds that we previously derived, i.e., the operator norms of the weight matrices in the Polynomial Nets. Hence, we propose to regularize the CCP and NCP models by constraining such operator norms. In doing so, our theoretical results indicate that both the generalization and the robustness to adversarial perturbations should improve. We propose a Projected Stochastic Gradient Descent scheme (Algorithm 1), enjoying the same per-iteration complexity as vanilla back-propagation in the ∞ -norm case, and a variant that augments the base algorithm with adversarial traning (Algorithm 2).Experiments. We conduct experimentation in five widely-used datasets on image recognition and on dataset in audio recognition. The experimentation illustrates how the aforementioned regularization schemes impact the accuracy (and the robust accuracy) of both CCP and NCP models, outperforming alternative schemes such as Jacobian regularization and the L 2 weight decay. Indeed, for a grid of regularization parameters we observe that there exists a sweet-spot for the regularization parameter which not only increases the test-accuracy of the model, but also its resilience to adversarial perturbations. Larger values of the regularization parameter also allow a trade-off between accuracy and robustness. The observation is consistent across all datasets and all adversarial attacks demonstrating the efficacy of the proposed regularization scheme.RADEMACHER COMPLEXITY AND LIPSCHITZ CONSTANT BOUNDS FOR POLYNOMIAL NETSNotation. The symbol • denotes the Hadamard (element-wise) product, the symbol • is the facesplitting product, while the symbol denotes a convolutional operator. Matrices are denoted by uppercase letters e.g., V . Due to the space constraints, a detailed notation is deferred to Appendix C. | Published as a conference paper at ICLR 2022 CONTROLLING THE COMPLEXITY AND LIPSCHITZ CONSTANT IMPROVES POLYNOMIAL NETS |
d825469 | We propose 'Dracula', a new framework for unsupervised feature selection from sequential data such as text. Dracula learns a dictionary of n-grams that efficiently compresses a given corpus and recursively compresses its own dictionary; in effect, Dracula is a 'deep' extension of Compressive Feature Learning. It requires solving a binary linear program that may be relaxed to a linear program. Both problems exhibit considerable structure, their solution paths are well behaved, and we identify parameters which control the depth and diversity of the dictionary. We also discuss how to derive features from the compressed documents and show that while certain unregularized linear models are invariant to the structure of the compressed dictionary, this structure may be used to regularize learning. Experiments are presented that demonstrate the efficacy of Dracula's features. | Published as a conference paper at ICLR 2016 DATA REPRESENTATION AND COMPRESSION USING LINEAR-PROGRAMMING APPROXIMATIONS |
d40325044 | In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a building via GPS signal changes. Second, we use a barometer equipped smartphone to measure the change in barometric pressure from the entrance of the building to the victim's indoor location. Unlike impractical previous approaches, our system is the first that does not require the use of beacons, prior knowledge of the building infrastructure, or knowledge of user behavior. We demonstrate real-world feasibility through 63 experiments across five different tall buildings throughout New York City where our system predicted the correct floor level with 100% accuracy.arXiv:1710.11122v4 [cs.CY] 15 Sep 2018Published as a conference paper at ICLR 2018 user could use our system at a random time and place. We offer an extensive discussion of potential real-world problems and provide solutions in (appendix B).We conducted 63 test experiments across six different buildings in New York City to show that the system can estimate the floor level inside a building with 65.0% accuracy when the floor-ceiling distance in the building is unknown. However, when repeated visit data can be collected, our simple clustering method can learn the floor-ceiling distances and improve the accuracy to 100%. All code, data and data collection app are available open-source on github. 2 . | Published as a conference paper at ICLR 2018 PREDICTING FLOOR LEVEL FOR 911 CALLS WITH NEURAL NETWORKS AND SMARTPHONE SENSOR DATA |
d253237320 | The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making. To this end, recent work has focused on miscalibration, i.e., the over or under confidence of model scores. Yet calibration is not enough: even a perfectly calibrated classifier with the best possible accuracy can have confidence scores that are far from the true posterior probabilities. This is due to the grouping loss, created by samples with the same confidence scores but different true posterior probabilities. Proper scoring rule theory shows that given the calibration loss, the missing piece to characterize individual errors is the grouping loss. While there are many estimators of the calibration loss, none exists for the grouping loss in standard settings. Here, we propose an estimator to approximate the grouping loss. We show that modern neural network architectures in vision and NLP exhibit grouping loss, notably in distribution shifts settings, which highlights the importance of pre-production validation. | Published as a conference paper at ICLR 2023 BEYOND CALIBRATION: ESTIMATING THE GROUPING LOSS OF MODERN NEURAL NETWORKS |
d259129237 | Given a graph learning task, such as link prediction, on a new graph, how can we select the best method as well as its hyperparameters (collectively called a model) without having to train or evaluate any model on the new graph? Model selection for graph learning has been largely ad hoc. A typical approach has been to apply popular methods to new datasets, but this is often suboptimal. On the other hand, systematically comparing models on the new graph quickly becomes too costly, or even impractical. In this work, we develop the first meta-learning approach for evaluation-free graph learning model selection, called METAGL, which utilizes the prior performances of existing methods on various benchmark graph datasets to automatically select an effective model for the new graph, without any model training or evaluations. To quantify similarities across a wide variety of graphs, we introduce specialized meta-graph features that capture the structural characteristics of a graph. Then we design G-M network, which represents the relations among graphs and models, and develop a graph-based meta-learner operating on this G-M network, which estimates the relevance of each model to different graphs. Extensive experiments show that using METAGL to select a model for the new graph greatly outperforms several existing meta-learning techniques tailored for graph learning model selection (up to 47% better), while being extremely fast at test time (∼1 sec). | Published as a conference paper at ICLR 2023 METAGL: EVALUATION-FREE SELECTION OF GRAPH LEARNING MODELS VIA META-LEARNING |
d258865745 | We present EMMa, an Extensible, Multimodal dataset of Amazon product listings that contains rich Material annotations. It contains more than 2.8 million objects, each with image(s), listing text, mass, price, product ratings, and position in Amazon's product-category taxonomy. We also design a comprehensive taxonomy of 182 physical materials (e.g., Plastic → Thermoplastic → Acrylic). Objects are annotated with one or more materials from this taxonomy. With the numerous attributes available for each object, we develop a Smart Labeling framework to quickly add new binary labels to all objects with very little manual labeling effort, making the dataset extensible. Each object attribute in our dataset can be included in either the model inputs or outputs, leading to combinatorial possibilities in task configurations. For example, we can train a model to predict the object category from the listing text, or the mass and price from the product listing image. EMMa offers a new benchmark for multi-task learning in computer vision and NLP, and allows practitioners to efficiently add new tasks and object attributes at scale. | Published as a conference paper at ICLR 2023 AN EXTENSIBLE MULTIMODAL MULTI-TASK OBJECT DATASET WITH MATERIALS |
d249626076 | Transformers have become a default architecture in computer vision, but understanding what drives their predictions remains a challenging problem. Current explanation approaches rely on attention values or input gradients, but these provide a limited view of a model's dependencies. Shapley values offer a theoretically sound alternative, but their computational cost makes them impractical for large, high-dimensional models. In this work, we aim to make Shapley values practical for vision transformers (ViTs). To do so, we first leverage an attention masking approach to evaluate ViTs with partial information, and we then develop a procedure to generate Shapley value explanations via a separate, learned explainer model. Our experiments compare Shapley values to many baseline methods (e.g., attention rollout, GradCAM, LRP), and we find that our approach provides more accurate explanations than existing methods for ViTs. * Equal contribution. | Published as a conference paper at ICLR 2023 LEARNING TO ESTIMATE SHAPLEY VALUES WITH VISION TRANSFORMERS |
d3526769 | Convolutional neural networks have demonstrated their powerful ability on various tasks in recent years. However, they are extremely vulnerable to adversarial examples. I.e., clean images, with imperceptible perturbations added, can easily cause convolutional neural networks to fail. In this paper, we propose to utilize randomization to mitigate adversarial effects. Specifically, we use two randomization operations: random resizing, which resizes the input images to a random size, and random padding, which pads zeros around the input images in a random manner. Extensive experiments demonstrate that the proposed randomization method is very effective at defending against both single-step and iterative attacks. Our method also enjoys the following advantages: 1) no additional training or fine-tuning, 2) very few additional computations, 3) compatible with other adversarial defense methods. By combining the proposed randomization method with an adversarially trained model, it achieves a normalized score of 0.924 (ranked No.2 among 107 defense teams) in the NIPS 2017 adversarial examples defense challenge, which is far better than using adversarial training alone with a normalized score of 0.773 (ranked No.56). The code is public available at https: /. R-fcn: Object detection via region-based fully convolutional networks. | MITIGATING ADVERSARIAL EFFECTS THROUGH RAN- DOMIZATION |
d3459663 | We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance function with unbiased mini-batch gradients. Experimentally we show OT-GAN to be highly stable when trained with large mini-batches, and we present state-of-the-art results on several popular benchmark problems for image generation. * equal contribution † work performed during an internship at OpenAI | Published as a conference paper at ICLR 2018 IMPROVING GANS USING OPTIMAL TRANSPORT |
d211069144 | Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, while understanding the dynamics of gradient algorithms for solving such formulations has remained a grand challenge. As a first step, we restrict to bilinear zero-sum games and give a systematic analysis of popular gradient updates, for both simultaneous and alternating versions. We provide exact conditions for their convergence and find the optimal parameter setup and convergence rates. In particular, our results offer formal evidence that alternating updates converge "better" than simultaneous ones. | CONVERGENCE OF GRADIENT METHODS ON BILIN- EAR ZERO-SUM GAMES |
d208195366 | Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain. While response properties of neurons in artificial neural networks bear similarities to those in the brain, the network architectures are often constrained to be different. Here we ask if a neural network can recover both neural representations and, if the architecture is unconstrained and optimized, the anatomical properties of neural circuits. We demonstrate this in a system where the connectivity and the functional organization have been characterized, namely, the head direction circuits of the rodent and fruit fly. We trained recurrent neural networks (RNNs) to estimate head direction through integration of angular velocity. We found that the two distinct classes of neurons observed in the head direction system, the Ring neurons and the Shifter neurons, emerged naturally in artificial neural networks as a result of training. Furthermore, connectivity analysis and in-silico neurophysiology revealed structural and mechanistic similarities between artificial networks and the head direction system. Overall, our results show that optimization of RNNs in a goal-driven task can recapitulate the structure and function of biological circuits, suggesting that artificial neural networks can be used to study the brain at the level of both neural activity and anatomical organization.Published as a conference paper at ICLR 2020 by long evolutionary time. By leveraging existing knowledge of the biological head direction (HD) systems, we demonstrate that RNNs exhibit striking similarities in both structure and function. Our results suggest that goal-driven training of artificial neural networks provide a framework to study neural systems at the level of both neural activity and anatomical organization. | EMERGENCE OF FUNCTIONAL AND STRUCTURAL PROPERTIES OF THE HEAD DIRECTION SYSTEM BY OP- TIMIZATION OF RECURRENT NEURAL NETWORKS |
d232269984 | Numerous task-specific variants of conditional generative adversarial networks have been developed for image completion. Yet, a serious limitation remains that all existing algorithms tend to fail when handling large-scale missing regions. To overcome this challenge, we propose a generic new approach that bridges the gap between image-conditional and recent modulated unconditional generative architectures via co-modulation of both conditional and stochastic style representations. Also, due to the lack of good quantitative metrics for image completion, we propose the new Paired/Unpaired Inception Discriminative Score (P-IDS/U-IDS), which robustly measures the perceptual fidelity of inpainted images compared to real images via linear separability in a feature space. Experiments demonstrate superior performance in terms of both quality and diversity over state-of-the-art methods in free-form image completion and easy generalization to image-to-image translation. Code is available at https://github.com/zsyzzsoft/co-mod-gan. | LARGE SCALE IMAGE COMPLETION VIA CO-MODUL- ATED GENERATIVE ADVERSARIAL NETWORKS |
d256598204 | Reward function is essential in reinforcement learning (RL), serving as the guiding signal to incentivize agents to solve given tasks, however, is also notoriously difficult to design. In many cases, only imperfect rewards are available, which inflicts substantial performance loss for RL agents. In this study, we propose a unified offline policy optimization approach, RGM (Reward Gap Minimization), which can smartly handle diverse types of imperfect rewards. RGM is formulated as a bi-level optimization problem: the upper layer optimizes a reward correction term that performs visitation distribution matching w.r.t. some expert data; the lower layer solves a pessimistic RL problem with the corrected rewards. By exploiting the duality of the lower layer, we derive a tractable algorithm that enables sampled-based learning without any online interactions. Comprehensive experiments demonstrate that RGM achieves superior performance to existing methods under diverse settings of imperfect rewards. Further, RGM can effectively correct wrong or inconsistent rewards against expert preference and retrieve useful information from biased rewards. | Published as a conference paper at ICLR 2023 MIND THE GAP: OFFLINE POLICY OPTIMIZATION FOR IMPERFECT REWARDS |
d209444397 | Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms. Recently, several learned keypoint methods have demonstrated compelling performance on challenging benchmarks. However, generating consistent and accurate training data for interest-point detection in natural images still remains challenging, especially for human annotators. We introduce IO-Net (i.e. InlierOutlierNet), a novel proxy task for the self-supervision of keypoint detection, description and matching. By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching. Second, we introduce KeyPointNet, a keypoint-network architecture that is especially amenable to robust keypoint detection and description. We design the network to allow local keypoint aggregation to avoid artifacts due to spatial discretizations commonly used for this task, and we improve fine-grained keypoint descriptor performance by taking advantage of efficient sub-pixel convolutions to upsample the descriptor feature-maps to a higher operating resolution. Through extensive experiments and ablative analysis, we show that the proposed self-supervised keypoint learning method greatly improves the quality of feature matching and homography estimation on challenging benchmarks over the state-of-the-art. † † Code: https://github.com/Published as a conference paper at ICLR 2020 Our main contributions are: (i) We introduce IO-Net (i.e. InlierOutlierNet), a novel proxy task for the self-supervision of keypoint detection, description and matching. By using a neurally-guided outlier-rejection scheme (Brachmann & Rother, 2019) as an auxiliary task, we show that we are able to simultaneously self-supervise keypoint description and generate optimal inlier sets from possible corresponding point-pairs. While the keypoint network is fully self-supervised, the network is able to effectively learn distinguishable features for two-view matching, via the flow of gradients from consistently matched point-pairs. (ii) We introduce KeyPointNet, and propose two modifications to the keypoint-network architecture described inChristiansen et al. (2019). First, we allow the keypoint location head to regress keypoint locations outside their corresponding cells, enabling keypoint matching near and across cell-boundaries. Second, by taking advantage of sub-pixel convolutions to interpolate the descriptor feature-maps to a higher resolution, we show that we are able to improve the fine-grained keypoint descriptor fidelity and performance especially as they retain more fine-grained detail for pixel-level metric learning in the self-supervised regime. Through extensive experiments and ablation studies, we show that the proposed architecture allows us to establish state-of-the-art performance for the task of self-supervised keypoint detection, description and matching. | Published as a conference paper at ICLR 2020 NEURAL OUTLIER REJECTION FOR SELF-SUPERVISED KEYPOINT LEARNING |
d259373058 | Model-based reinforcement learning is one approach to increase sample efficiency. However, the accuracy of the dynamics model and the resulting compounding error over modelled trajectories are commonly regarded as key limitations. A natural question to ask is: How much more sample efficiency can be gained by improving the learned dynamics models? Our paper empirically answers this question for the class of model-based value expansion methods in continuous control problems. Value expansion methods should benefit from increased model accuracy by enabling longer rollout horizons and better value function approximations. Our empirical study, which leverages oracle dynamics models to avoid compounding model errors, shows that (1) longer horizons increase sample efficiency, but the gain in improvement decreases with each additional expansion step, and (2) the increased model accuracy only marginally increases the sample efficiency compared to learned models with identical horizons. Therefore, longer horizons and increased model accuracy yield diminishing returns in terms of sample efficiency. These improvements in sample efficiency are particularly disappointing when compared to model-free value expansion methods. Even though they introduce no computational overhead, we find their performance to be on-par with model-based value expansion methods. Therefore, we conclude that the limitation of model-based value expansion methods is not the model accuracy of the learned models. While higher model accuracy is beneficial, our experiments show that even a perfect model will not provide an un-rivalled sample efficiency but that the bottleneck lies elsewhere. Levine. Model-based value estimation for efficient model-free reinforcement learning. | DIMINISHING RETURN OF VALUE EXPANSION METH- ODS IN MODEL-BASED REINFORCEMENT LEARNING |
d3803599 | Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. A recent view argues that Resnets perform iterative refinement of features. We attempt to further expose properties of this aspect. To this end, we study Resnets both analytically and empirically. We formalize the notion of iterative refinement in Resnets by showing that residual architectures naturally encourage features to move along the negative gradient of loss during the feedforward phase. In addition, our empirical analysis suggests that Resnets are able to perform both representation learning and iterative refinement. In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features. Finally we observe that sharing residual layers naively leads to representation explosion and hurts generalization performance, and show that simple existing strategies can help alleviating this problem. | RESIDUAL CONNECTIONS ENCOURAGE ITERATIVE IN- FERENCE |
d256615571 | There is a growing interest in the machine learning community in developing predictive algorithms that are "interpretable by design". Towards this end, recent work proposes to make interpretable decisions by sequentially asking interpretable queries about data until a prediction can be made with high confidence based on the answers obtained (the history). To promote short query-answer chains, a greedy procedure called Information Pursuit (IP) is used, which adaptively chooses queries in order of information gain. Generative models are employed to learn the distribution of query-answers and labels, which is in turn used to estimate the most informative query. However, learning and inference with a full generative model of the data is often intractable for complex tasks. In this work, we propose Variational Information Pursuit (V-IP), a variational characterization of IP which bypasses the need for learning generative models. V-IP is based on finding a query selection strategy and a classifier that minimizes the expected cross-entropy between true and predicted labels. We then demonstrate that the IP strategy is the optimal solution to this problem. Therefore, instead of learning generative models, we can use our optimal strategy to directly pick the most informative query given any history. We then develop a practical algorithm by defining a finite-dimensional parameterization of our strategy and classifier using deep networks and train them end-to-end using our objective. Empirically, V-IP is 10-100x faster than IP on different Vision and NLP tasks with competitive performance. Moreover, V-IP finds much shorter query chains when compared to reinforcement learning which is typically used in sequential-decision-making problems. Finally, we demonstrate the utility of V-IP on challenging tasks like medical diagnosis where the performance is far superior to the generative modelling approach. 1 Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. pages 17 Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30, 2017. pages 1 : Efficient dynamic discovery of high-value information with partial vae. arXiv preprint arXiv:1809.11142, 2018. pages 3, 6, 9, 21 Rishabh Misra. News category dataset, 06 2018. pages 7 Volodymyr Mnih, Nicolas Heess, Alex Graves, et al. Recurrent models of visual attention. Advances in neural information processing systems, 27, 2014. pages 2, 3, 7 Meike Nauta, Ron van Bree, and Christin Seifert. Neural prototype trees for interpretable finegrained image recognition. In . Neuralsympcheck: A symptom checking and disease diagnostic neural model with logic regularization. arXiv preprint arXiv:2206.00906, 2022. pages 9, 21 Andrew Ng and Michael Jordan. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in neural information processing systems, 14, 2001. pages 6 . Refuel: Exploring sparse features in deep reinforcement learning for fast disease diagnosis. Advances in neural information processing systems, 31, 2018. pages 2, 3, 7, 9, 20 Samrudhdhi B Rangrej and James J Clark. A probabilistic hard attention model for sequentially observed scenes. arXiv preprint arXiv:2111.07534, 2021. pages 3, 6, 7, 8, 9, 21 Sashank J Reddi, Satyen Kale, and Sanjiv Kumar. On the convergence of adam and beyond. arXiv preprint arXiv:1904.09237, 2019. pages 17 Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135-1144, 2016. pages 1 12 Published as a conference paper at ICLR 2023 Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206-215, 2019. pages 2, 3 Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar, and Vineeth N Balasubramanian. A framework for learning ante-hoc explainable models via concepts. In policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. pages 17 Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In | Published as a conference paper at ICLR 2023 VARIATIONAL INFORMATION PURSUIT FOR INTER- PRETABLE PREDICTIONS |
d251442769 | Models using structured state space sequence (S4) layers have achieved state-ofthe-art performance on long-range sequence modeling tasks. An S4 layer combines linear state space models (SSMs), the HiPPO framework, and deep learning to achieve high performance. We build on the design of the S4 layer and introduce a new state space layer, the S5 layer. Whereas an S4 layer uses many independent single-input, single-output SSMs, the S5 layer uses one multi-input, multi-output SSM. We establish a connection between S5 and S4, and use this to develop the initialization and parameterization used by the S5 model. The result is a state space layer that can leverage efficient and widely implemented parallel scans, allowing S5 to match the computational efficiency of S4, while also achieving state-of-the-art performance on several long-range sequence modeling tasks. S5 averages 87.4% on the long range arena benchmark, and 98.5% on the most difficult Path-X task. Re. Efficiently modeling long sequences with structured state | Published as a conference paper at ICLR 2023 SIMPLIFIED STATE SPACE LAYERS FOR SEQUENCE MODELING |
d246430935 | Humans excel at continually learning from an ever-changing environment whereas it remains a challenge for deep neural networks which exhibit catastrophic forgetting. The complementary learning system (CLS) theory suggests that the interplay between rapid instance-based learning and slow structured learning in the brain is crucial for accumulating and retaining knowledge. Here, we propose CLS-ER, a novel dual memory experience replay (ER) method which maintains short-term and long-term semantic memories that interact with the episodic memory. Our method employs an effective replay mechanism whereby new knowledge is acquired while aligning the decision boundaries with the semantic memories. CLS-ER does not utilize the task boundaries or make any assumption about the distribution of the data which makes it versatile and suited for "general continual learning". Our approach achieves state-of-the-art performance on standard benchmarks as well as more realistic general continual learning settings. | Published as a conference paper at ICLR 2022 LEARNING FAST, LEARNING SLOW: A GENERAL CONTINUAL LEARNING METHOD BASED ON COMPLE- MENTARY LEARNING SYSTEM |
d256105733 | Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries. However, recent studies reveal that text-to-SQL models are vulnerable to task-specific perturbations. Previous curated robustness test sets usually focus on individual phenomena. In this paper, we propose a comprehensive robustness benchmark 1 based on Spider, a cross-domain text-to-SQL benchmark, to diagnose the model robustness. We design 17 perturbations on databases, natural language questions, and SQL queries to measure the robustness from different angles. In order to collect more diversified natural question perturbations, we utilize large pretrained language models (PLMs) to simulate human behaviors in creating natural questions. We conduct a diagnostic study of the state-of-the-art models on the robustness set. Experimental results reveal that even the most robust model suffers from a 14.0% performance drop overall and a 50.7% performance drop on the most challenging perturbation. We also present a breakdown analysis regarding text-to-SQL model designs and provide insights for improving model robustness. * Work done during internship at AWS AI Labs 1 Our data and code are available at https://github.com/awslabs/ diagnostic-robustness-text-to-sql. | |
d234358863 | Combinations of neural ODEs with recurrent neural networks (RNN), like GRU-ODE-Bayes or ODE-RNN are well suited to model irregularly observed time series. While those models outperform existing discrete-time approaches, no theoretical guarantees for their predictive capabilities are available. Assuming that the irregularly-sampled time series data originates from a continuous stochastic process, the L 2 -optimal online prediction is the conditional expectation given the currently available information. We introduce the Neural Jump ODE (NJ-ODE) that provides a data-driven approach to learn, continuously in time, the conditional expectation of a stochastic process. Our approach models the conditional expectation between two observations with a neural ODE and jumps whenever a new observation is made. We define a novel training framework, which allows us to prove theoretical guarantees for the first time. In particular, we show that the output of our model converges to the L 2 -optimal prediction. This can be interpreted as solution to a special filtering problem. We provide experiments showing that the theoretical results also hold empirically. Moreover, we experimentally show that our model outperforms the baselines in more complex learning tasks and give comparisons on real-world datasets.arXiv:2006.04727v4 [stat.ML] 16 Apr 2021Published as a conference paper at ICLR 2021 hidden state is updated at each observation and constant in between. Conversely, in the GRU-ODE-Bayes and ODE-RNN framework, a neural ODE is trained to model the continuous evolution of the hidden state of the RNN between two observations. While GRU-ODE-Bayes and ODE-RNN both provide convincing empirical results, they lack thorough theoretical guarantees.Contribution. In this paper, we introduce a mathematical framework to precisely describe the problem statement of online prediction and filtering of a stochastic process with temporal irregular observations. Based on this rigorous mathematical description, we introduce the Neural Jump ODE (NJ-ODE). The model architecture is very similar to the one of GRU-ODE-Bayes and ODE-RNN, however we introduce a novel training framework, which in contrast to them allows us to prove convergence guarantees for the first time. Moreover, we demonstrate empirically the capabilities of our model.Precise problem formulation. We emphasize that a precise definition of all ingredients is needed, to be able to show theoretical convergence guarantees, which is the main purpose of this work. Since the objects of interest are stochastic processes, we use tools from probability theory and stochastic calculus. To make the paper more readable and comprehensible also for readers without background in these fields, the precise formulations and demonstrations of all claims are given in the appendix, while the main part of the paper focuses on giving well understandable heuristics.PROPOSED METHOD -NEURAL JUMP ODEMarkovian paths. Our assumptions on the stochastic process X imply that it is a Markov process. In particular, the optimal prediction of a future state of X only depends on the current state rather than on the full history. Hence, the previous values do not provide any additional useful information for the prediction.JumpNN instead of RNN. Using a neural ODE between two observation has the advantage that it allows to continuously model the hidden state between two observations. But since the underlying process is Markov, there is no need to use a RNN-cell to model the updates of the hidden state at each new observation. Instead, whenever a new observation is made, the new hidden state can solely be defined from this observation. Therefore, we replace the RNN-cell used in ODE-RNN by a standard neural network mapping the observation to the hidden state. We call this the jumpNN which can be interpreted as an encoder map. Compared to ODE-RNN, this architecture is easier to train.Last observation and time increment as additional inputs for the neural ODE. The neural network f θ used in the neural ODE takes two arguments as inputs, the hidden state h t and the current time t. However, our theoretical problem analysis suggests, that instead of t the last observation time t i−1 and the time increment t − t i−1 should be used. Additionally the last observation x i−1 should also be part of the input.Published as a conference paper at ICLR 2021 Torben G. Andersen and Tim Bollerslev. Intraday periodicity and volatility persistence in financial markets. | Published as a conference paper at ICLR 2021 NEURAL JUMP ORDINARY DIFFERENTIAL EQUATIONS: CONSISTENT CONTINUOUS-TIME PREDICTION AND FILTERING |
d3358859 | Visual Question Answering (VQA) models have struggled with counting objects in natural images so far. We identify a fundamental problem due to soft attention in these models as a cause. To circumvent this problem, we propose a neural network component that allows robust counting from object proposals. Experiments on a toy task show the effectiveness of this component and we obtain state-of-theart accuracy on the number category of the VQA v2 dataset without negatively affecting other categories, even outperforming ensemble models with our single model. On a difficult balanced pair metric, the component gives a substantial improvement in counting over a strong baseline by 6.6%. exist. The main difference is that, since we are interested in counting, our component does not need to make discrete decisions about which bounding boxes to keep; it outputs counting features, not a smaller set of bounding boxes. Our component is also easily integrated into standard VQA models that utilize soft attention without any need for other network architecture changes and can be used without using true bounding boxes for supervision.On the VQA v2 dataset (Goyal et al., 2017) that we apply our method on, only few advances on counting questions have been made. The main improvement in accuracy is due to the use of object proposals in the visual processing pipeline, proposed byAnderson et al. (2017). Their object proposal network is trained with classes in singular and plural forms, for example "tree" versus "trees", which only allows primitive counting information to be present in the object features after region-of-interest pooling. Our approach differs in the way that instead of relying on counting features being present in the input, we create counting features using information present in the attention map over object proposals. This has the benefit of being able to count anything that the attention mechanism can discriminate instead of only objects that belong to the predetermined set of classes that had plural forms. | Published as a conference paper at ICLR 2018 LEARNING TO COUNT OBJECTS IN NATURAL IMAGES FOR VISUAL QUESTION ANSWERING |
d8581028 | The past year saw the introduction of new architectures such as Highway networks(Srivastava et al., 2015a)and Residual networks (He et al., 2015) which, for the first time, enabled the training of feedforward networks with dozens to hundreds of layers using simple gradient descent. While depth of representation has been posited as a primary reason for their success, there are indications that these architectures defy a popular view of deep learning as a hierarchical computation of increasingly abstract features at each layer.In this report, we argue that this view is incomplete and does not adequately explain several recent findings. We propose an alternative viewpoint based on unrolled iterative estimation-a group of successive layers iteratively refine their estimates of the same features instead of computing an entirely new representation. We demonstrate that this viewpoint directly leads to the construction of Highway and Residual networks. Finally we provide preliminary experiments to discuss the similarities and differences between the two architectures. | Published as a conference paper at ICLR 2017 HIGHWAY AND RESIDUAL NETWORKS LEARN UNROLLED ITERATIVE ESTIMATION |
d3075448 | Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks. The two main challenges are the large number of samples typically required, and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data. We address the first challenge by using value functions to substantially reduce the variance of policy gradient estimates at the cost of some bias, with an exponentially-weighted estimator of the advantage function that is analogous to TD(λ). We address the second challenge by using trust region optimization procedure for both the policy and the value function, which are represented by neural networks. Our approach yields strong empirical results on highly challenging 3D locomotion tasks, learning running gaits for bipedal and quadrupedal simulated robots, and learning a policy for getting the biped to stand up from starting out lying on the ground. In contrast to a body of prior work that uses hand-crafted policy representations, our neural network policies map directly from raw kinematics to joint torques. Our algorithm is fully model-free, and the amount of simulated experience required for the learning tasks on 3D bipeds corresponds to 1-2 weeks of real time.Published as a conference paper at ICLR 2016 λ ∈ [0, 1], the generalized advantage estimator (GAE). Related methods have been proposed in the context of online actor-critic methods(Kimura & Kobayashi, 1998;Wawrzyński, 2009). We provide a more general analysis, which is applicable in both the online and batch settings, and discuss an interpretation of our method as an instance of reward shaping(Ng et al., 1999), where the approximate value function is used to shape the reward.We present experimental results on a number of highly challenging 3D locomotion tasks, where we show that our approach can learn complex gaits using high-dimensional, general purpose neural network function approximators for both the policy and the value function, each with over 10 4 parameters. The policies perform torque-level control of simulated 3D robots with up to 33 state dimensions and 10 actuators.The contributions of this paper are summarized as follows:1. We provide justification and intuition for an effective variance reduction scheme for policy gradients, which we call generalized advantage estimation (GAE). While the formula has been proposed in prior work(Kimura & Kobayashi, 1998;Wawrzyński, 2009), our analysis is novel and enables GAE to be applied with a more general set of algorithms, including the batch trust-region algorithm we use for our experiments. 2. We propose the use of a trust region optimization method for the value function, which we find is a robust and efficient way to train neural network value functions with thousands of parameters. 3. By combining(1)and(2)above, we obtain an algorithm that empirically is effective at learning neural network policies for challenging control tasks. The results extend the state of the art in using reinforcement learning for high-dimensional continuous control. Videos are available at https://sites.google.com/site/gaepapersupp. | HIGH-DIMENSIONAL CONTINUOUS CONTROL USING GENERALIZED ADVANTAGE ESTIMATION |
d252715492 | Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing attention from the graph machine learning community | MULTI-TASK SELF-SUPERVISED GRAPH NEURAL NET- WORKS ENABLE STRONGER TASK GENERALIZATION |
d256597815 | In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas. Unfortunately, existing offline RL methods are often over-conservative, inevitably hurting generalization performance outside data distribution. In our study, one interesting observation is that deep Q functions approximate well inside the convex hull of training data. Inspired by this, we propose a new method, DOGE (Distance-sensitive Offline RL with better GEneralization). DOGE marries dataset geometry with deep function approximators in offline RL, and enables exploitation in generalizable OOD areas rather than strictly constraining policy within data distribution. Specifically, DOGE trains a state-conditioned distance function that can be readily plugged into standard actor-critic methods as a policy constraint. Simple yet elegant, our algorithm enjoys better generalization compared to state-of-the-art methods on D4RL benchmarks. Theoretical analysis demonstrates the superiority of our approach to existing methods that are solely based on data distribution or support constraints. Song. Uncertainty-based offline reinforcement learning with diversified q-ensemble. Advances in neural information processing systems, 34:7436-7447, 2021. Anonymous. Lightweight uncertainty for offline reinforcement learning via bayesian posterior. In , et al. Human-level control through deep reinforcement learning. nature, 518(7540):529-533, 2015. Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine. Accelerating online reinforcement learning with offline datasets. arXiv preprint arXiv:, et al. Mastering the game of go without human knowledge. nature, 550(7676):354-359, 2017. Masatoshi Uehara and Wen Sun. Pessimistic model-based offline reinforcement learning under partial Modayil. Deep reinforcement learning and the deadly triad. arXiv preprint arXiv:1812.02648, 2018.Vladimir N Vapnik and A Ya Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. . Offline reinforcement learning with soft behavior regularization. arXiv preprint arXiv:2110.07395, 2021. | Published as a conference paper at ICLR 2023 WHEN DATA GEOMETRY MEETS DEEP FUNCTION: GENERALIZING OFFLINE REINFORCEMENT LEARNING |
d195218789 | We investigate the robustness properties of image recognition models equipped with two features inspired by human vision, an explicit episodic memory and a shape bias, at the ImageNet scale. As reported in previous work, we show that an explicit episodic memory improves the robustness of image recognition models against small-norm adversarial perturbations under some threat models. It does not, however, improve the robustness against more natural, and typically larger, perturbations. Learning more robust features during training appears to be necessary for robustness in this second sense. We show that features derived from a model that was encouraged to learn global, shape-based representations (Geirhos et al., 2019) do not only improve the robustness against natural perturbations, but when used in conjunction with an episodic memory, they also provide additional robustness against adversarial perturbations. Finally, we address three important design choices for the episodic memory: memory size, dimensionality of the memories and the retrieval method. We show that to make the episodic memory more compact, it is preferable to reduce the number of memories by clustering them, instead of reducing their dimensionality. | Improving the robustness of ImageNet classifiers using elements of human visual cognition |
d233307445 | Despite -or maybe because of-their astonishing capacity to fit data, neural networks are believed to have difficulties extrapolating beyond training data distribution. This work shows that, for extrapolations based on finite transformation groups, a model's inability to extrapolate is unrelated to its capacity. Rather, the shortcoming is inherited from a learning hypothesis: Examples not explicitly observed with infinitely many training examples have underspecified outcomes in the learner's model. In order to endow neural networks with the ability to extrapolate over group transformations, we introduce a learning framework counterfactually-guided by the learning hypothesis that any group invariance to (known) transformation groups is mandatory even without evidence, unless the learner deems it inconsistent with the training data. Unlike existing invariance-driven methods for (counterfactual) extrapolations, this framework allows extrapolations from a single environment. Finally, we introduce sequence and image extrapolation tasks that validate our framework and showcase the shortcomings of traditional approaches.Published as a conference paper at ICLR 2021 consider a supervised learning task where the training data contains infinitely many sequences x (tr) =(A,B) associated with label y (tr) = C, but no examples of a sequence x (tr) =(B,A). If given a test example x (te) =(B,A), the hypothesis considers it to be out of distribution and the prediction P (Y (te) = C|X (te) = (B,A)) is undefined, since P (X (tr) = (B,A)) = 0. This happens regardless of a prior over P (X (tr) ). This unseen-is-underspecified learning hypothesis is not guaranteed to push neural networks to assume symmetric extrapolations without evidence.Contributions. Since symmetries are intrinsically tied to human single-environment extrapolation capabilities, this work explores a learning framework that modifies the learner's hypothesis space to allow symmetric extrapolation (over known groups) without evidence, while not losing valuable antisymmetric information if observed to predict the target variable in the training data. Formally, a symmetry is an invariance to transformations of a group, known as a G-invariance. In Theorem 1 we show that the counterfactual invariances needed for symmetry extrapolation -denoted Counterfactual G-invariances (CG-invariances)-are stronger than traditional G-invariances. Theorem 2, then, introduces a condition in the structural causal model where G-invariances of linear automorphism groups are safe to use as CG-invariances. With that, Theorem 3 defines a partial order over the appropriate invariant subspaces that we use to learn the correct G-invariances from a single environment without evidence, while retaining the ability to be sensitive to antisymmetries shown to be relevant in the training data. Finally, we introduce sequence and image counterfactual extrapolation tasks with experiments that validate the theoretical results and showcase the advantages of our approach. | Published as a conference paper at ICLR 2021 NEURAL NETWORKS FOR LEARNING COUNTERFAC- TUAL G-INVARIANCES FROM SINGLE ENVIRONMENTS |
d251648112 | Self-supervised visual representation learning has recently attracted significant research interest. While a common way to evaluate self-supervised representations is through transfer to various downstream tasks, we instead investigate the problem of measuring their interpretability, i.e. understanding the semantics encoded in raw representations. We formulate the latter as estimating the mutual information between the representation and a space of manually labelled concepts. To quantify this we introduce a decoding bottleneck: information must be captured by simple predictors, mapping concepts to clusters in representation space. This approach, which we call reverse linear probing, provides a single number sensitive to the semanticity of the representation. This measure is also able to detect when the representation contains combinations of concepts (e.g., "red apple") instead of just individual attributes ("red" and "apple" independently). Finally, we propose to use supervised classifiers to automatically label large datasets in order to enrich the space of concepts used for probing. We use our method to evaluate a large number of self-supervised representations, ranking them by interpretability, highlight the differences that emerge compared to the standard evaluation with linear probes and discuss several qualitative insights. Code at: https://github.com/iro-cp/ssl-qrp.arXiv:2209.03268v1 [cs.CV] 7 Sep 2022Published as a conference paper at ICLR 2022 the prediction accuracies of several independent classifiers, and there is no natural way of doing so (e.g., a simple average would not account for the different complexity of the different prediction tasks). Furthermore, the representation might be predictive of combinations of attributes, i.e. it might understand the concept of "red apple" without necessarily understanding the individual concepts, "red" and "apple". While it is in principle possible to test any combination of attributes via linear probing, most attribute combinations are too rare to generate significant statistics for this analysis.We propose a complementary assessment strategy to address these shortcomings. In contrast to linear probes, we start by considering the reverse prediction problem, mapping label vectors y(x) to representation vectors f (x). A key advantage is that the entire attribute vector is used for this mapping, which, as we show later, accounts for attribute combinations more effectively.Next, we consider the challenge of deriving a quantity that allows to compare representations based on the performance of these reverse predictors. Obviously a simple metric such as the average L 2 prediction error is meaningless as its magnitude would be affected by irrelevant factors such as the scale of the representation vectors. To solve this problem in a principled manner, we consider instead the mutual information between the concepts and quantized representation vectors. This approach, which we justify from the viewpoint of information theory, essentially measures whether the representation groups the data in a human-interpretable manner.We use our approach to evaluate a large number of self-supervised and supervised image representations. Importantly, we show that (a) all methods capture interpretable concepts that extend well beyond the underlying label distribution (e.g., ImageNet labels, which are not used for training), (b) while some clusters that form in the representation space are purely semantic (object-centric), others carry information about scenery, material, textures, or combined concepts, and (c) context matters. We also show that more performant methods recover more of the original label distribution, i.e. learn better "ImageNet concepts", and rely less on lower-level concepts. Quantitatively, we observe that our interpretability measure results in a similar but not identical ranking for state-of-the-art methods with clustering-based approaches generally producing more interpretable representations. | MEASURING THE INTERPRETABILITY OF UNSUPER- VISED REPRESENTATIONS VIA QUANTIZED REVERSE PROBING |
d252668615 | A generative model based on a continuous-time normalizing flow between any pair of base and target probability densities is proposed. The velocity field of this flow is inferred from the probability current of a time-dependent density that interpolates between the base and the target in finite time. Unlike conventional normalizing flow inference methods based the maximum likelihood principle, which require costly backpropagation through ODE solvers, our interpolant approach leads to a simple quadratic loss for the velocity itself which is expressed in terms of expectations that are readily amenable to empirical estimation. The flow can be used to generate samples from either the base or target, and to estimate the likelihood at any time along the interpolant. In addition, the flow can be optimized to minimize the path length of the interpolant density, thereby paving the way for building optimal transport maps. In situations where the base is a Gaussian density, we also show that the velocity of our normalizing flow can also be used to construct a diffusion model to sample the target as well as estimate its score. However, our approach shows that we can bypass this diffusion completely and work at the level of the probability flow with greater simplicity, opening an avenue for methods based solely on ordinary differential equations as an alternative to those based on stochastic differential equations. Benchmarking on density estimation tasks illustrates that the learned flow can match and surpass conventional continuous flows at a fraction of the cost, and compares well with diffusions on image generation on CIFAR-10 and ImageNet 32×32. The method scales ab-initio ODE flows to previously unreachable image resolutions, demonstrated up to 128 × 128. | Published as a conference paper at ICLR 2023 BUILDING NORMALIZING FLOWS WITH STOCHASTIC INTERPOLANTS |
d7253610 | Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive workloads of existing distributed deep learning systems. We introduce SparkNet, a framework for training deep networks in Spark. Our implementation includes a convenient interface for reading data from Spark RDDs, a Scala interface to the Caffe deep learning framework, and a lightweight multidimensional tensor library. Using a simple parallelization scheme for stochastic gradient descent, SparkNet scales well with the cluster size and tolerates very high-latency communication. Furthermore, it is easy to deploy and use with no parameter tuning, and it is compatible with existing Caffe models. We quantify the dependence of the speedup obtained by SparkNet on the number of machines, the communication frequency, and the cluster's communication overhead, and we benchmark our system's performance on the ImageNet dataset. Published as a conference paper at ICLR 2016 class Net { def Net(netParams: NetParams): Net def setTrainingData(data: Iterator[(NDArray,Int)]) def setValidationData(data: Iterator[(NDArray,Int)]) def train(numSteps: Int) def test(numSteps: Int): Float def setWeights(weights: WeightCollection) def getWeights(): WeightCollection } Listing 1: SparkNet API | SPARKNET: TRAINING DEEP NETWORKS IN SPARK |
d254636518 | Recent years have seen progress beyond domain-specific sound separation for speech or music towards universal sound separation for arbitrary sounds. Prior work on universal sound separation has investigated separating a target sound out of an audio mixture given a text query. Such text-queried sound separation systems provide a natural and scalable interface for specifying arbitrary target sounds. However, supervised text-queried sound separation systems require costly labeled audio-text pairs for training. Moreover, the audio provided in existing datasets is often recorded in a controlled environment, causing a considerable generalization gap to noisy audio in the wild. In this work, we aim to approach text-queried universal sound separation by using only unlabeled data. We propose to leverage the visual modality as a bridge to learn the desired audio-textual correspondence. The proposed CLIPSep model first encodes the input query into a query vector using the contrastive language-image pretraining (CLIP) model, and the query vector is then used to condition an audio separation model to separate out the target sound. While the model is trained on image-audio pairs extracted from unlabeled videos, at test time we can instead query the model with text inputs in a zero-shot setting, thanks to the joint language-image embedding learned by the CLIP model. Further, videos in the wild often contain off-screen sounds and background noise that may hinder the model from learning the desired audio-textual correspondence. To address this problem, we further propose an approach called noise invariant training for training a query-based sound separation model on noisy data. Experimental results show that the proposed models successfully learn text-queried universal sound separation using only noisy unlabeled videos, even achieving competitive performance against a supervised model in some settings. * | CLIPSEP: LEARNING TEXT-QUERIED SOUND SEPA- RATION WITH NOISY UNLABELED VIDEOS |
d231799762 | Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, a threat to these systems arises that adversarial attacks make CNNs vulnerable. Inaccurate diagnosis results make a negative influence on human healthcare. There is a need to investigate potential adversarial attacks to robustify deep medical diagnosis systems. On the other side, there are several modalities of medical images (e.g., CT, fundus, and endoscopic image) of which each type is significantly different from others. It is more challenging to generate adversarial perturbations for different types of medical images. In this paper, we propose an image-based medical adversarial attack method to consistently produce adversarial perturbations on medical images. The objective function of our method consists of a loss deviation term and a loss stabilization term. The loss deviation term increases the divergence between the CNN prediction of an adversarial example and its ground truth label. Meanwhile, the loss stabilization term ensures similar CNN predictions of this example and its smoothed input. From the perspective of the whole iterations for perturbation generation, the proposed loss stabilization term exhaustively searches the perturbation space to smooth the single spot for local optimum escape. We further analyze the KL-divergence of the proposed loss function and find that the loss stabilization term makes the perturbations updated towards a fixed objective spot while deviating from the ground truth. This stabilization ensures the proposed medical attack effective for different types of medical images while producing perturbations in small variance. Experiments on several medical image analysis benchmarks including the recent COVID-19 dataset show the stability of the proposed method. | Published as a conference paper at ICLR 2021 STABILIZED MEDICAL IMAGE ATTACKS |
d238354388 | In this article, we investigate the spectral behavior of random features kernel matrices of the type K = E w [σ(w T x i )σ(w T x j )] n i,j=1 , with nonlinear function σ(·), data x 1 , . . . , x n ∈ R p , and random projection vector w ∈ R p having i.i.d. entries. In a high-dimensional setting where the number of data n and their dimension p are both large and comparable, we show, under a Gaussian mixture model for the data, that the eigenspectrum of K is independent of the distribution of the i.i.d. (zero-mean and unit-variance) entries of w, and only depends on σ(·) via its (generalized) Gaussian momentsAs a result, for any kernel matrix K of the form above, we propose a novel random features technique, called Ternary Random Feature (TRF), that (i) asymptotically yields the same limiting kernel as the original K in a spectral sense and (ii) can be computed and stored much more efficiently, by wisely tuning (in a data-dependent manner) the function σ and the random vector w, both taking values in {−1, 0, 1}. The computation of the proposed random features requires no multiplication, and a factor of b times less bits for storage compared to classical random features such as random Fourier features, with b the number of bits to store full precision values. Besides, it appears in our experiments on real data that the substantial gains in computation and storage are accompanied with somewhat improved performances compared to state-of-the-art random features compression/quantization methods. | RANDOM MATRICES IN SERVICE OF ML FOOTPRINT: TERNARY RANDOM FEATURES WITH NO PERFOR- MANCE LOSS |
d254535649 | Large-scale diffusion models have achieved state-of-the-art results on text-to-image synthesis (T2I) tasks. Despite their ability to generate high-quality yet creative images, we observe that attribution-binding and compositional capabilities are still considered major challenging issues, especially when involving multiple objects. Attribute-binding requires the model to associate objects with the correct attribute descriptions, and compositional skills require the model to combine and generate multiple concepts into a single image. In this work, we improve these two aspects of T2I models to achieve more accurate image compositions. To do this, we incorporate linguistic structures with the diffusion guidance process based on the controllable properties of manipulating cross-attention layers in diffusion-based T2I models. We observe that keys and values in cross-attention layers have strong semantic meanings associated with object layouts and content. Therefore, by manipulating the cross-attention representations based on linguistic insights, we can better preserve the compositional semantics in the generated image. Built upon Stable Diffusion, a SOTA T2I model, our structured cross-attention design is efficient that requires no additional training samples. We achieve better compositional skills in qualitative and quantitative results, leading to a significant 5-8% advantage in head-to-head user comparison studies. Lastly, we conduct an in-depth analysis to reveal potential causes of incorrect image compositions and justify the properties of cross-attention layers in the generation process. | Published as a conference paper at ICLR 2023 TRAINING-FREE STRUCTURED DIFFUSION GUIDANCE FOR COMPOSITIONAL TEXT-TO-IMAGE SYNTHESIS |
d13123084 | In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions. This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training. Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations. We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training. Finally, we demonstrate good tolerance to incorrect labels. | Published as a conference paper at ICLR 2017 TEMPORAL ENSEMBLING FOR SEMI-SUPERVISED LEARNING |
d252815486 | Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method. Alas, the computational cost associated with Bayesian linear models constrains this method's application to small networks, small output spaces and small datasets. We address this limitation by introducing a scalable sample-based Bayesian inference method for conjugate Gaussian multi-output linear models, together with a matching method for hyperparameter (regularisation strength) selection. Furthermore, we use a classic feature normalisation method, the g-prior, to resolve a previously highlighted pathology of the linearised Laplace method. Together, these contributions allow us to perform linearised neural network inference with ResNet-18 on CIFAR100 (11M parameters, 100 outputs × 50k datapoints), with ResNet-50 on Imagenet (50M parameters, 1000 outputs × 1.2M datapoints) and with a U-Net on a high-resolution tomographic reconstruction task (2M parameters, 251k output dimensions).Published as a conference paper at ICLR 2023 classification problems, through the use of the Laplace approximation. In the context of linearised NNs, our approach also differs from previous work in that it avoids instantiating the full NN Jacobian matrix, an operation requiring as many backward passes as output dimensions in the network.We demonstrate the strength of our inference technique in the context of the linearised Laplace procedure for image classification on CIFAR100 (100 classes × 50k datapoints) and Imagenet (1000 classes × 1.2M datapoints) using an 11M parameter ResNet-18 and a 25M parameter ResNet-50, respectively. We also consider a high-resolution (251k pixel) tomographic reconstruction (regression) task with a 2M parameter U-Net. In tackling these, we encounter a pathology in the M-step of the procedure first highlighted byAntorán et al. (2022c): the standard objective therein is illdefined when the NN contains normalisation layers. Rather than using the solution proposed inAntorán et al. (2022c), which introduces more hyperparameters, we show that a standard featurenormalisation method, the g-prior (Zellner, 1986;Minka, 2000), resolves this pathology. For the tomographic reconstruction task, the regression problem requires a dual-form formulation of our E-step; interestingly, we show that this is equivalent to an optimisation viewpoint on Matheron's rule(Journel & Huijbregts, 1978;Wilson et al., 2020), a connection we believe to be novel. | SAMPLING-BASED INFERENCE FOR LARGE LINEAR MODELS WITH APPLICATION TO LINEARISED LAPLACE |
d252967782 | It is expensive to collect training data for every possible domain that a vision model may encounter when deployed. We instead consider how simply verbalizing the training domain (e.g. "photos of birds") as well as domains we want to extend to but do not have data for (e.g. "paintings of birds") can improve robustness. Using a multimodal model with a joint image and language embedding space, our method LADS learns a transformation of the image embeddings from the training domain to each unseen test domain, while preserving task relevant information. Without using any images from the unseen test domain, we show that over the extended domain containing both training and unseen test domains, LADS outperforms standard fine-tuning and ensemble approaches over a suite of four benchmarks targeting domain adaptation and dataset bias. Code is available at https://github.com/lisadunlap/LADS."Stop Sign" similarityExtended DomainI have photos of sunny road signs, but I want do well on snowy road signs.Latent Augmentation using Domain descriptionS A mix of training + unseen domain samples Joint Vision & Language Embedding SpaceTraining Domain Unseen Domain≈Training Domain"Snowy" similarity "Sunny" similarity "Snowy Tree" ≈Figure 1: Consider a model trained to recognize road signs in sunny weather. We aim to extend to a new domain of snowy weather. Our method LADS (Latent Augmentation using Domain descrip-tionS) leverages a multimodal model's knowledge of the classes and the domain shift verbalized in natural language ("sunny" to "snowy") to train an augmentation network without any samples from the unseen test domain. This network is used to translate multimodal image embeddings from the training domain to the unseen test domain, while retaining class-relevant information. Then, real and augmented embeddings are used jointly to train a classifier. | Published as a conference paper at ICLR 2023 USING LANGUAGE TO EXTEND TO UNSEEN DOMAINS |
d211146200 | We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-training quantization. By training quantization-ready networks, our approach enables storing a single set of weights that can be quantized ondemand to different bit-widths as energy and memory requirements of the application change. Unlike quantization-aware training using the straight-through estimator that only targets a specific bit-width and requires access to training data and pipeline, our regularization-based method paves the way for "on the fly" posttraining quantization to various bit-widths. We show that by modeling quantization as a ∞ -bounded perturbation, the first-order term in the loss expansion can be regularized using the 1 -norm of gradients. We experimentally validate the effectiveness of our regularization scheme on different architectures on CIFAR-10 and ImageNet datasets. * Work done during internship at Qualcomm AI Research † Qualcom AI Research is an initiative of Qualcomm Technologies, Inc. | Published as a conference paper at ICLR 2020 GRADIENT 1 REGULARIZATION FOR QUANTIZATION ROBUSTNESS |
d256358823 | Guided diffusion is a technique for conditioning the output of a diffusion model at sampling time without retraining the network for each specific task. One drawback of diffusion models, however, is their slow sampling process. Recent techniques can accelerate unguided sampling by applying high-order numerical methods to the sampling process when viewed as differential equations. On the contrary, we discover that the same techniques do not work for guided sampling, and little has been explored about its acceleration. This paper explores the culprit of this problem and provides a solution based on operator splitting methods, motivated by our key finding that classical high-order numerical methods are unsuitable for the conditional function. Our proposed method can re-utilize the high-order methods for guided sampling and can generate images with the same quality as a 250step DDIM baseline using 32-58% less sampling time on ImageNet256. We also demonstrate usage on a wide variety of conditional generation tasks, such as textto-image generation, colorization, inpainting, and super-resolution. | Published as a conference paper at ICLR 2023 ACCELERATING GUIDED DIFFUSION SAMPLING WITH SPLITTING NUMERICAL METHODS |
d256503815 | It is well known that the finite step-size (h) in Gradient Descent (GD) implicitly regularizes solutions to flatter minima. A natural question to ask is "Does the momentum parameter β play a role in implicit regularization in Heavy-ball (H.B) momentum accelerated gradient descent (GD+M)?" To answer this question, first, we show that the discrete H.B momentum update (GD+M) follows a continuous trajectory induced by a modified loss, which consists of an original loss and an implicit regularizer. Then, we show that this implicit regularizer for (GD+M) is stronger than that of (GD) by factor of ( 1+β 1−β ), thus explaining why (GD+M) shows better generalization performance and higher test accuracy than (GD). Furthermore, we extend our analysis to the stochastic version of gradient descent with momentum (SGD+M) and characterize the continuous trajectory of the update of (SGD+M) in a pointwise sense. We explore the implicit regularization in (SGD+M) and (GD+M) through a series of experiments validating our theory. | Published as a conference paper at ICLR 2023 IMPLICIT REGULARIZATION IN HEAVY-BALL MOMEN- TUM ACCELERATED STOCHASTIC GRADIENT DESCENT |
d257079089 | The data management of large companies often prioritize more recent data, as a source of higher accuracy prediction than outdated data. For example, the Facebook data policy retains user search histories for 6 months while the Google data retention policy states that browser information may be stored for up to 9 months. These policies are captured by the sliding window model, in which only the most recent W statistics form the underlying dataset.In this paper, we consider the problem of privately releasing the L 2 -heavy hitters in the sliding window model, which include L p -heavy hitters for p ≤ 2 and in some sense are the strongest possible guarantees that can be achieved using polylogarithmic space, but cannot be handled by existing techniques due to the sub-additivity of the L 2 norm. Moreover, existing non-private sliding window algorithms use the smooth histogram framework, which has high sensitivity.To overcome these barriers, we introduce the first differentially private algorithm for L 2 -heavy hitters in the sliding window model by initiating a number of L 2 -heavy hitter algorithms across the stream with significantly lower threshold. Similarly, we augment the algorithms with an approximate frequency tracking algorithm with significantly higher accuracy. We then use smooth sensitivity and statistical distance arguments to show that we can add noise proportional to an estimation of the L 2 norm. To the best of our knowledge, our techniques are the first to privately release statistics that are related to a sub-additive function in the sliding window model, and may be of independent interest to future differentially private algorithmic design in the sliding window model. | Differentially Private L 2 -Heavy Hitters in the Sliding Window Model |
d224893 | Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with limited power budgets. To solve this problem, we propose Trained Ternary Quantization (TTQ), a method that can reduce the precision of weights in neural networks to ternary values. This method has very little accuracy degradation and can even improve the accuracy of some models (32, 44, 56-layer ResNet) on CIFAR-10 and AlexNet on ImageNet. And our AlexNet model is trained from scratch, which means it's as easy as to train normal full precision model. We highlight our trained quantization method that can learn both ternary values and ternary assignment. During inference, only ternary values (2-bit weights) and scaling factors are needed, therefore our models are nearly 16× smaller than fullprecision models. Our ternary models can also be viewed as sparse binary weight networks, which can potentially be accelerated with custom circuit. Experiments on CIFAR-10 show that the ternary models obtained by trained quantization method outperform full-precision models of ResNet-32,44,56 by 0.04%, 0.16%, 0.36%, respectively. On ImageNet, our model outperforms full-precision AlexNet model by 0.3% of Top-1 accuracy and outperforms previous ternary models by 3%. * Work done while at Stanford CVA lab. | Published as a conference paper at ICLR 2017 TRAINED TERNARY QUANTIZATION |
d245650405 | The neural Hawkes process(Mei & Eisner, 2017) is a generative model of irregularly spaced sequences of discrete events. To handle complex domains with many event types, Mei et al. (2020a) further consider a setting in which each event in the sequence updates a deductive database of facts (via domain-specific pattern-matching rules); future events are then conditioned on the database contents. They show how to convert such a symbolic system into a neuro-symbolic continuous-time generative model, in which each database fact and possible event has a time-varying embedding that is derived from its symbolic provenance. In this paper, we modify both models, replacing their recurrent LSTM-based architectures with flatter attention-based architectures (Vaswani et al., 2017), which are simpler and more parallelizable. This does not appear to hurt our accuracy, which is comparable to or better than that of the original models as well as (where applicable) previous attention-based methods(Zuo et al., 2020;Zhang et al., 2020a). | Published as a conference paper at ICLR 2022 TRANSFORMER EMBEDDINGS OF IRREGULARLY SPACED EVENTS AND THEIR PARTICIPANTS |
d257365673 | Deep regression networks are widely used to tackle the problem of predicting a continuous value for a given input. Task-specialized approaches for training regression networks have shown significant improvement over generic approaches, such as direct regression. More recently, a generic approach based on regression by binary classification using binary-encoded labels has shown significant improvement over direct regression. The space of label encodings for regression is large. Lacking heretofore have been automated approaches to find a good label encoding for a given application. This paper introduces Regularized Label Encoding Learning (RLEL) for end-to-end training of an entire network and its label encoding. RLEL provides a generic approach for tackling regression. Underlying RLEL is our observation that the search space of label encodings can be constrained and efficiently explored by using a continuous search space of real-valued label encodings combined with a regularization function designed to encourage encodings with certain properties. These properties balance the probability of classification error in individual bits against error correction capability. Label encodings found by RLEL result in lower or comparable errors to manually designed label encodings. Applying RLEL results in 10.9% and 12.4% improvement in Mean Absolute Error (MAE) over direct regression and multiclass classification, respectively. Our evaluation demonstrates that RLEL can be combined with off-the-shelf feature extractors and is suitable across different architectures, datasets, and tasks. Code is available at https://github.com/ubc-aamodt-group/RLEL_regression.Reproducibility:We have provided details on training hyperparameters, experimental setup, and network architectures in Appendix A.3. Code is available at https://github.com/ ubc-aamodt-group/RLEL_regression. We have provided the training and inference code with trained models.Code of Ethics: Autonomous robotics and vehicles are major applications of deep regression networks. Thus improvement of regression tasks can accelerate the progress of these fields, which may lead to some negative societal impacts such as loss of jobs, privacy, and ethical concerns.REFERENCESErin L. Allwein, Robert E. Schapire, and Yoram Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. consistent ordinal regression for neural networks with application to age estimation. A novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res., 18(1): 6765-6816, jan 2017. ISSN 1532-4435. William Libaw and Leonard Craig. A photoelectric decimal-coded shaft digitizer. Electronic . Ordinal regression with multiple output CNN for age estimation. | Published as a conference paper at ICLR 2023 LEARNING LABEL ENCODINGS FOR DEEP REGRES- SION |
d222091019 | Adversarial training (AT) is one of the most effective strategies for promoting model robustness. However, recent benchmarks show that most of the proposed improvements on AT are less effective than simply early stopping the training procedure. This counter-intuitive fact motivates us to investigate the implementation details of tens of AT methods. Surprisingly, we find that the basic training settings (e.g., weight decay, learning rate schedule, etc.) used in these methods are highly inconsistent, which could largely affect the model performance as shown in our experiments. For example, a slightly different value of weight decay can reduce the model robust accuracy by more than 7%, which is probable to override the potential promotion induced by the proposed methods. In this work, we provide comprehensive evaluations on the effects of basic training tricks and hyperparameter settings for adversarially trained models. We provide a reasonable baseline setting and re-implement previous defenses to achieve new state-of-the-art results 1 . | Preprint and under review BAG OF TRICKS FOR ADVERSARIAL TRAINING |
d232417695 | Greedy-GQ is a value-based reinforcement learning (RL) algorithm for optimal control. Recently, the finite-time analysis of Greedy-GQ has been developed under linear function approximation and Markovian sampling, and the algorithm is shown to achieve an -stationary point with a sample complexity in the order of O( −3 ). Such a high sample complexity is due to the large variance induced by the Markovian samples. In this paper, we propose a variance-reduced Greedy-GQ (VR-Greedy-GQ) algorithm for off-policy optimal control. In particular, the algorithm applies the SVRG-based variance reduction scheme to reduce the stochastic variance of the two time-scale updates. We study the finite-time convergence of VR-Greedy-GQ under linear function approximation and Markovian sampling and show that the algorithm achieves a much smaller bias and variance error than the original Greedy-GQ. In particular, we prove that VR-Greedy-GQ achieves an improved sample complexity that is in the order of O( −2 ). We further compare the performance of VR-Greedy-GQ with that of Greedy-GQ in various RL experiments to corroborate our theoretical findings. . SVRG for policy evaluation with fewer gradient evaluations. arXiv:1906.03704, 2019. Theodore J Perkins and Doina Precup. A convergent form of approximate policy iteration. In Proc. . Mastering the game of Go with deep neural networks and tree search. nature, 529(7587):484, 2016. Rayadurgam Srikant and Lei Ying. Finite-time error bounds for linear stochastic approximation andtd . A convergent O(n) temporal-difference algorithm for off-policy learning with linear function approximation. . Variance-reduced q-learning is minimax optimal. arXiv:1906.04697, 06 2019. Gang Wang, Bingcong Li, and Georgios B. Giannakis. A multistep lyapunov approach for finitetime analysis of biased stochastic approximation. arXiv:1909.04299, 2020. Yue Wang and Shaofeng Zou. Finite-sample analysis of greedy-gq with linear function approximation under markovian noise. . Finite sample analysis of the gtd policy evaluation algorithms in markov setting. In . A finite time analysis of two time-scale actor critic methods. arXiv preprint arXiv:2005.01350, 2020. Pan Xu and Quanquan Gu. A finite-time analysis of q-learning with neural network function approximation. arXiv preprint arXiv:1912.04511, 2019. | GREEDY-GQ WITH VARIANCE REDUCTION: FINITE- TIME ANALYSIS AND IMPROVED COMPLEXITY |
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