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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 ... | 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 decom... | 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 localiz... | 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)... | 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 learn... | 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 conceptual... | 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 mod... | 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 techni... | 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 d... | 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 guara... | 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... | 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 an... | 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 ta... | 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 c... | 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 res... | 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 lea... | 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... | 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... | 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 pe... | 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 so... | 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 ... | 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 probl... | 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 connectio... | 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... | 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 ... | 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 multip... | 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 s... | 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, propos... | 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. Howev... | 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 ... | 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 in... | 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 ... | 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 di... | 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 lengt... | 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 o... | 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 reason... | 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 realizat... | 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 Valu... | 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... | 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 102... | 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 corpor... | 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, su... | 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 ... | 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 ... | 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 d... | 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... | 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 model... | 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 th... | 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 an... | 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... | 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... | 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... | 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 (... | 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 requir... | 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 b... | 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 possib... | 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 ... | 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... | 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 ... | 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 utiliz... | 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 define... | 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 giv... | 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... | 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 g... | 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 propo... | 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... | 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 ... | 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 s... | 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 o... | 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 ... | 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... | 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 ... | |
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 irregul... | 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 ... | 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 be... | 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... | 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,... | 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 agains... | 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,... | 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 en... | 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... | 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 ... | 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 ... | 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 adver... | 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 ... | 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 mult... | 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 differen... | 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... | 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.... | 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... | 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 ... | 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... | 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... | 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 ... | 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-m... | 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 ... | 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 ... | 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 )... | GREEDY-GQ WITH VARIANCE REDUCTION: FINITE- TIME ANALYSIS AND IMPROVED COMPLEXITY |
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