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d259298735 | Repeated parameter sharing in federated learning causes significant information leakage about private data, thus defeating its main purpose: data privacy. Mitigating the risk of this information leakage, using state of the art differentially private algorithms, also does not come for free. Randomized mechanisms can pre... | Published as a conference paper at ICLR 2023 SHARE YOUR REPRESENTATION ONLY: GUARANTEED IMPROVEMENT OF THE PRIVACY-UTILITY TRADEOFF IN FEDERATED LEARNING |
d222125298 | We provide a general self-attention formulation to impose group equivariance to arbitrary symmetry groups. This is achieved by defining positional encodings that are invariant to the action of the group considered. Since the group acts on the positional encoding directly, group equivariant self-attention networks (GSA-... | ArXiv preprint. Under review. GROUP EQUIVARIANT STAND-ALONE SELF-ATTENTION FOR VISION |
d256105320 | The vast amount of health data has been continuously collected for each patient, providing opportunities to support diverse healthcare predictive tasks such as seizure detection and hospitalization prediction. Existing models are mostly trained on other patients' data and evaluated on new patients. Many of them might s... | MA N YDG: MANY-DOMAIN GENERALIZATION FOR HEALTHCARE APPLICATIONS |
d232233677 | The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs). Different from existing works, in this paper, we highlight two new characteristics of adversarial examples from the channel-wise activation perspective: 1) the acti... | Published as a conference paper at ICLR 2021 IMPROVING ADVERSARIAL ROBUSTNESS VIA CHANNEL-WISE ACTIVATION SUPPRESSING |
d238634169 | The power of a generalization system follows directly from its biases"(Mitchell 1980). Today, CNNs are incredibly powerful generalisation systems-but to what degree have we understood how their inductive bias influences model decisions?We here attempt to disentangle the various aspects that determine how a model decide... | TRIVIAL OR IMPOSSIBLE-DICHOTOMOUS DATA DIFFICULTY MASKS MODEL DIFFERENCES (ON IMAGENET AND BEYOND) |
d13022595 | Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their ... | Published as a conference paper at ICLR 2016 PRIORITIZED EXPERIENCE REPLAY |
d10465751 | Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper, we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather ... | The Manifold of Human Emotions |
d57573823 | Variational Autoencoder (VAE), a simple and effective deep generative model, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. However, recent studies demonstrate that, when equipped with expressive generative distributions (aka. decoders), VAE suff... | MAE: MUTUAL POSTERIOR-DIVERGENCE REGULAR- IZATION FOR VARIATIONAL AUTOENCODERS |
d257496411 | In this work, we present the Bregman Alternating Projected Gradient (BAPG) method, a single-loop algorithm that offers an approximate solution to the Gromov-Wasserstein (GW) distance. We introduce a novel relaxation technique that balances accuracy and computational efficiency, albeit with some compromises in the feasi... | A CONVERGENT SINGLE-LOOP ALGORITHM FOR RE- LAXATION OF GROMOV-WASSERSTEIN IN GRAPH DATA |
d159181884 | Comparative Law in Asia: The Case for Intra-Asia Intensification | |
d248834505 | Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples.... | Published as a conference paper at ICLR 2022 NEURAL PROGRAM SYNTHESIS WITH QUERY |
d12538994 | We consider the task of learning to extract motion from videos. To this end, we show that the detection of spatial transformations can be viewed as the detection of synchrony between the image sequence and a sequence of features undergoing the motion we wish to detect. We show that learning about synchrony is possible ... | Learning to encode motion using spatio-temporal synchrony |
d235652267 | We empirically show that the test error of deep networks can be estimated by training the same architecture on the same training set but with two different runs of Stochastic Gradient Descent (SGD), and then measuring the disagreement rate between the two networks on unlabeled test data. This builds on -and is a strong... | Published as a conference paper at ICLR 2022 ASSESSING GENERALIZATION VIA DISAGREEMENT |
d237605129 | Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget. Common solutions uniformly downsample the input images to meet memory constraints, assuming all pixels are equally informative. In this work,... | Published as a conference paper at ICLR 2022 LEARNING TO DOWNSAMPLE FOR SEGMENTATION OF ULTRA-HIGH RESOLUTION IMAGES |
d257427479 | Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a vast majority of scenes where object geometries are complex or unknown. In this work, we aim to identify parameters characterizing a physical... | PAC-NERF: PHYSICS AUGMENTED CONTINUUM NEURAL RADIANCE FIELDS FOR GEOMETRY- AGNOSTIC SYSTEM IDENTIFICATION |
d238634635 | The pretrain-finetune paradigm has shown outstanding performance on many applications of deep learning, where a model is pre-trained on an upstream large dataset (e.g. ImageNet), and is then fine-tuned to different downstream tasks. Though for most cases, the pre-training stage is conducted based on supervised methods,... | Published as a conference paper at ICLR 2022 RETHINKING SUPERVISED PRE-TRAINING FOR BETTER DOWNSTREAM TRANSFERRING |
d252815793 | The ideally disentangled latent space in GAN involves the global representation of latent space with semantic attribute coordinates. In other words, considering that this disentangled latent space is a vector space, there exists the global semantic basis where each basis component describes one attribute of generated i... | Published as a conference paper at ICLR 2023 FINDING THE GLOBAL SEMANTIC REPRESENTATION IN GAN THROUGH FRÉCHET MEAN |
d9996719 | Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on Microsoft COCO, we compare our model with several ba... | Published as a conference paper at ICLR 2016 GENERATING IMAGES FROM CAPTIONS WITH ATTENTION |
d225068784 | Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent's ability to query the environment for transitions and rewards is effectively unlimited.However, in many practical applications, the situation is reversed: an agent may have access to large amounts of undir... | OPAL: OFFLINE PRIMITIVE DISCOVERY FOR ACCEL-ERATING OFFLINE REINFORCEMENT LEARNING |
d2181703 | Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting. The performance depends critically on the amount of labeled examples, and in current practice the labels are assumed to be unambiguous and a... | Under review as a conference paper at ICLR 2015 TRAINING DEEP NEURAL NETWORKS ON NOISY LABELS WITH BOOTSTRAPPING |
d7823468 | This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as ... | Grid Long Short-Term Memory |
d257771836 | Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set. However, in practice these annotations can differ systematically in the way they are generated, for example through the use of different labeling tool... | Published as a conference paper at ICLR 2023 THAT LABEL'S GOT STYLE: HANDLING LABEL STYLE BIAS FOR UNCERTAIN IMAGE SEGMENTATION |
d13890001 | The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images. To tackle... | Published as a conference paper at ICLR 2017 NEURAL PHOTO EDITING WITH INTROSPECTIVE AD- VERSARIAL NETWORKS |
d249626510 | Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models (DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising process as in the original DDIM, we examine the mechanism of DDIM from a numerical perspective. We discover that the DDIM can be obtained by us... | Published as a conference paper at ICLR 2023 GDDIM: GENERALIZED DENOISING DIFFUSION IM- PLICIT MODELS |
d85457862 | We study data-driven methods for community detection in graphs. This estimation problem is typically formulated in terms of the spectrum of certain operators, as well as via posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the Stochastic Block Model, recent res... | Community Detection with Graph Neural Networks |
d256231177 | Diffusion models have emerged as powerful generative models in the text-toimage domain. This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments. Human behaviour is stochastic and multimodal, with structured correlations between action dimensions. Mea... | Published as a conference paper at ICLR 2023 IMITATING HUMAN BEHAVIOUR WITH DIFFUSION MODELS |
d239024453 | Despite rapid advances in continual learning, a large body of research is devoted to improving performance in the existing setups. While a handful of work do propose new continual learning setups, they still lack practicality in certain aspects. For better practicality, we first propose a novel continual learning setup... | ONLINE CONTINUAL LEARNING ON CLASS INCRE- MENTAL BLURRY TASK CONFIGURATION WITH ANY- TIME INFERENCE |
d4712464 | The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus on the transfer scenario where the dynamics among tasks are the same, but their goals differ. Although general value function(Sutton et al., 2011)has been shown to be useful for kno... | Workshop track -ICLR 2018 UNIVERSAL SUCCESSOR REPRESENTATIONS FOR TRANSFER REINFORCEMENT LEARNING |
d260499190 | We consider the natural problem of learning a ReLU network from queries, which was recently remotivated by model extraction attacks. In this work, we present a polynomial-time algorithm that can learn a depth-two ReLU network from queries under mild general position assumptions. We also present a polynomial-time algori... | Published as a conference paper at ICLR 2023 AN EXACT POLY-TIME MEMBERSHIP-QUERIES AL- GORITHM FOR EXTRACTING A THREE-LAYER RELU NETWORK |
d246996668 | Few-shot learning is an established topic in natural images for years, but few work is attended to histology images, which is of high clinical value since well-labeled datasets and rare abnormal samples are expensive to collect. Here, we facilitate the study of few-shot learning in histology images by setting up three ... | Published as a conference paper at ICLR 2022 TOWARDS BETTER UNDERSTANDING AND BETTER GENERALIZATION OF FEW-SHOT CLASSIFICATION IN HISTOLOGY IMAGES WITH CONTRASTIVE LEARNING |
d225039984 | Trajectory prediction is a critical part of many AI applications, for example, the safe operation of autonomous vehicles. However, current methods are prone to making inconsistent and physically unrealistic predictions. We leverage insights from fluid dynamics to overcome this limitation by considering internal symmetr... | Preprint. Under review. TRAJECTORY PREDICTION USING EQUIVARIANT CON- TINUOUS CONVOLUTION |
d67855770 | The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations. In this paper, we take a ... | HARMONIC UNPAIRED IMAGE-TO-IMAGE TRANSLA- TION |
d212718244 | We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under changes to the gender and/or ethnicity of the applicant. We formalize this ... | Published as a conference paper at ICLR 2020 TRAINING INDIVIDUALLY FAIR ML MODELS WITH SENSITIVE SUBSPACE ROBUSTNESS |
d15454326 | Why Size Matters: Feature Coding as Nyström Sampling | |
d246473016 | Recent work suggests that feature constraints in the training datasets of deep neural networks (DNNs) drive robustness to adversarial noise . The representations learned by such adversarially robust networks have also been shown to be more human perceptually-aligned than non-robust networks via image manipulations . De... | Published as a conference paper at ICLR 2022 FINDING BIOLOGICAL PLAUSIBILITY FOR ADVER- SARIALLY ROBUST FEATURES VIA METAMERIC TASKS |
d246035679 | Arguably the most fundamental question in the theory of generative adversarial networks (GANs) is to understand to what extent GANs can actually learn the underlying distribution. Theoretical and empirical evidence (see e.g. [ARZ18]) suggests local optimality of the empirical training objective is insufficient. Yet, it... | Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs * |
d256662429 | The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A recent alternative formulation instead treats federated learning as a distributed inference problem, whe... | Published as a conference paper at ICLR 2023 FEDERATED LEARNING AS VARIATIONAL INFERENCE: A SCALABLE EXPECTATION PROPAGATION APPROACH |
d58004595 | This paper proposes a representational model for grid cells. In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or displacement of the agent is represented by a matrix that transforms the vector. Each component of the vector is a unit or a cell. The mode... | LEARNING GRID CELLS AS VECTOR REPRESENTA- TION OF SELF-POSITION COUPLED WITH MATRIX REPRESENTATION OF SELF-MOTION |
d258298063 | MOBA games, e.g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far. However, these AI systems mainly focus on how to compete with humans, less on exploring how to collaborate with humans. To this en... | Published as a conference paper at ICLR 2023 TOWARDS EFFECTIVE AND INTERPRETABLE HUMAN-AGENT COLLABORATION IN MOBA GAMES: A COMMUNICATION PERSPECTIVE |
d204824219 | Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporat... | Published as a conference paper at ICLR 2020 WEAKLY SUPERVISED DISENTANGLEMENT WITH GUARANTEES |
d53325983 | Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection fo... | KERNEL CHANGE-POINT DETECTION WITH AUXIL- IARY DEEP GENERATIVE MODELS |
d259298789 | The dominant text generation models compose the output by sequentially selecting words from a fixed vocabulary.In this paper, we formulate text generation as progressively copying text segments (e.g., words or phrases) from an existing text collection.We compute the contextualized representations of meaningful text seg... | |
d232075995 | Compared to traditional visual question answering, video-grounded dialogues require additional reasoning over dialogue context to answer questions in a multiturn setting. Previous approaches to video-grounded dialogues mostly use dialogue context as a simple text input without modelling the inherent information flows a... | Published as a conference paper at ICLR 2021 LEARNING REASONING PATHS OVER SEMANTIC GRAPHS FOR VIDEO-GROUNDED DIALOGUES |
d252968153 | We consider a setting that a model needs to adapt to a new domain under distribution shifts, given that only unlabeled test samples from the new domain are accessible at test time. A common idea in most of the related works is constructing pseudolabels for the unlabeled test samples and applying gradient descent (GD) t... | Published as a conference paper at ICLR 2023 TOWARDS UNDERSTANDING GD WITH HARD AND CONJUGATE PSEUDO-LABELS FOR TEST-TIME ADAP- TATION |
d17140888 | The problem of detecting and recognizing text in natural scenes has proved to be more challenging than its counterpart in documents, with most of the previous work focusing on a single part of the problem. In this work, we propose new solutions to the character and word recognition problems and then show how to combine... | End-to-End Text Recognition with Hybrid HMM Maxout Models Ouais Alsharif |
d2780493 | Layer-sequential unit-variance (LSUV) initialization -a simple method for weight initialization for deep net learning -is proposed. The method consists of the two steps. First, pre-initialize weights of each convolution or inner-product layer with orthonormal matrices. Second, proceed from the first to the final layer,... | ALL YOU NEED IS A GOOD INIT |
d2134321 | Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce "deep compression", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduc... | Published as a conference paper at ICLR 2016 DEEP COMPRESSION: COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING, TRAINED QUANTIZATION AND HUFFMAN CODING |
d214536507 | We introduce LiPopt, a polynomial optimization framework for computing increasingly tighter upper bounds on the Lipschitz constant of neural networks. The underlying optimization problems boil down to either linear (LP) or semidefinite (SDP) programming. We show how to use the sparse connectivity of a network, to signi... | LIPSCHITZ CONSTANT ESTIMATION OF NEURAL NET- WORKS VIA SPARSE POLYNOMIAL OPTIMIZATION |
d3509328 | In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions. It directly models the probability distribution of generating a word given previous words and an image. Image captions are generated according to this distribution. The model consists of two sub-networks: ... | DEEP CAPTIONING WITH MULTIMODAL RECURRENT NEURAL NETWORKS (M-RNN) |
d257377961 | Controlling agents remotely with deep reinforcement learning (DRL) in the real world is yet to come. One crucial stepping stone is to devise RL algorithms that are robust in the face of dropped information from corrupted communication or malfunctioning sensors. Typical RL methods usually require considerable online int... | Published as a conference paper at ICLR 2023 DECISION TRANSFORMER UNDER RANDOM FRAME DROPPING |
d257020074 | Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference. However, finding appropriate topological structures for specific phylogenetic inference tasks often requires significant design effort and domain expertise. In this paper, we propose a novel structural representati... | Published as a conference paper at ICLR 2023 LEARNABLE TOPOLOGICAL FEATURES FOR PHYLOGE- NETIC INFERENCE VIA GRAPH NEURAL NETWORKS |
d11217889 | While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks ... | Under review as a conference paper at ICLR 2018 CERTIFIED DEFENSES AGAINST ADVERSARIAL EX- AMPLES |
d260440449 | Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer m... | LONG RANGE ARENA: A BENCHMARK FOR EFFICIENT TRANSFORMERS |
d232257874 | Recent works in Generative Adversarial Networks (GANs) are actively revisiting various data augmentation techniques as an effective way to prevent discriminator overfitting. It is still unclear, however, that which augmentations could actually improve GANs, and in particular, how to apply a wider range of augmentations... | Published as a conference paper at ICLR 2021 TRAINING GANS WITH STRONGER AUGMENTATIONS VIA CONTRASTIVE DISCRIMINATOR |
d225075683 | Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exp... | Preprint. Under review CONSERVATIVE SAFETY CRITICS FOR EXPLORATION |
d228084090 | In many scenarios, named entity recognition (NER) models severely suffer from unlabeled entity problem, where the entities of a sentence may not be fully annotated. Through empirical studies performed on synthetic datasets, we find two causes of the performance degradation. One is the reduction of annotated entities an... | EMPIRICAL ANALYSIS OF UNLABELED ENTITY PROB- LEM IN NAMED ENTITY RECOGNITION |
d3334133 | It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks (DNNs). In this paper, we make the case that sparse representations of the input data are a crucial tool for combating such attacks. For linear classifiers, we show that a sparsifying front end is prova... | Workshop track -ICLR 2018 COMBATING ADVERSARIAL ATTACKS USING SPARSE REPRESENTATIONS |
d232233782 | Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing approaches to handling noisy labels fall into two categories: they either assume an ideal feature-independent noise, or remain heuristic wi... | Published as a conference paper at ICLR 2021 LEARNING WITH FEATURE-DEPENDENT LABEL NOISE: A PROGRESSIVE APPROACH |
d231861410 | Machine learning benefits from large training datasets, which may not always be possible to collect by any single entity, especially when using privacy-sensitive data. In many contexts, such as healthcare and finance, separate parties may wish to collaborate and learn from each other's data but are prevented from doing... | Published as a conference paper at ICLR 2021 CAPC LEARNING: CONFIDENTIAL AND PRIVATE COLLABORATIVE LEARNING |
d255595956 | Our brain can almost effortlessly decompose visual data streams into background and salient objects. Moreover, it can anticipate object motion and interactions, which are crucial abilities for conceptual planning and reasoning. Recent object reasoning datasets, such as CATER, have revealed fundamental shortcomings of c... | LEARNING WHAT AND WHERE: DISENTANGLING LOCATION AND IDENTITY TRACK- ING WITHOUT SUPERVISION |
d252211791 | Adversarial patch attacks are an emerging security threat for real world deep learning applications. We present DEMASKED SMOOTHING, the first approach (up to our knowledge) to certify the robustness of semantic segmentation models against this threat model. Previous work on certifiably defending against patch attacks h... | Published as a conference paper at ICLR 2023 CERTIFIED DEFENCES AGAINST ADVERSARIAL PATCH ATTACKS ON SEMANTIC SEGMENTATION |
d209483461 | Binary Neural Networks (BNNs) have been garnering interest thanks to their compute cost reduction and memory savings. However, BNNs suffer from performance degradation mainly due to the gradient mismatch caused by binarizing activations. Previous works tried to address the gradient mismatch problem by reducing the disc... | BINARYDUO: REDUCING GRADIENT MISMATCH IN BI- NARY ACTIVATION NETWORK BY COUPLING BINARY ACTIVATIONS |
d2922805 | We propose BlackOut, an approximation algorithm to efficiently train massive recurrent neural network language models (RNNLMs) with million word vocabularies. BlackOut is motivated by using a discriminative loss, and we describe a weighted sampling strategy which significantly reduces computation while improving stabil... | BLACKOUT: SPEEDING UP RECURRENT NEURAL NET- WORK LANGUAGE MODELS WITH VERY LARGE VO- CABULARIES |
d16209268 | Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization, such as unsupervised pretraining. However, modern neural networks are... | Published as a conference paper at ICLR 2015 QUALITATIVELY CHARACTERIZING NEURAL NETWORK OPTIMIZATION PROBLEMS |
d231985837 | To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployme... | Published as a conference paper at ICLR 2021 TARGETED ATTACK AGAINST DEEP NEURAL NET- WORKS VIA FLIPPING LIMITED WEIGHT BITS |
d212644628 | Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the physical sciences, they struggle to learn these symmetries. In this paper, we p... | LAGRANGIAN NEURAL NETWORKS |
d222272305 | Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environments. To facilitate research addressing this problem, we propose CausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment. The environ... | CAUSALWORLD: A ROBOTIC MANIPULATION BENCHMARK FOR CAUSAL STRUCTURE AND TRANS- FER LEARNING |
d249209650 | Many real-world settings involve costs for performing actions; transaction costs in financial systems and fuel costs being common examples. In these settings, performing actions at each time step quickly accumulates costs leading to vastly suboptimal outcomes. Additionally, repeatedly acting produces wear and tear and ... | Published as a conference paper at ICLR 2023 TIMING IS EVERYTHING: LEARNING TO ACT SELEC- TIVELY WITH COSTLY ACTIONS AND CONSTRAINTS |
d52901322 | Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a single point in the embedding space. Often the distance between points is used as a proxy for match confi... | MODELING UNCERTAINTY WITH HEDGED INSTANCE EMBEDDING |
d252734863 | The application of pre-training large transformer models on massive amounts of unlabeled data and fine-tuning them on labeled datasets for diverse downstream tasks has demonstrated remarkable success in various vision and natural language processing tasks. However, the direct fine-tuning approach may result in suboptim... | Published as a conference paper at ICLR 2023 SELF-DISTILLATION FOR FURTHER PRE-TRAINING OF TRANSFORMERS |
d1369182 | We develop a metalearning approach for learning hierarchically structured policies, improving sample efficiency on unseen tasks through the use of shared primitives-policies that are executed for large numbers of timesteps. Specifically, a set of primitives are shared within a distribution of tasks, and are switched be... | META LEARNING SHARED HIERARCHIES Work done as an intern at OpenAI |
d13669032 | We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs). The motivation is that when the discriminator D spreads out its model capacity in the right way, the learning signals given to the generator G are more informative and diverse. These in turn help G to explore better and di... | Published as a conference paper at ICLR 2018 IMPROVING GAN TRAINING VIA BINARIZED REPRESENTATION ENTROPY (BRE) REGULARIZATION |
d257102642 | To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional FL algorithms (e.g., FedAvg) typically relies on multi-round model-based interactions. However, it is impractical and inefficient for transla... | Published as a conference paper at ICLR 2023 FEDERATED NEAREST NEIGHBOR MACHINE TRANSLATION |
d249395677 | Recent approaches in self-supervised learning of image representations can be categorized into different families of methods and, in particular, can be divided into contrastive and non-contrastive approaches. While differences between the two families have been thoroughly discussed to motivate new approaches, we focus ... | Published as a conference paper at ICLR 2023 ON THE DUALITY BETWEEN CONTRASTIVE AND NON- CONTRASTIVE SELF-SUPERVISED LEARNING |
d14538467 | The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters. In this work we ... | An empirical analysis of dropout in piecewise linear networks |
d257631760 | Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-trained model, which becomes prohibitive when a large number of downstream tasks are present. Therefore, many fine-tuning methods are proposed to ... | Published as a conference paper at ICLR 2023 ADAPTIVE BUDGET ALLOCATION FOR PARAMETER- EFFICIENT FINE-TUNING |
d257102667 | The transferability of adversarial perturbations between image models has been extensively studied. In this case, an attack is generated from a known surrogate e.g., the ImageNet trained model, and transferred to change the decision of an unknown (black-box) model trained on an image dataset. However, attacks generated... | Published as a conference paper at ICLR 2023 BOOSTING ADVERSARIAL TRANSFERABILITY USING DYNAMIC CUES |
d231855369 | We consider representation learning of 3D molecular graphs in which each atom is associated with a spatial position in 3D. This is an under-explored area of research, and a principled message passing framework is currently lacking. In this work, we conduct analyses in the spherical coordinate system (SCS) for the compl... | Published as a conference paper at ICLR 2022 SPHERICAL MESSAGE PASSING FOR 3D MOLECULAR GRAPHS |
d211252650 | We introduce the notion of property signatures, a representation for programs and program specifications meant for consumption by machine learning algorithms. Given a function with input type τ in and output type τ out , a property is a function of type: (τ in , τ out ) → Bool that (informally) describes some simple pr... | Published as a conference paper at ICLR 2020 LEARNING TO REPRESENT PROGRAMS WITH PROPERTY SIGNATURES |
d213488539 | In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision. We propose a rule-exemplar method for collecting human supervision to combine the efficiency of rules with the quality of instance labels. The supervision is coupled such that it is both natural for... | Published as a conference paper at ICLR 2020 LEARNING FROM RULES GENERALIZING LABELED EXEMPLARS |
d245906266 | It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. The recent advances in this research have been mainly driven by 3D convolutional neural networks and vision transformers. Althou... | UNIFORMER: UNIFIED TRANSFORMER FOR EFFICIENT SPATIOTEMPORAL REPRESENTATION LEARNING |
d5334223 | Retrosynthesis is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro-and fine chemicals. In retrosynthesis, a search tree is built by analysing molecules recursively and dissecting them into simpler molecular building blocks until one obtains a set of known building blocks. The sea... | TOWARDS "ALPHACHEM": CHEMICAL SYNTHESIS PLANNING WITH TREE SEARCH AND DEEP NEURAL NETWORK POLICIES |
d256105572 | Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Neural Processes (NPs), which model the distributions over functions conditioned on partial observations (context set), provide a practical so... | Published as a conference paper at ICLR 2023 VERSATILE NEURAL PROCESSES FOR LEARNING IM- PLICIT NEURAL REPRESENTATIONS |
d257232713 | Reinforcement learning (RL) agents can leverage batches of previously collected data to extract a reasonable control policy. An emerging issue in this offline RL setting, however, is that the bootstrapping update underlying many of our methods suffers from insufficient action-coverage: standard max operator may select ... | Published as a conference paper at ICLR 2023 THE IN-SAMPLE SOFTMAX FOR OFFLINE REINFORCE- MENT LEARNING |
d3502463 | Recent advances in deep reinforcement learning have made significant strides in performance on applications such as Go and Atari games. However, developing practical methods to balance exploration and exploitation in complex domains remains largely unsolved. Thompson Sampling and its extension to reinforcement learning... | Published as a conference paper at ICLR 2018 DEEP BAYESIAN BANDITS SHOWDOWN AN EMPIRICAL COMPARISON OF BAYESIAN DEEP NETWORKS FOR THOMPSON SAMPLING |
d252762466 | Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity, modeled by simp... | Published as a conference paper at ICLR 2023 UNDERSTANDING NEURAL CODING ON LATENT MAN- IFOLDS BY SHARING FEATURES AND DIVIDING EN- SEMBLES |
d246867402 | Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of modifying existing nodes or edges, i.e., Graph Modification Attack (GMA). Although GIA has achieved promising results, little is known about ... | Published as a conference paper at ICLR 2022 UNDERSTANDING AND IMPROVING GRAPH INJECTION ATTACK BY PROMOTING UNNOTICEABILITY |
d2350854 | A key requirement for the development of effective learning representations is their evaluation and comparison to representations we know to be effective. In natural sensory domains, the community has viewed the brain as a source of inspiration and as an implicit benchmark for success. However, it has not been possible... | The Neural Representation Benchmark and its Evaluation on Brain and Machine |
d256808600 | Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D shapes. When applied to 3D shapes, INRs allow to overcome the fragmentation and shortcomings of the popular discrete representations used s... | Published as a conference paper at ICLR 2023 DEEP LEARNING ON IMPLICIT NEURAL REPRESENTATIONS OF SHAPES |
d222134189 | Convolutional neural networks (CNN) exhibit unmatched performance in a multitude of computer vision tasks. However, the advantage of using convolutional networks over fully-connected networks is not understood from a theoretical perspective. In this work, we show how convolutional networks can leverage locality in the ... | COMPUTATIONAL SEPARATION BETWEEN CONVOLU- TIONAL AND FULLY-CONNECTED NETWORKS |
d248084993 | Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancing representation learning. Natural views are often handled by multiview analysis tools, e.g., (deep) canonical correlation analysis [(D)CCA]... | Published as a conference paper at ICLR 2022 UNDERSTANDING LATENT CORRELATION-BASED MULTIVIEW LEARNING AND SELF-SUPERVISION: AN IDENTIFIABILITY PERSPECTIVE |
d259298198 | Errors in labels obtained via human annotation adversely affect a model's performance.Existing approaches propose ways to mitigate the effect of label error on a model's downstream accuracy, yet little is known about its impact on a model's disparity metrics 1 .Here we study the effect of label error on a model's dispa... | QUANTIFYING AND MITIGATING THE IMPACT OF LA-BEL ERRORS ON MODEL DISPARITY METRICS |
d8875939 | The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative mo... | Deep Predictive Coding Networks |
d201058750 | It is well-known that overparametrized neural networks trained using gradientbased methods quickly achieve small training error with appropriate hyperparameter settings. Recent papers have proved this statement theoretically for highly overparametrized networks under reasonable assumptions. These results either assume ... | EFFECT OF ACTIVATION FUNCTIONS ON THE TRAIN- ING OF OVERPARAMETRIZED NEURAL NETS |
d212802994 | Stochastic gradient descent (SGD) with stochastic momentum is popular in nonconvex stochastic optimization and particularly for the training of deep neural networks. In standard SGD, parameters are updated by improving along the path of the gradient at the current iterate on a batch of examples, where the addition of a... | Published as a conference paper at ICLR 2020 ESCAPING SADDLE POINTS FASTER WITH STOCHASTIC MOMENTUM |
d258426655 | We propose Structured Exploration with Achievements (SEA), a multi-stage reinforcement learning algorithm designed for achievement-based environments, a particular type of environment with an internal achievement set. SEA first uses offline data to learn a representation of the known achievements with a determinant los... | Published as a conference paper at ICLR 2023 LEARNING ACHIEVEMENT STRUCTURE FOR STRUC- TURED EXPLORATION IN DOMAINS WITH SPARSE RE- WARD |
d255341108 | Can we build continuous generative models which generalize across scales, can be evaluated at any coordinate, admit calculation of exact derivatives, and are conceptually simple? Existing MLP-based architectures generate worse samples than the grid-based generators with favorable convolutional inductive biases. Models ... | Published as a conference paper at ICLR 2023 FUNKNN: NEURAL INTERPOLATION FOR FUNCTIONAL GENERATION |
d211096737 | We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. This setting is similar to semi-supervised learning, but significantly harder because there are no labelled examples for the new classes. The challenge, then, is to leverage the information contained in t... | Published as a conference paper at ICLR 2020 AUTOMATICALLY DISCOVERING AND LEARNING NEW VISUAL CATEGORIES WITH RANKING STATISTICS |
d247475824 | A recent line of work on black-box adversarial attacks has revived the use of transfer from surrogate models by integrating it into query-based search. However, we find that existing approaches of this type underperform their potential, and can be overly complicated besides. Here, we provide a short and simple algorith... | Published as a conference paper at ICLR 2022 ATTACKING DEEP NETWORKS WITH SURROGATE- BASED ADVERSARIAL BLACK-BOX METHODS IS EASY |
d222208985 | This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks, and hence are critical to learn. Existing methods either suffer from the problem of searching in a large search space (e.... | Published as a conference paper at ICLR 2021 RNNLOGIC: LEARNING LOGIC RULES FOR REASON- ING ON KNOWLEDGE GRAPHS |
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