_id stringlengths 4 10 | text stringlengths 0 18.4k | title stringlengths 0 8.56k |
|---|---|---|
d257079142 | Humans excel at lifelong learning, as the brain has evolved to be robust to distribution shifts and noise in our ever-changing environment. Deep neural networks (DNNs), however, exhibit catastrophic forgetting and the learned representations drift drastically as they encounter a new task. This alludes to a different er... | ERROR SENSITIVITY MODULATION BASED EXPERI- ENCE REPLAY: MITIGATING ABRUPT REPRESENTA- TION DRIFT IN CONTINUAL LEARNING |
d1204679 | The linear layer is one of the most pervasive modules in deep learning representations. However, it requires O(N 2 ) parameters and O(N 2 ) operations. These costs can be prohibitive in mobile applications or prevent scaling in many domains. Here, we introduce a deep, differentiable, fully-connected neural network modu... | ACDC: A STRUCTURED EFFICIENT LINEAR LAYER |
d5096141 | Hash codes are a very efficient data representation needed to be able to cope with the ever growing amounts of data. We introduce a random forest semantic hashing scheme with information-theoretic code aggregation, showing for the first time how random forest, a technique that together with deep learning have shown spe... | Under review as a workshop contribution at ICLR 2015 RANDOM FORESTS CAN HASH |
d225075982 | Empirical studies demonstrate that the performance of neural networks improves with increasing number of parameters. In most of these studies, the number of parameters is increased by increasing the network width. This begs the question: Is the observed improvement due to the larger number of parameters, or is it due t... | Published as a conference paper at ICLR 2021 ARE WIDER NETS BETTER GIVEN THE SAME NUMBER OF PARAMETERS? |
d257255014 | Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward prediction. Many applications of contextual bandits involve multiple agents who collaborate without sharing raw observations, thus giving r... | Published as a conference paper at ICLR 2023 FEDERATED NEURAL BANDITS |
d68222714 | Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 arch... | APPROXIMATING CNNS WITH BAG-OF-LOCAL- FEATURES MODELS WORKS SURPRISINGLY WELL ON IMAGENET |
d253801755 | We consider the general problem of recovering a high-dimensional signal from noisy quantized measurements. Quantization, especially coarse quantization such as 1-bit sign measurements, leads to severe information loss and thus a good prior knowledge of the unknown signal is helpful for accurate recovery. Motivated by t... | Published as a conference paper at ICLR 2023 QUANTIZED COMPRESSED SENSING WITH SCORE- BASED GENERATIVE MODELS |
d3338083 | The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing. Here we used the Generative Adversarial Networks (GANs) framework to simulate the concerted activity of a population of neurons. We adapted the Wasserstein-GAN variant to facilitate the generation of u... | Published as a conference paper at ICLR 2018 SYNTHESIZING REALISTIC NEURAL POPULATION ACTIVITY PATTERNS USING GENERATIVE ADVERSARIAL NETWORKS |
d252531892 | In cooperative multi-agent reinforcement learning (MARL), combining value decomposition with actor-critic enables agents to learn stochastic policies, which are more suitable for the partially observable environment. Given the goal of learning local policies that enable decentralized execution, agents are commonly assu... | MORE CENTRALIZED TRAINING, STILL DECENTRAL- IZED EXECUTION: MULTI-AGENT CONDITIONAL POL- ICY FACTORIZATION |
d238582653 | Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on both generative and discriminative language tasks. Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that ... | VECTOR-QUANTIZED IMAGE MODELING WITH IM- PROVED VQGAN |
d46929526 | In recent years, deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the Atari games. Many reinforcement learning problems, however, involve highdimensional discrete action spaces as well as high-dimensional state spaces. This pap... | Efficient Entropy for Policy Gradient with Multidimensional Action Space |
d252846362 | The graph Transformer emerges as a new architecture and has shown superior performance on various graph mining tasks. In this work, we observe that existing graph Transformers treat nodes as independent tokens and construct a single long sequence composed of all node tokens so as to train the Transformer model, causing... | NAGPHORMER: A TOKENIZED GRAPH TRANS- FORMER FOR NODE CLASSIFICATION IN LARGE GRAPHS |
d211082896 | We introduce a new routing algorithm for capsule networks, in which a child capsule is routed to a parent based only on agreement between the parent's state and the child's vote. The new mechanism 1) designs routing via inverted dot-product attention; 2) imposes Layer Normalization as normalization; and 3) replaces seq... | Published as a conference paper at ICLR 2020 CAPSULES WITH INVERTED DOT-PRODUCT ATTENTION ROUTING |
d247476275 | Common image-to-image translation methods rely on joint training over data from both source and target domains. The training process requires concurrent access to both datasets, which hinders data separation and privacy protection; and existing models cannot be easily adapted for translation of new domain pairs. We pre... | Published as a conference paper at ICLR 2023 DUAL DIFFUSION IMPLICIT BRIDGES FOR IMAGE-TO-IMAGE TRANSLATION |
d5668935 | Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual features for multilabel annotation, features based on Deep Neural Networks have shown potential to significantly boost performance. In this work, ... | Deep Convolutional Ranking for Multilabel Image Annotation |
d247595391 | We introduce the concept of provably robust adversarial examples for deep neural networks -connected input regions constructed from standard adversarial examples which are guaranteed to be robust to a set of real-world perturbations (such as changes in pixel intensity and geometric transformations). We present a novel ... | Published as a conference paper at ICLR 2022 PROVABLY ROBUST ADVERSARIAL EXAMPLES |
d249461729 | For unsupervised pretraining, mask-reconstruction pretraining (MRP) approaches, e.g. MAE (He et al., 2021) and data2vec (Baevski et al., 2022), randomly mask input patches and then reconstruct the pixels or semantic features of these masked patches via an auto-encoder. Then for a downstream task, supervised fine-tunin... | Published as a conference paper at ICLR 2023 TOWARDS UNDERSTANDING WHY MASK RECON- STRUCTION PRETRAINING HELPS IN DOWNSTREAM TASKS |
d239016586 | Multi-head, key-value attention is the backbone of the widely successful Transformer model and its variants. This attention mechanism uses multiple parallel key-value attention blocks (called heads), each performing two fundamental computations: (1) search -selection of a relevant entity from a set via query-key intera... | Published as a conference paper at ICLR 2022 COMPOSITIONAL ATTENTION: DISENTANGLING SEARCH AND RETRIEVAL |
d235078790 | Overparameterized Neural Networks (NN) display state-of-the-art performance. However, there is a growing need for smaller, energy-efficient, neural networks to be able to use machine learning applications on devices with limited computational resources. A popular approach consists of using pruning techniques. While the... | Published as a conference paper at ICLR 2021 ROBUST PRUNING AT INITIALIZATION |
d233033714 | We find that the way we choose to represent data labels can have a profound effect on the quality of trained models. For example, training an image classifier to regress audio labels rather than traditional categorical probabilities produces a more reliable classification. This result is surprising, considering that au... | Published as a conference paper at ICLR 2021 BEYOND CATEGORICAL LABEL REPRESENTATIONS FOR IMAGE CLASSIFICATION |
d258352540 | The success of the Adam optimizer on a wide array of architectures has made it the default in settings where stochastic gradient descent (SGD) performs poorly. However, our theoretical understanding of this discrepancy is lagging, preventing the development of significant improvements on either algorithm. Recent work a... | Published as a conference paper at ICLR 2023 NOISE IS NOT THE MAIN FACTOR BEHIND THE GAP BETWEEN SGD AND ADAM ON TRANSFORMERS, BUT SIGN DESCENT MIGHT BE |
d252568019 | In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training. We present a novel neurosymbolic approach called SPICE to solve this safe exploration problem. SPICE uses an online shielding layer based on symbolic w... | Published as a conference paper at ICLR 2023 GUIDING SAFE EXPLORATION WITH WEAKEST PRECONDITIONS |
d249089172 | When a dynamical system can be modeled as a sequence of observations, Granger causality is a powerful approach for detecting predictive interactions between its variables. However, traditional Granger causal inference has limited utility in domains where the dynamics need to be represented as directed acyclic graphs (D... | Published as a conference paper at ICLR 2022 GRANGER CAUSAL INFERENCE ON DAGS IDENTIFIES GENOMIC LOCI REGULATING TRANSCRIPTION |
d13687188 | Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning behaviors of the students. In the field of artificia... | Published as a conference paper at ICLR 2018 LEARNING TO TEACH |
d3279351 | E-commerce companies such as Amazon, Alibaba and Flipkart process billions of orders every year. However, these orders represent only a small fraction of all plausible orders. Exploring the space of all plausible orders could help us better understand the relationships between the various entities in an e-commerce ecos... | eCommerceGAN : A Generative Adversarial Network for E-commerce |
d258298544 | A common assumption when training embodied agents is that the impact of taking an action is stable; for instance, executing the "move ahead" action will always move the agent forward by a fixed distance, perhaps with some small amount of actuator-induced noise. This assumption is limiting; an agent may encounter settin... | Published as a conference paper at ICLR 2023 MOVING FORWARD BY MOVING BACKWARD: EMBED- DING ACTION IMPACT OVER ACTION SEMANTICS/projects/action-adaptive-policy |
d232307616 | Recognizing relations between entities is a pivotal task of relational learning. Learning relation representations from distantly-labeled datasets is difficult because of the abundant label noise and complicated expressions in human language. This paper aims to learn predictive, interpretable, and robust relation repre... | Published as a conference paper at ICLR 2021 PROTOTYPICAL REPRESENTATION LEARNING FOR RE- LATION EXTRACTION |
d53389725 | Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to an... | ACCUMULATION BIT-WIDTH SCALING FOR ULTRA- LOW PRECISION TRAINING OF DEEP NETWORKS |
d244116787 | a) class: band aid, spurious feature: fingers, -41.54% (b) class: space bar, spurious feature: keys, -46.15% (c) class: plate, spurious feature: food, -32.31% (d) class: butterfly, spurious feature: flowers, -21.54% (e) class: potter's wheel, spurious feature: vase, -21.54% | Published as a conference paper at ICLR 2022 SALIENT IMAGENET: HOW TO DISCOVER SPURIOUS FEATURES IN DEEP LEARNING? |
d238634783 | Spatial convolutions 1 are widely used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different frames. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding 2 , which shows that adaptive weight calibr... | Published as a conference paper at ICLR 2022 TADA! TEMPORALLY-ADAPTIVE CONVOLUTIONS FOR VIDEO UNDERSTANDING |
d252683760 | Monotonic linear interpolation (MLI) -on the line connecting a random initialization with the minimizer it converges to, the loss and accuracy are monotonic -is a phenomenon that is commonly observed in the training of neural networks. Such a phenomenon may seem to suggest that optimization of neural networks is easy. ... | Published as a conference paper at ICLR 2023 PLATEAU IN MONOTONIC LINEAR INTERPOLATION - A "BIASED" VIEW OF LOSS LANDSCAPE FOR DEEP NETWORKS |
d257913759 | We improve upon previous oblivious sketching and turnstile streaming results for 1 and logistic regression, giving a much smaller sketching dimension achieving O(1)-approximation and yielding an efficient optimization problem in the sketch space. Namely, we achieve for any constant c > 0 a sketching dimension ofÕ(d 1+c... | Almost Linear Constant-Factor Sketching for 1 and Logistic Regression |
d26238954 | We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with di... | Under review as a conference paper at ICLR 2018 BREAKING THE SOFTMAX BOTTLENECK: A HIGH-RANK RNN LANGUAGE MODEL |
d4009713 | Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn those features, they usually require massive amounts of manually labeled data, which... | Published as a conference paper at ICLR 2018 UNSUPERVISED REPRESENTATION LEARNING BY PRE- DICTING IMAGE ROTATIONS |
d232240244 | Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples equally without considering their individual impacts on the model. To address this, for the augmented samples from the same training example,... | Published as a conference paper at ICLR 2021 REWEIGHTING AUGMENTED SAMPLES BY MINIMIZ- ING THE MAXIMAL EXPECTED LOSS |
d2524977 | In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012) implementation and its naive data parallelism on multiple GPUs. Our performance on 2 GPUs is comparable with the state-of-art Caffe library (Jia et al., 2014) run on 1 GPU. To the best of our knowledge, this is the first open-source Python-bas... | THEANO-BASED LARGE-SCALE VISUAL RECOGNI- TION WITH MULTIPLE GPUS |
d257232949 | Recently, both Contrastive Learning (CL) and Mask Image Modeling (MIM) demonstrate that self-supervision is powerful to learn good representations. However, naively combining them is far from success. In this paper, we start by making the empirical observation that a naive joint optimization of CL and MIM losses leads ... | LAYER GRAFTED PRE-TRAINING: BRIDGING CON- TRASTIVE LEARNING AND MASKED IMAGE MODEL- ING FOR LABEL-EFFICIENT REPRESENTATIONS |
d252815657 | Transformers flexibly operate over sets of real-valued vectors representing taskspecific entities and their attributes, where each vector might encode one wordpiece token and its position in a sequence, or some piece of information that carries no position at all. But as set processors, standard transformers are at a d... | Published as a conference paper at ICLR 2023 RELATIONAL ATTENTION: GENERALIZING TRANSFORMERS FOR GRAPH-STRUCTURED TASKS |
d3507990 | In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes representing different c... | Published as a conference paper at ICLR 2018 META-LEARNING FOR SEMI-SUPERVISED FEW-SHOT CLASSIFICATION |
d3790787 | Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use neural network architectures to define energy functions that can capture arbitrary dependencies among parts of structured outputs. Prior work used gradient descent for inference, relaxing the structured output to a set of continuous variables a... | Published as a conference paper at ICLR 2018 LEARNING APPROXIMATE INFERENCE NETWORKS FOR STRUCTURED PREDICTION |
d6458072 | We propose several simple approaches to training deep neural networks on data with noisy labels. We introduce an extra noise layer into the network which adapts the network outputs to match the noisy label distribution. The parameters of this noise layer can be estimated as part of the training process and involve simp... | Learning from Noisy Labels with Deep Neural Networks |
d249152128 | Auxiliary objectives, supplementary learning signals that are introduced to help aid learning on data-starved or highly complex end-tasks, are commonplace in machine learning. Whilst much work has been done to formulate useful auxiliary objectives, their construction is still an art which proceeds by slow and tedious h... | Published as a conference paper at ICLR 2023 AANG: AUTOMATING AUXILIARY LEARNING |
d246861899 | Point cloud analysis is challenging due to irregularity and unordered data structure. To capture the 3D geometries, prior works mainly rely on exploring sophisticated local geometric extractors using convolution, graph, or attention mechanisms. These methods, however, incur unfavorable latency during inference, and the... | Published as a conference paper at ICLR 2022 RETHINKING NETWORK DESIGN AND LOCAL GEOM- ETRY IN POINT CLOUD: A SIMPLE RESIDUAL MLP FRAMEWORK |
d235376887 | Recently, numerous machine learning based methods for combinatorial optimization problems have been proposed that learn to construct solutions in a sequential decision process via reinforcement learning. While these methods can be easily combined with search strategies like sampling and beam search, it is not straightf... | Published as a conference paper at ICLR 2022 EFFICIENT ACTIVE SEARCH FOR COMBINATORIAL OPTIMIZATION PROBLEMS |
d1882658 | In this paper we consider a problem of searching a space of predictive models for a given training data set. We propose an iterative procedure for deriving a sequence of improving models and a corresponding sequence of sets of non-linear features on the original input space. After a finite number of iterations N , the ... | |
d9369284 | Workshop track -ICLR 2017 CHANGING MODEL BEHAVIOR AT TEST-TIME USING REINFORCEMENT LEARNING | |
d3630111 | We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world. In spite of being trained with only internally available signals, thes... | Published as a conference paper at ICLR 2018 LEARNING AWARENESS MODELS |
d247362882 | Product quantization (PQ) coupled with a space rotation, is widely used in modern approximate nearest neighbor (ANN) search systems to significantly compress the disk storage for embeddings and speed up the inner product computation. Existing rotation learning methods, however, minimize quantization distortion for fixe... | Published as a conference paper at ICLR 2022 GIVENS COORDINATE DESCENT METHODS FOR ROTA- TION MATRIX LEARNING IN TRAINABLE EMBEDDING INDEXES |
d248476097 | The past few years have witnessed the great success of Diffusion models (DMs) in generating high-fidelity samples in generative modeling tasks. A major limitation of the DM is its notoriously slow sampling procedure which normally requires hundreds to thousands of time discretization steps of the learned diffusion proc... | FAST SAMPLING OF DIFFUSION MODELS WITH EXPO- NENTIAL INTEGRATOR |
d51747407 | We introduce a new unsupervised representation learning and visualization using deep convolutional networks and self organizing maps called Deep Neural Maps (DNM). DNM jointly learns an embedding of the input data and a mapping from the embedding space to a two-dimensional lattice. We compare visualizations of DNM with... | Workshop track -ICLR 2018 DEEP NEURAL MAPS |
d1988653 | Stochastic binary hidden units in a multi-layer perceptron (MLP) network give at least three potential benefits when compared to deterministic MLP networks. (1) They allow to learn one-to-many type of mappings.(2) They can be used in structured prediction problems, where modeling the internal structure of the output is... | Techniques for Learning Binary Stochastic Feedforward Neural Networks |
d235458009 | An important paradigm of natural language processing consists of large-scale pretraining on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -deploying independen... | LORA: LOW-RANK ADAPTATION OF LARGE LAN- GUAGE MODELS |
d211132598 | Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need to store the data in the cloud. We propose the Federated matched averaging (FedMA) algorithm designed for federated learning of modern neura... | Published as a conference paper at ICLR 2020 FEDERATED LEARNING WITH MATCHED AVERAGING |
d246867209 | Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for training and inference. However, current methods are insufficient to enable this optimization and lead to a large degradation in model perfor... | Published as a conference paper at ICLR 2022 PROSPECT PRUNING: FINDING TRAINABLE WEIGHTS AT INITIALIZATION USING META-GRADIENTS |
d21529792 | Reinforcement learning provides a powerful and general framework for decision making and control, but its application in practice is often hindered by the need for extensive feature and reward engineering. Deep reinforcement learning methods can remove the need for explicit engineering of policy or value features, but ... | Published as a conference paper at ICLR 2018 LEARNING ROBUST REWARDS WITH ADVERSARIAL INVERSE REINFORCEMENT LEARNING |
d3695872 | This paper proposes a class of well-conditioned neural networks in which a unit amount of change in the inputs causes at most a unit amount of change in the outputs or any of the internal layers. We develop the known methodology of controlling Lipschitz constants to realize its full potential in maximizing robustness, ... | L 2 -NONEXPANSIVE NEURAL NETWORKS |
d5696027 | Information propagation is a hard task where the goal is to predict users behavior. We introduce an extension of a model which make use of a kernel to modelize diffusion in a latent space. This extension introduce a threhsold to differentiate if users are contaminated or not. | Predict Information Diffusion using a Latent Representation Space Predict Information Diffusion using a Latent Representation Space |
d209531937 | Neural architecture search (NAS) has achieved breakthrough success in a great number of applications in the past few years. It could be time to take a step back and analyze the good and bad aspects in the field of NAS. A variety of algorithms search architectures under different search space. These searched architectur... | NAS-BENCH-201: EXTENDING THE SCOPE OF RE- PRODUCIBLE NEURAL ARCHITECTURE SEARCH |
d254221009 | Models trained via empirical risk minimization (ERM) are known to rely on spurious correlations between labels and task-independent input features, resulting in poor generalization to distributional shifts. Group distributionally robust optimization (G-DRO) can alleviate this problem by minimizing the worst-case loss o... | Arxiv Submission AGRO: ADVERSARIAL DISCOVERY OF ERROR-PRONE GROUPS FOR ROBUST OPTIMIZATION |
d232013968 | Autoregressive models are widely used for tasks such as image and audio generation. The sampling process of these models, however, does not allow interruptions and cannot adapt to real-time computational resources. This challenge impedes the deployment of powerful autoregressive models, which involve a slow sampling pr... | Published as a conference paper at ICLR 2021 ANYTIME SAMPLING FOR AUTOREGRESSIVE MODELS VIA ORDERED AUTOENCODING |
d222398658 | Explainability in AI is crucial for model development, compliance with regulation, and providing operational nuance to predictions.The Shapley framework for explainability attributes a model's predictions to its input features in a mathematically principled and model-agnostic way.However, general implementations of Sha... | |
d247011732 | Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoisi... | Published as a conference paper at ICLR 2022 PSEUDO NUMERICAL METHODS FOR DIFFUSION MODELS ON MANIFOLDS |
d232404024 | While autoregressive models excel at image compression, their sample quality is often lacking. Although not realistic, generated images often have high likelihood according to the model, resembling the case of adversarial examples. Inspired by a successful adversarial defense method, we incorporate randomized smoothing... | Published as a conference paper at ICLR 2021 IMPROVED AUTOREGRESSIVE MODELING WITH DISTRIBUTION SMOOTHING |
d257255456 | Reward design in reinforcement learning (RL) is challenging since specifying human notions of desired behavior may be difficult via reward functions or require many expert demonstrations. Can we instead cheaply design rewards using a natural language interface? This paper explores how to simplify reward design by promp... | Published as a conference paper at ICLR 2023 REWARD DESIGN WITH LANGUAGE MODELS |
d68071305 | We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observed in expert demonstrations, using generative models to reason probabilistically about alternative histories of demonstrated states. We show ... | GENERATIVE PREDECESSOR MODELS FOR SAMPLE- EFFICIENT IMITATION LEARNING |
d231847288 | Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tasks where efficient exploration is a bottleneck. These methods consider a policy (the actor) and a value function (the critic) whose respecti... | Published as a conference paper at ICLR 2021 ADVERSARIALLY GUIDED ACTOR-CRITIC |
d54444711 | We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph... | LEARNING MULTIMODAL GRAPH-TO-GRAPH TRANS- LATION FOR MOLECULAR OPTIMIZATION |
d254246465 | Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels. Such models typically lead to poor performance during inference for data lacking such correlations. In this work, we explicitly consider... | Published as a conference paper at ICLR 2023 AVOIDING SPURIOUS CORRELATIONS VIA LOGIT COR- RECTION |
d231648272 | Exploration under sparse reward is a long-standing challenge of model-free reinforcement learning. The state-of-the-art methods address this challenge by introducing intrinsic rewards to encourage exploration in novel states or uncertain environment dynamics. Unfortunately, methods based on intrinsic rewards often fall... | Published as a conference paper at ICLR 2021 RANK THE EPISODES: A SIMPLE APPROACH FOR EXPLORATION IN PROCEDURALLY-GENERATED ENVIRONMENTS |
d252668844 | Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However, factors such as the r... | Published as a conference paper at ICLR 2023 RELATIVE REPRESENTATIONS ENABLE ZERO-SHOT LATENT SPACE COMMUNICATION |
d212633559 | Neural Ordinary Differential Equations (NODEs) have proven to be a powerful modeling tool for approximating (interpolation) and forecasting (extrapolation) irregularly sampled time series data. However, their performance degrades substantially when applied to real-world data, especially long-term data with complex beha... | PROGRESSIVE GROWING OF NEURAL ODES |
d3347806 | We propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better language modeling. Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic informatio... | Under review as a conference paper NEURAL LANGUAGE MODELING BY JOINTLY LEARNING SYNTAX AND LEXICON |
d247793212 | Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and nonstationary nature of continual learning data, especially in the online setting, amplify the discrepancy between training and testing in BN and hinder the perform... | Published as a conference paper at ICLR 2022 CONTINUAL NORMALIZATION: RETHINKING BATCH NORMALIZATION FOR ONLINE CONTINUAL LEARNING |
d257496586 | Due to its geometric properties, hyperbolic space can support high-fidelity embeddings of tree-and graph-structured data, upon which various hyperbolic networks have been developed. Existing hyperbolic networks encode geometric priors not only for the input, but also at every layer of the network. This approach involve... | Published as a conference paper at ICLR 2023 RANDOM LAPLACIAN FEATURES FOR LEARNING WITH HYPERBOLIC SPACE |
d3566136 | Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the state of parts of the world that are currently unobserved. In order to match real-world conditions this causal knowledge must be lear... | Published as a conference paper at ICLR 2018 RELATIONAL NEURAL EXPECTATION MAXIMIZATION: UNSUPERVISED DISCOVERY OF OBJECTS AND THEIR INTERACTIONS |
d14124313 | In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achieved by pushing th... | Very Deep Convolutional Networks for Large-Scale Image Recognition |
d8328889 | We present an efficient document representation learning framework, Document Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average of word embeddings. It ensures a representation generated as such captures the semantic meanings of the document during learning. A corruption model is... | Published as a conference paper at ICLR 2017 EFFICIENT VECTOR REPRESENTATION FOR DOCU- MENTS THROUGH CORRUPTION |
d202677369 | We propose a black-box algorithm called Adversarial Variational Inference and Learning (AdVIL) to perform inference and learning in a general Markov random field (MRF). AdVIL employs two variational distributions to approximately infer the latent variables and estimate the partition function of an MRF, respectively. Th... | Published as a conference paper at ICLR 2020 TO RELIEVE YOUR HEADACHE OF TRAINING AN MRF, TAKE ADVIL |
d251135247 | Deep neural networks (DNNs) often have to be compressed, via pruning and/or quantization, before they can be deployed in practical settings. In this work we propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way, in order to produce models whose local loss behavior... | Published as a conference paper at ICLR 2023 CRAM: A COMPRESSION-AWARE MINIMIZER |
d222341795 | Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related problems are encouraging signs that these models can be adapted to more realistic settings where train and test distributions di... | Theoretical bounds on estimation error for meta-learning Mengye Ren African Master for Mathematical Sciences; Vector Institute |
d6230637 | In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an ... | Published as a conference paper at ICLR 2016 UNSUPERVISED AND SEMI-SUPERVISED LEARNING WITH CATEGORICAL GENERATIVE ADVERSARIAL NETWORKS |
d246904340 | Transformer has shown great successes in natural language processing, computer vision, and audio processing. As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length. Kernel methods are ... | Published as a conference paper at ICLR 2022 COSFORMER : RETHINKING SOFTMAX IN ATTENTION |
d257496457 | In this paper, we study a novel inference paradigm, termed as schema inference, that learns to deductively infer the explainable predictions by rebuilding the prior deep neural network (DNN) forwarding scheme, guided by the prevalent philosophical cognitive concept of schema. We strive to reformulate the conventional m... | Published as a conference paper at ICLR 2023 SCHEMA INFERENCE FOR INTERPRETABLE IMAGE CLASSIFICATION |
d247291992 | Despite the tremendous empirical success of deep learning models to solve various learning tasks, our theoretical understanding of their generalization ability is very limited. Classical generalization bounds based on tools such as the VC dimension or Rademacher complexity, are so far unsuitable for deep models and it ... | Published as a conference paper at ICLR 2022 GENERALIZATION THROUGH THE LENS OF LEAVE- ONE-OUT ERROR |
d15323440 | The Weyl transform is introduced as a powerful framework for representing measurement data. Transform coefficients are connected to the Walsh-Hadamard transform of multiscale autocorrelations, and different forms of dyadic periodicity in a signal are shown to appear as different features in its Weyl coefficients. A lar... | REPRESENTATION USING THE WEYL TRANSFORM |
d251402961 | We present Bit Diffusion: a simple and generic approach for generating discrete data with continuous state and continuous time diffusion models. The main idea behind our approach is to first represent the discrete data as binary bits, and then train a continuous diffusion model to model these bits as real numbers which... | Published as a conference paper at ICLR 2023 ANALOG BITS: GENERATING DISCRETE DATA USING DIFFUSION MODELS WITH SELF-CONDITIONING |
d4302773 | The Madry Lab recently hosted a competition designed to test the robustness of their adversarially trained MNIST model. Attacks were constrained to perturb each pixel of the input image by a scaled maximal L ∞ distortion = 0.3. This decision discourages the use of attacks which are not optimized on the L ∞ distortion m... | Workshop track -ICLR 2018 ATTACKING THE MADRY DEFENSE MODEL WITH L 1 -BASED ADVERSARIAL EXAMPLES |
d247451267 | Disentangled representation learning is one of the major goals of deep learning, and is a key step for achieving explainable and generalizable models. A well-defined theoretical guarantee still lacks for the VAE-based unsupervised methods, which are a set of popular methods to achieve unsupervised disentanglement. The ... | TOWARDS BUILDING A GROUP-BASED UNSUPER- VISED REPRESENTATION DISENTANGLEMENT FRAME- WORK |
d249240147 | Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in collaborative and federated learning. However, many fruitful directions, such as the usage of variance reduction for achieving robustness and communication compression for reducing communication costs, remain weakly explored i... | VARIANCE REDUCTION IS AN ANTIDOTE TO BYZANTINE WORKERS: BETTER RATES, WEAKER ASSUMPTIONS AND COMMUNICATION COMPRESSION AS A CHERRY ON THE TOP |
d4410570 | Recurrent neural networks (RNNs) form an important class of architectures among neural networks useful for language modeling and sequential prediction. However, optimizing RNNs is known to be harder compared to feed-forward neural networks. A number of techniques have been proposed in literature to address this problem... | Published as a conference paper at ICLR 2018 FRATERNAL DROPOUT |
d11222874 | Hypothesis testing is an important cognitive process that supports human reasoning. In this paper, we introduce a new computational hypothesis testing framework that is based on memory augmented neural networks. Our approach involves a hypothesis testing loop that reconsiders and progressively refines a previously form... | Published as a conference paper at ICLR 2017 REASONING WITH MEMORY AUGMENTED NEURAL NETWORKS FOR LANGUAGE COMPREHENSION |
d198895601 | We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the atte... | U-GAT-IT: UNSUPERVISED GENERATIVE ATTEN- TIONAL NETWORKS WITH ADAPTIVE LAYER- INSTANCE NORMALIZATION FOR IMAGE-TO-IMAGE TRANSLATION |
d7179166 | Artificial neural networks typically have a fixed, non-linear activation function at each neuron. We have designed a novel form of piecewise linear activation function that is learned independently for each neuron using gradient descent. With this adaptive activation function, we are able to improve upon deep neural ne... | LEARNING ACTIVATION FUNCTIONS TO IMPROVE DEEP NEURAL NETWORKS |
d250089350 | Reward-free reinforcement learning (RF-RL), a recently introduced RL paradigm, relies on random action-taking to explore the unknown environment without any reward feedback information. While the primary goal of the exploration phase in RF-RL is to reduce the uncertainty in the estimated model with minimum number of tr... | SAFE EXPLORATION INCURS NEARLY NO ADDI- TIONAL SAMPLE COMPLEXITY FOR REWARD-FREE RL |
d248405706 | Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems. Generally speaking, however, a good loss function can take on much more flexible forms, and should be tailored for different tasks and datasets. Motivated by how functions can be approximated vi... | POLYLOSS: A POLYNOMIAL EXPANSION PERSPEC- TIVE OF CLASSIFICATION LOSS FUNCTIONS |
d237420771 | A Learning hierarchical structures in sequential data-from simple algorithmic patterns to natural language-in a reliable, generalizable way remains a challenging problem for neural language models. Past work has shown that recurrent neural networks (RNNs) struggle to generalize on held-out algorithmic or syntactic patt... | |
d17826787 | The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision. These results have largely come from computational break throughs of two forms: model parallelism, e.g. GPU accelerated training, which has seen quick adoption in computer vision circles, and ... | GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training |
d252683719 | Consider the problem of estimating the causal effect of some attribute of a text document; for example: what effect does writing a polite vs. rude email have on response time?To estimate a causal effect from observational data, we need to adjust for confounding aspects of the text that affect both the treatment and out... | CAUSAL ESTIMATION FOR TEXT DATA WITH (APPAR-ENT) OVERLAP VIOLATIONS |
d257365448 | Recent advancements in explainable machine learning provide effective and faithful solutions for interpreting model behaviors. However, many explanation methods encounter efficiency issues, which largely limit their deployments in practical scenarios. Real-time explainer (RTX) frameworks have thus been proposed to acce... | Published as a conference paper at ICLR 2023 CORTX: CONTRASTIVE FRAMEWORK FOR REAL- TIME EXPLANATION |
d255522680 | Various saliency map methods have been proposed to interpret and explain predictions of deep learning models. Saliency maps allow us to interpret which parts of the input signals have a strong influence on the prediction results. However, since a saliency map is obtained by complex computations in deep learning models,... | Valid P -Value for Deep Learning-Driven Salient Region |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.