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d232222444 | We accelerate deep reinforcement learning-based training in visually complex 3D environments by two orders of magnitude over prior work, realizing end-to-end training speeds of over 19,000 frames of experience per second on a single GPU and up to 72,000 frames per second on a single eight-GPU machine. The key idea of o... | Published as a conference paper at ICLR 2021 LARGE BATCH SIMULATION FOR DEEP REINFORCEMENT LEARNING |
d212415013 | Many neural network pruning algorithms proceed in three steps: train the network to completion, remove unwanted structure to compress the network, and retrain the remaining structure to recover lost accuracy. The standard retraining technique, fine-tuning, trains the unpruned weights from their final trained values usi... | Published as a conference paper at ICLR 2020 COMPARING REWINDING AND FINE-TUNING IN NEURAL NETWORK PRUNING |
d15336613 | Convolutional neural networks[3]have proven useful in many domains, including computer vision[1,4,5], audio processing[6,7]and natural language processing[8]. These powerful models come at great cost in training time, however. Currently, long training periods make experimentation difficult and time consuming.In this wo... | Multi-GPU Training of ConvNets |
d5161803 | 6[cs.LG]
We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequential data.Similar to Recurrent Neural Networks (RNNs), RGPs can have different formulations for their internal states, distinct i... | RECURRENT GAUSSIAN PROCESSES
24 Feb 2016
César Lincoln cesarlincoln@terr |
d244709713 | Simulation-based inference with conditional neural density estimators is a powerful approach to solving inverse problems in science. However, these methods typically treat the underlying forward model as a black box, with no way to exploit geometric properties such as equivariances. Equivariances are common in scientif... | Published as a conference paper at ICLR 2022 GROUP EQUIVARIANT NEURAL POSTERIOR ESTIMATION |
d252734772 | Though end-to-end neural approaches have recently been dominating NLP tasks in both performance and ease-of-use, they lack interpretability and robustness. We propose BINDER, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) fu... | Published as a conference paper at ICLR 2023 BINDING LANGUAGE MODELS IN SYMBOLIC LANGUAGES |
d14047116 | Generative Stochastic Networks (GSNs) have been recently introduced as an alternative to traditional probabilistic modeling: instead of parametrizing the data distribution directly, one parametrizes a transition operator for a Markov chain whose stationary distribution is an estimator of the data generating distributio... | Multimodal Transitions for Generative Stochastic Networks |
d248239666 | The problem of processing very long time-series data (e.g., a length of more than 10,000) is a long-standing research problem in machine learning. Recently, one breakthrough, called neural rough differential equations (NRDEs), has been proposed and has shown that it is able to process such data. Their main concept is t... | LORD: LOWER-DIMENSIONAL EMBEDDING OF LOG- SIGNATURE IN NEURAL ROUGH DIFFERENTIAL EQUA- TIONS |
d3602416 | We relate the minimax game of generative adversarial networks (GANs) to finding the saddle points of the Lagrangian function for a convex optimization problem, where the discriminator outputs and the distribution of generator outputs play the roles of primal variables and dual variables, respectively. This formulation ... | Published as a conference paper at ICLR 2018 TRAINING GENERATIVE ADVERSARIAL NETWORKS VIA PRIMAL-DUAL SUBGRADIENT METHODS: A LAGRANGIAN PERSPECTIVE |
d257232537 | Multimodal few-shot learning is challenging due to the large domain gap between vision and language modalities. Existing methods are trying to communicate visual concepts as prompts to frozen language models, but rely on hand-engineered task induction to reduce the hypothesis space. To make the whole process learnable,... | Published as a conference paper at ICLR 2023 META LEARNING TO BRIDGE VISION AND LANGUAGE MODELS FOR MULTIMODAL FEW-SHOT LEARNING |
d209334533 | Predicting outcomes and planning interactions with the physical world are longstanding goals for machine learning. A variety of such tasks involves continuous physical systems, which can be described by partial differential equations (PDEs) with many degrees of freedom. Existing methods that aim to control the dynamics... | Published as a conference paper at ICLR 2020 LEARNING TO CONTROL PDES WITH DIFFERENTIABLE PHYSICS |
d11383178 | The variational autoencoder (VAE; ) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference. It typically makes strong assumptions about posterior inference, for instance that the posterior distribution is approximately fa... | IMPORTANCE WEIGHTED AUTOENCODERS |
d249209875 | Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms have been proposed for offline RL in the previous literature, the minimax optimality has only been (nearly) established for tabular Markov de... | NEARLY MINIMAX OPTIMAL OFFLINE REINFORCE- MENT LEARNING WITH LINEAR FUNCTION APPROXI- MATION: SINGLE-AGENT MDP AND MARKOV GAME |
d258212652 | Decoupling representation learning and classifier learning has been shown to be effective in classification with long-tailed data. There are two main ingredients in constructing a decoupled learning scheme; 1) how to train the feature extractor for representation learning so that it provides generalizable representatio... | Published as a conference paper at ICLR 2023 DECOUPLED TRAINING FOR LONG-TAILED CLASSIFI- CATION WITH STOCHASTIC REPRESENTATIONS |
d247158827 | It is well-known that deep neural networks (DNNs) are susceptible to adversarial attacks, exposing a severe fragility of deep learning systems. As the result, adversarial training (AT) method, by incorporating adversarial examples during training, represents a natural and effective approach to strengthen the robustness... | A UNIFIED WASSERSTEIN DISTRIBUTIONAL ROBUST- NESS FRAMEWORK FOR ADVERSARIAL TRAINING |
d256615635 | Recent vision transformer based video models mostly follow the "image pretraining then finetuning" paradigm and have achieved great success on multiple video benchmarks. However, full finetuning such a video model could be computationally expensive and unnecessary, given the pre-trained image transformer models have de... | Published as a conference paper at ICLR 2023 AIM: ADAPTING IMAGE MODELS FOR EFFICIENT VIDEO ACTION RECOGNITION |
d214309387 | We introduce a deep recurrent neural network architecture that approximates visual cortical circuits(Mély et al., 2018). We show that this architecture, which we refer to as the γ-Net, learns to solve contour detection tasks with better sample efficiency than state-of-the-art feedforward networks, while also exhibiting... | Published as a conference paper at ICLR 2020 RECURRENT NEURAL CIRCUITS FOR CONTOUR DETECTION |
d252568221 | In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions-ranging from routing problems to online advertising auctions-evolve dynamically, thereby leading to many similar games to be solved. To address this gap, we ... | Meta-Learning in Games * |
d238407888 | Neural collapse is a highly symmetric geometry of neural networks that emerges during the terminal phase of training, with profound implications on the generalization performance and robustness of the trained networks. To understand how the last-layer features and classifiers exhibit this recently discovered implicit b... | Published as a conference paper at ICLR 2022 AN UNCONSTRAINED LAYER-PEELED PERSPECTIVE ON NEURAL COLLAPSE |
d211011309 | Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices. Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight ker... | Published as a conference paper at ICLR 2020 AUTOQ: AUTOMATED KERNEL-WISE NEURAL NETWORK QUANTIZATION * |
d257756989 | In-Context Learning (ICL), which formulates target tasks as prompt completion conditioned on in-context demonstrations, has become the prevailing utilization of LLMs. In this paper, we first disclose an actual predicament for this typical usage that it can not scale up with training data due to context length restricti... | Published as a conference paper at ICLR 2023 kNN PROMPTING: BEYOND-CONTEXT LEARNING WITH CALIBRATION-FREE NEAREST NEIGHBOR IN- FERENCE |
d252780278 | Demonstrations and natural language instructions are two common ways to specify and teach robots novel tasks. However, for many complex tasks, a demonstration or language instruction alone contains ambiguities, preventing tasks from being specified clearly. In such cases, a combination of both a demonstration and an in... | Published as a conference paper at ICLR 2023 USING BOTH DEMONSTRATIONS AND LANGUAGE IN- STRUCTIONS TO EFFICIENTLY LEARN ROBOTIC TASKS |
d2222933 | One major challenge in training Deep Neural Networks is preventing overfitting. Many techniques such as data augmentation and novel regularizers such as Dropout have been proposed to prevent overfitting without requiring a massive amount of training data. In this work, we propose a new regularizer called DeCov which le... | Published as a conference paper at ICLR 2016 REDUCING OVERFITTING IN DEEP NETWORKS BY DECORRELATING REPRESENTATIONS |
d256598058 | Most graph neural networks follow the message passing mechanism. However, it faces the over-smoothing problem when multiple times of message passing is applied to a graph, causing indistinguishable node representations and prevents the model to effectively learn dependencies between farther-away nodes. On the other han... | Published as a conference paper at ICLR 2023 ORDERED GNN: ORDERING MESSAGE PASSING TO DEAL WITH HETEROPHILY AND OVER-SMOOTHING |
d239768902 | Despite being able to capture a range of features of the data, high accuracy models trained with supervision tend to make similar predictions. This seemingly implies that high-performing models share similar biases regardless of training methodology, which would limit ensembling benefits and render low-accuracy models ... | Published as a conference paper at ICLR 2022 NO ONE REPRESENTATION TO RULE THEM ALL: OVERLAPPING FEATURES OF TRAINING METHODS |
d209461008 | The allocation of computation resources in the backbone is a crucial issue in object detection. However, classification allocation pattern is usually adopted directly to object detector, which is proved to be sub-optimal. In order to reallocate the engaged computation resources in a more efficient way, we present CR-NA... | Published as a conference paper at ICLR 2020 COMPUTATION REALLOCATION FOR OBJECT DETECTION |
d256827203 | Mixed-integer linear programming (MILP) is widely employed for modeling combinatorial optimization problems. In practice, similar MILP instances with only coefficient variations are routinely solved, and machine learning (ML) algorithms are capable of capturing common patterns across these MILP instances. In this work,... | A GNN-GUIDED PREDICT-AND-SEARCH FRAME- WORK FOR MIXED-INTEGER LINEAR PROGRAMMING |
d245906452 | The architecture and the parameters of neural networks are often optimized independently, which requires costly retraining of the parameters whenever the architecture is modified. In this work we instead focus on growing the architecture without requiring costly retraining. We present a method that adds new neurons dur... | Published as a conference paper at ICLR 2022 GRADMAX: GROWING NEURAL NETWORKS USING GRADIENT INFORMATION |
d254636613 | Equipping predicted segmentation with calibrated uncertainty is essential for safety-critical applications. In this work, we focus on capturing the data-inherent uncertainty (aka aleatoric uncertainty) in segmentation, typically when ambiguities exist in input images. Due to the high-dimensional output space and potent... | Published as a conference paper at ICLR 2023 MODELING MULTIMODAL ALEATORIC UNCERTAINTY IN SEGMENTATION WITH MIXTURE OF STOCHASTIC EXPERTS |
d252596292 | Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require ... | Published as a conference paper at ICLR 2023 BRIDGING THE GAP TO REAL-WORLD OBJECT- CENTRIC LEARNING |
d69629714 | A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an initialization for fine-tuning a new model. To address this issue, in this paper, we develop knowledge flow which moves 'knowledge' from mult... | KNOWLEDGE FLOW: IMPROVE UPON YOUR TEACH- ERS |
d254096353 | Blind image super-resolution (Blind-SR) aims to recover a high-resolution (HR) image from its corresponding low-resolution (LR) input image with unknown degradations. Most of the existing works design an explicit degradation estimator for each degradation to guide SR. However, it is infeasible to provide concrete label... | Published as a conference paper at ICLR 2023 KNOWLEDGE DISTILLATION BASED DEGRADATION ESTIMATION FOR BLIND SUPER-RESOLUTION |
d252439090 | Early sensory systems in the brain rapidly adapt to fluctuating input statistics, which requires recurrent communication between neurons. Mechanistically, such recurrent communication is often indirect and mediated by local interneurons. In this work, we explore the computational benefits of mediating recurrent communi... | Published as a conference paper at ICLR 2023 INTERNEURONS ACCELERATE LEARNING DYNAMICS IN RECURRENT NEURAL NETWORKS FOR STATISTICAL ADAPTATION |
d13583585 | The past several years have seen remarkable progress in generative models which produce convincing samples of images and other modalities. A shared component of many powerful generative models is a decoder network, a parametric deep neural net that defines a generative distribution. Examples include variational autoenc... | Published as a conference paper at ICLR 2017 ON THE QUANTITATIVE ANALYSIS OF DECODER- BASED GENERATIVE MODELS |
d203836194 | Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity.To overcome this issue, recent works on deformation modeling seek to spatially reconfigure the data towards a common arrangement such that semantic recognition suffers less from de... | DEFORMABLE KERNELS: ADAPTING EFFECTIVE RE-CEPTIVE FIELDS FOR OBJECT DEFORMATION |
d235254582 | Understanding how large neural networks avoid memorizing training data is key to explaining their high generalization performance. To examine the structure of when and where memorization occurs in a deep network, we use a recently developed replica-based mean field theoretic geometric analysis method. We find that all ... | ON THE GEOMETRY OF GENERALIZATION AND MEMO- RIZATION IN DEEP NEURAL NETWORKS |
d256389374 | Heteroscedastic classifiers, which learn a multivariate Gaussian distribution over prediction logits, have been shown to perform well on image classification problems with hundreds to thousands of classes. However, compared to standard classifiers, they introduce extra parameters that scale linearly with the number of ... | Published as a conference paper at ICLR 2023 MASSIVELY SCALING HETEROSCEDASTIC CLASSIFIERS |
d52962991 | When a black-box classifier processes an input to render a prediction, which input features are relevant and why? We propose to answer this question by efficiently marginalizing over the universe of plausible alternative values for a subset of features by conditioning a generative model of the input distribution on the... | EXPLAINING IMAGE CLASSIFIERS BY COUNTERFAC- TUAL GENERATION |
d38796293 | Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suff... | Published as a conference paper at ICLR 2018 DEEP GRADIENT COMPRESSION: REDUCING THE COMMUNICATION BANDWIDTH FOR DISTRIBUTED TRAINING |
d257279837 | Spectral graph neural networks (GNNs) learn graph representations via spectraldomain graph convolutions. However, most existing spectral graph filters are scalar-to-scalar functions, i.e., mapping a single eigenvalue to a single filtered value, thus ignoring the global pattern of the spectrum. Furthermore, these filter... | SPECFORMER: SPECTRAL GRAPH NEURAL NET- WORKS MEET TRANSFORMERS |
d237490346 | Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to a different unlabeled target domain. Most existing UDA methods focus on learning domain-invariant feature representation, either from the domain level or category level, using convolution neural networks (CNNs)-based... | CDTRANS: CROSS-DOMAIN TRANSFORMER FOR UN- SUPERVISED DOMAIN ADAPTATION |
d243861189 | Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification, regression, and recommendation tasks. GNNs work well when rich and high-quality connections are available. However, their effectiveness is often jeopardized in many real-world graphs in which node degrees have power-law dist... | Published as a conference paper at ICLR 2022 COLD BREW: DISTILLING GRAPH NODE REPRESEN- TATIONS WITH INCOMPLETE OR MISSING NEIGHBOR- HOODS |
d3457087 | One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.... | Published as a conference paper at ICLR 2018 COMPOSITIONAL OBVERTER COMMUNICATION LEARNING FROM RAW VISUAL INPUT |
d208527260 | Traditional compression methods including network pruning, quantization, low rank factorization and knowledge distillation all assume that network architectures and parameters are one-to-one mapped. In this work, we propose a new perspective on network compression, i.e., network parameters can be disentangled from the ... | NEURAL EPITOME SEARCH FOR ARCHITECTURE- AGNOSTIC NETWORK COMPRESSION |
d211133221 | Robustness verification that aims to formally certify the prediction behavior of neural networks has become an important tool for understanding model behavior and obtaining safety guarantees. However, previous methods can usually only handle neural networks with relatively simple architectures. In this paper, we consid... | Published as a conference paper at ICLR 2020 ROBUSTNESS VERIFICATION FOR TRANSFORMERS |
d224726548 | Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, designing label-preserving transformations for text data tends to be more challenging. In this paper, we propose a novel data augmentation fra... | CODA: CONTRAST-ENHANCED AND DIVERSITY- PROMOTING DATA AUGMENTATION FOR NATURAL LANGUAGE UNDERSTANDING |
d211107043 | Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification. Retrieval of such representations from a large database is however computationally challenging. Approximate methods based on learning compact representations, have been widely explore... | Published as a conference paper at ICLR 2020 MINIMIZING FLOPS TO LEARN EFFICIENT SPARSE REPRESENTATIONS |
d246652175 | Owing much to the revolution of information technology, the recent progress of deep learning benefits incredibly from the vastly enhanced access to data available in various digital formats. However, in certain scenarios, people may not want their data being used for training commercial models and thus studied how to a... | Published as a conference paper at ICLR 2022 LEARNABILITY LOCK: AUTHORIZED LEARNABIL- ITY CONTROL THROUGH ADVERSARIAL INVERTIBLE TRANSFORMATIONS |
d247996596 | Recent text-to-image models have achieved impressive results. However, since they require large-scale datasets of text-image pairs, it is impractical to train them on new domains where data is scarce or not labeled. In this work, we propose using large-scale retrieval methods, in particular, efficient k-Nearest-Neighbo... | KNN-Diffusion: Image Generation via Large-Scale Retrieval Figure 1: (a) Samples of stickers generated from text inputs, (b) Semantic text-guided manipulations applied to the "Original" image without using edit masks. In both cases, our model was trained without any text data |
d17267607 | One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter-and intra-subject differences, as well as to inherent noise associated with EEG data collection. Herein, we propose a novel approach for learning such representations from multi... | LEARNING REPRESENTATIONS FROM EEG WITH DEEP RECURRENT-CONVOLUTIONAL NEURAL NETWORKS |
d13748235 | Current reinforcement learning (RL) methods can successfully learn single tasks, but often generalize poorly to modest perturbations in task domain or training procedure. In this work we present a decoupled learning strategy for RL that creates a shared representation space where knowledge can be robustly transferred. ... | Decoupling Dynamics and Reward for Transfer Learning |
d257505065 | AutoML has demonstrated remarkable success in finding an effective neural architecture for a given machine learning task defined by a specific dataset and an evaluation metric. However, most present AutoML techniques consider each task independently from scratch, which requires exploring many architectures, leading to ... | AUTOTRANSFER: AUTOML WITH KNOWLEDGE TRANSFER -AN APPLICATION TO GRAPH NEURAL NETWORKS |
d492235 | This paper is focused on studying the view-manifold structure in the feature spaces implied by the different layers of Convolutional Neural Networks (CNN). There are several questions that this paper aims to answer: Does the learned CNN representation achieve viewpoint invariance? How does it achieve viewpoint invarian... | DIGGING DEEP INTO THE LAYERS OF CNNS: IN SEARCH OF HOW CNNS ACHIEVE VIEW INVARIANCE |
d256662685 | Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also known but wrongly classified samples, which tends to be more practical in real-world applications... | THE DEVIL IS IN THE WRONGLY-CLASSIFIED SAM- PLES: TOWARDS UNIFIED OPEN-SET RECOGNITION |
d232013680 | Neural Architecture Search (NAS) has been explosively studied to automate the discovery of top-performer neural networks. Current works require heavy training of supernet or intensive architecture evaluations, thus suffering from heavy resource consumption and often incurring search bias due to truncated training or ap... | Published as a conference paper at ICLR 2021 NEURAL ARCHITECTURE SEARCH ON IMAGENET IN FOUR GPU HOURS: A THEORETICALLY INSPIRED PERSPECTIVE |
d233181741 | Recently, a variety of probing tasks are proposed to discover linguistic properties learned in contextualized word embeddings. Many of these works implicitly assume these embeddings lay in certain metric spaces, typically the Euclidean space. This work considers a family of geometrically special spaces, the hyperbolic ... | Published as a conference paper at ICLR 2021 PROBING BERT IN HYPERBOLIC SPACES |
d35742668 | Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples. Such inputs are typically generated by adding small but purposeful modifications that lead to incorrect outputs while imperceptible to human eyes. The goal of this paper is not to introduce a single method, but to... | Workshop track -ICLR 2017 A THEORETICAL FRAMEWORK FOR ROBUSTNESS OF (DEEP) CLASSIFIERS AGAINST ADVERSARIAL EXAMPLES |
d238408313 | A more realistic object detection paradigm, Open-World Object Detection, has arised increasing research interests in the community recently. A qualified openworld object detector can not only identify objects of known categories, but also discover unknown objects, and incrementally learn to categorize them when their a... | Published as a conference paper at ICLR 2022 OBJECTS IN SEMANTIC TOPOLOGY |
d210473577 | A central question of representation learning asks under which conditions it is possible to reconstruct the true latent variables of an arbitrarily complex generative process. Recent breakthrough work by Khemakhem et al. (2019) on nonlinear ICA has answered this question for a broad class of conditional generative pro... | Published as a conference paper at ICLR 2020 DISENTANGLEMENT BY NONLINEAR ICA WITH GENERAL INCOMPRESSIBLE-FLOW NETWORKS (GIN) |
d13056261 | We investigate deep generative models that can exchange multiple modalities bidirectionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that ... | JOINT MULTIMODAL LEARNING WITH DEEP GENERA- TIVE MODELS |
d16342357 | It is an important task to learn a representation for images which has low dimension and preserve the valuable information in original space. At the perspective of manifold, this is conduct by using a series of local invariant mapping. Inspired by the recent successes of deep architectures, we propose a local invariant... | Image Representation Learning Using Graph Regularized Auto-Encoders |
d233739662 | We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering framework that simultaneously exploits the discriminative representations learned by contrastive learning and the semantic structures captured by a latent mixture model. Motivated by the mixture of experts, MiCE employs a gating functi... | MICE: MIXTURE OF CONTRASTIVE EXPERTS FOR UN- SUPERVISED IMAGE CLUSTERING |
d53218829 | In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically "generate and hope" generic utterances that can be memorized in the weights of the model when mapping from input utteran... | OF WIKIPEDIA: KNOWLEDGE-POWERED CONVERSATIONAL AGENTS |
d257280201 | Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building-block for many vision tasks. One generalizable and scalable strategy for HOI detection is to use weak supervision, learning from image-level annotations only. This is inherently challe... | WEAKLY-SUPERVISED HOI DETECTION VIA PRIOR- GUIDED BI-LEVEL REPRESENTATION LEARNING |
d250627507 | We introduce a practical method to enforce partial differential equation (PDE) constraints for functions defined by neural networks (NNs), with a high degree of accuracy and up to a desired tolerance. We develop a differentiable PDEconstrained layer that can be incorporated into any NN architecture. Our method leverage... | Published as a conference paper at ICLR 2023 LEARNING DIFFERENTIABLE SOLVERS FOR SYSTEMS WITH HARD CONSTRAINTS |
d231985467 | Neural Ordinary Differential Equations (NODEs), a framework of continuousdepth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emergent in application of ... | Published as a conference paper at ICLR 2021 NEURAL DELAY DIFFERENTIAL EQUATIONS |
d235254386 | Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued by many scholars. One of the principal benefits of counterfactual explanations is allowing users to explore "what-if" scenarios through what... | DISSECT: DISENTANGLED SIMULTANEOUS EXPLA- NATIONS VIA CONCEPT TRAVERSALS |
d232404017 | Sequential deep learning models such as RNN, causal CNN and attention mechanism do not readily consume continuous-time information. Discretizing the temporal data, as we show, causes inconsistency even for simple continuous-time processes. Current approaches often handle time in a heuristic manner to be consistent with... | A TEMPORAL KERNEL APPROACH FOR DEEP LEARN- ING WITH CONTINUOUS-TIME INFORMATION |
d1450294 | This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [5],... | Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps |
d257366012 | We propose a novel algorithm for offline reinforcement learning called Value Iteration with Perturbed Rewards (VIPeR), which amalgamates the pessimism principle with random perturbations of the value function. Most current offline RL algorithms explicitly construct statistical confidence regions to obtain pessimism via... | VIPER: PROVABLY EFFICIENT ALGORITHM FOR OF- FLINE RL WITH NEURAL FUNCTION APPROXIMATION |
d238407943 | Computational methods for predicting the interface contacts between proteins come highly sought after for drug discovery as they can significantly advance the accuracy of alternative approaches, such as protein-protein docking, protein function analysis tools, and other computational methods for protein bioinformatics.... | Published as a conference paper at ICLR 2022 GEOMETRIC TRANSFORMERS FOR PROTEIN INTERFACE CONTACT PREDICTION |
d232273772 | Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice. When instead learning layouts and modules jointly, compositionality does not arise automatically and an explicit pressure is necessary for the emergence of la... | Published as a conference paper at ICLR 2021 ITERATED LEARNING FOR EMERGENT SYSTEMATICITY IN VQA |
d257280374 | The use of well-disentangled representations offers many advantages for downstream tasks, e.g. an increased sample efficiency, or better interpretability. However, the quality of disentangled interpretations is often highly dependent on the choice of dataset-specific hyperparameters, in particular the regularization st... | Published as a conference paper at ICLR 2023 DAVA: DISENTANGLING ADVERSARIAL VARIA- TIONAL AUTOENCODER |
d211126805 | We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples. We show that our estimator can be derived as the Rao-Blackwellization of three different estimators. Combining our estimator with RE-INFORCE, we o... | Published as a conference paper at ICLR 2020 ESTIMATING GRADIENTS FOR DISCRETE RANDOM VARIABLES BY SAMPLING WITHOUT REPLACEMENT |
d221507913 | Data Poisoning attacks modify training data to maliciously control a model trained on such data. In this work, we focus on targeted poisoning attacks which cause a reclassification of an unmodified test image and as such breach model integrity. We consider a particularly malicious poisoning attack that is both "from sc... | WITCHES' BREW: INDUSTRIAL SCALE DATA POISON- ING VIA GRADIENT MATCHING |
d210023401 | In the Information Bottleneck (IB), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what's their relationship with the dataset and the learned representation? In this paper, we set out to answer these questions by studying multiple phase transitions in the IB... | PHASE TRANSITIONS FOR THE INFORMATION BOTTLE- NECK IN REPRESENTATION LEARNING |
d8739352 | Recently, we proposed to transform the outputs of each hidden neuron in a multilayer perceptron network to have zero output and zero slope on average, and use separate shortcut connections to model the linear dependencies instead. We continue the work by firstly introducing a third transformation to normalize the scale... | Pushing Stochastic Gradient towards Second-Order Methods -Backpropagation Learning with Transformations in Nonlinearities |
d3000562 | Neural language models predict the next token using a latent representation of the immediate token history. Recently, various methods for augmenting neural language models with an attention mechanism over a differentiable memory have been proposed. For predicting the next token, these models query information from a me... | Published as a conference paper at ICLR 2017 FRUSTRATINGLY SHORT ATTENTION SPANS IN NEURAL LANGUAGE MODELING |
d8221720 | Question answering tasks have shown remarkable progress with distributed vector representation. In this paper, we investigate the recently proposed Facebook bAbI tasks which consist of twenty different categories of questions that require complex reasoning. Because the previous work on bAbI are all end-to-end models, e... | Published as a conference paper at ICLR 2016 REASONING IN VECTOR SPACE: AN EXPLORATORY STUDY OF QUESTION ANSWERING |
d238531802 | Despite being widely used, face recognition models suffer from bias: the probability of a false positive (incorrect face match) strongly depends on sensitive attributes such as the ethnicity of the face. As a result, these models can disproportionately and negatively impact minority groups, particularly when used by la... | FAIRCAL: FAIRNESS CALIBRATION FOR FACE VERIFI- CATION |
d12072571 | Identifying acoustic events from a continuously streaming audio source is of interest for many applications including environmental monitoring for basic research. In this scenario neither different event classes are known nor what distinguishes one class from another. Therefore, an unsupervised feature learning method ... | Workshop track -ICLR 2017 UNSUPERVISED FEATURE LEARNING FOR AUDIO ANALYSIS |
d222178254 | Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings. We propose a novel general-purpose framework for conditional generation in multimodal spaces, that uses latent variables to mod... | CONDITIONAL GENERATIVE MODELING VIA LEARN- ING THE LATENT SPACE |
d207870430 | We introduce kNN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a k-nearest neighbors (kNN) model. The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and can be drawn from any text collection, including the original LM training d... | Published as a conference paper at ICLR 2020 GENERALIZATION THROUGH MEMORIZATION: NEAREST NEIGHBOR LANGUAGE MODELS |
d202889315 | Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups. Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometri... | ArXiv preprint. Under review. B-SPLINE CNNS ON LIE GROUPS |
d252683782 | Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks. Previous works focused on the pre-training in a model-free manner while lacking the study... | EUCLID: TOWARDS EFFICIENT UNSUPERVISED RE- INFORCEMENT LEARNING WITH MULTI-CHOICE DY- NAMICS MODEL |
d3632319 | We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not competitive with state-of-the-art SAT solvers, NeuroSAT can solve problems that are substantially larger and more difficult than it ever saw duri... | LEARNING A SAT SOLVER FROM SINGLE-BIT SUPER- VISION |
d247763065 | Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a c... | Published as a conference paper at ICLR 2022 DENOISING LIKELIHOOD SCORE MATCHING FOR CONDITIONAL SCORE-BASED DATA GENERATION |
d208915826 | The paper presents an O(N log N )-implementation of t-SNE -an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that normally runs in O(N 2 ). The new implementation uses vantage-point trees to compute sparse pairwise similarities between the input data object... | Barnes-Hut-SNE |
d248300043 | Adversarial Propagation (AdvProp) is an effective way to improve recognition models, leveraging adversarial examples. Nonetheless, AdvProp suffers from the extremely slow training speed, mainly because: a) extra forward and backward passes are required for generating adversarial examples; b) both original samples and t... | Published as a conference paper at ICLR 2022 FAST ADVPROP |
d254823436 | The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natural languages. This promotes the potential of utilizing models pretrained with data more than 3D as teachers for cross-modal knowledge transfe... | Published as a conference paper at ICLR 2023 AUTOENCODERS AS CROSS-MODAL TEACHERS: CAN PRETRAINED 2D IMAGE TRANSFORMERS HELP 3D REPRESENTATION LEARNING? |
d35615696 | Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities "solve" the exploding gradient problem, we show that this is not the case in general and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which network... | GRADIENTS EXPLODE -DEEP NETWORKS ARE SHALLOW -RESNET EXPLAINED |
d210699231 | Training with larger number of parameters while keeping fast iterations is an increasingly adopted strategy and trend for developing better performing Deep Neural Network (DNN) models. This necessitates increased memory footprint and computational requirements for training. Here we introduce a novel methodology for tra... | Published as a conference paper at ICLR 2020 SHIFTED AND SQUEEZED 8-BIT FLOATING POINT FOR- MAT FOR LOW-PRECISION TRAINING OF DEEP NEU- RAL NETWORKS |
d257050386 | Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive characteristics of low power consumption and temporal information processing. ANN-SNN conversion, as the most commonly used training method for applying SNNs, can ensure that converted SNNs achieve comparable performance to ANNs on l... | Published as a conference paper at ICLR 2023 BRIDGING THE GAP BETWEEN ANNS AND SNNS BY CALIBRATING OFFSET SPIKES |
d252668904 | Crossmodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning and demonstrates great success in various applications. To achieve knowledge transfer across modalities, a pretrained network from one modality is adopted as the teacher to provide supervision signals to... | THE MODALITY FOCUSING HYPOTHESIS: TOWARDS UNDERSTANDING CROSSMODAL KNOWLEDGE DISTIL- LATION |
d248218711 | This paper studies node classification in the inductive setting, i.e., aiming to learn a model on labeled training graphs and generalize it to infer node labels on unlabeled test graphs. This problem has been extensively studied with graph neural networks (GNNs) by learning effective node representations, as well as tr... | Published as a conference paper at ICLR 2022 NEURAL STRUCTURED PREDICTION FOR INDUCTIVE NODE CLASSIFICATION |
d207847681 | Graph representation learning for hypergraphs can be used to extract patterns among higher-order interactions that are critically important in many real world problems. Current approaches designed for hypergraphs, however, are unable to handle different types of hypergraphs and are typically not generic for various lea... | Hyper-SAGNN: a self-attention based graph neural network for hypergraphs A PREPRINT |
d238407713 | Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align with real-world applications and could suffer from poor performance in subsequent tasks such as classification. Thi... | Published as a conference paper at ICLR 2022 GRADIENT IMPORTANCE LEARNING FOR INCOMPLETE OBSERVATIONS |
d253553811 | We tackle the problem of Selective Classification where the objective is to achieve the best performance on a predetermined ratio (coverage) of the dataset. Recent state-of-the-art selective methods come with architectural changes either via introducing a separate selection head or an extra abstention logit. In this pa... | Published as a conference paper at ICLR 2023 TOWARDS BETTER SELECTIVE CLASSIFICATION |
d247411040 | Vision Transformer (ViT) has recently demonstrated promise in computer vision problems. However, unlike Convolutional Neural Networks (CNN), it is known that the performance of ViT saturates quickly with depth increasing, due to the observed attention collapse or patch uniformity. Despite a couple of empirical solution... | ANTI-OVERSMOOTHING IN DEEP VISION TRANS- FORMERS VIA THE FOURIER DOMAIN ANALYSIS: FROM THEORY TO PRACTICE |
d3603886 | Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics. Although previous works have successfully reduced precision in inference, transferring both training and inference processes to low-bitwidth integers has not been demonstrated simu... | Published as a conference paper at ICLR 2018 TRAINING AND INFERENCE WITH INTEGERS IN DEEP NEURAL NETWORKS |
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