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d221785486 | Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the geometric and relational aspects of the problem domain. To address this gap, we introduce geometric vector perceptrons, which ... | Published as a conference paper at ICLR 2021 LEARNING FROM PROTEIN STRUCTURE WITH GEOMETRIC VECTOR PERCEPTRONS |
d3384895 | The top-k error is a common measure of performance in machine learning and computer vision. In practice, top-k classification is typically performed with deep neural networks trained with the cross-entropy loss. Theoretical results indeed suggest that cross-entropy is an optimal learning objective for such a task in th... | Published as a conference paper at ICLR 2018 SMOOTH LOSS FUNCTIONS FOR DEEP TOP-K CLASSIFICATION |
d5462200 | Normalization techniques have only recently begun to be exploited in supervised learning tasks. Batch normalization exploits mini-batch statistics to normalize the activations. This was shown to speed up training and result in better models. However its success has been very limited when dealing with recurrent neural n... | Published as a conference paper at ICLR 2017 NORMALIZING THE NORMALIZERS: COMPARING AND EXTENDING NETWORK NORMALIZATION SCHEMES |
d257631600 | By enabling agents to communicate, recent cooperative multi-agent reinforcement learning (MARL) methods have demonstrated better task performance and more coordinated behavior. Most existing approaches facilitate inter-agent communication by allowing agents to send messages to each other through free communication chan... | Published as a conference paper at ICLR 2023 CHEAP TALK DISCOVERY AND UTILIZATION IN MULTI-AGENT REINFORCEMENT LEARNING |
d4807923 | Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction. We introduce two communication protoc... | Published as a conference paper at ICLR 2018 EMERGENT COMMUNICATION THROUGH NEGOTIATION |
d253237390 | Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithms (EA) are two major paradigms of policy optimization with distinct learning principles, i.e., gradient-based v.s. gradient-free. An appealing research direction is integrating Deep RL and EA to devise new methods by fusing their complementary advantages. ... | ERL-RE 2 : EFFICIENT EVOLUTIONARY REINFORCE- MENT LEARNING WITH SHARED STATE REPRESENTA- TION AND INDIVIDUAL POLICY REPRESENTATION |
d50773706 | Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1) the n... | EFFICIENT MULTI-OBJECTIVE NEURAL ARCHITEC- TURE SEARCH VIA LAMARCKIAN EVOLUTION |
d247446904 | Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein function or structure. Existing approaches usually pretrain protein language models on a large number of unlabeled amino acid sequences and then finetune the models with some labeled data in downstream tas... | Published as a conference paper at ICLR 2023 PROTEIN REPRESENTATION LEARNING BY GEOMETRIC STRUCTURE PRETRAINING |
d6079627 | Energy-based models are popular in machine learning due to the elegance of their formulation and their relationship to statistical physics. Among these, the Restricted Boltzmann Machine (RBM), and its staple training algorithm contrastive divergence (CD), have been the prototype for some recent advancements in the unsu... | UNDERSTANDING MINIMUM PROBABILITY FLOW FOR RBMS UNDER VARIOUS KINDS OF DYNAMICS |
d19340026 | Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attri... | WHITENING BLACK-BOX NEURAL NETWORKS |
d35673326 | Deep residual networks (ResNets) and their variants are widely used in many computer vision applications and natural language processing tasks. However, the theoretical principles for designing and training ResNets are still not fully understood. Recently, several points of view have emerged to try to interpret ResNet ... | MULTI-LEVEL RESIDUAL NETWORKS FROM DYNAMI- CAL SYSTEMS VIEW |
d49654320 | We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking. Since the state dynamics and rewards depend on... | VARIANCE REDUCTION FOR REINFORCEMENT LEARN- ING IN INPUT-DRIVEN ENVIRONMENTS |
d57761150 | To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the... | REVEALING INTERPRETABLE OBJECT REPRESENTA- TIONS FROM HUMAN BEHAVIOR |
d255749430 | Neural networks often exhibit emergent behavior, where qualitatively new capabilities arise from scaling up the amount of parameters, training data, or training steps.One approach to understanding emergence is to find continuous progress measures that underlie the seemingly discontinuous qualitative changes.We argue th... | |
d9542459 | We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience.The re-training process effectively performs distance metric l... | TRANSFER OF VIEW-MANIFOLD LEARNING TO SIMI-LARITY PERCEPTION OF NOVEL OBJECTS |
d253708071 | To reduce the human annotation efforts, the programmatic weak supervision (PWS) paradigm abstracts weak supervision sources as labeling functions (LFs) and involves a label model to aggregate the output of multiple LFs to produce training labels. Most existing label models require a parameter learning step for each d... | Published as a conference paper at ICLR 2023 LEARNING HYPER LABEL MODEL FOR PROGRAMMATIC WEAK SUPERVISION |
d257039062 | We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models. We show that measuring uncertainty in natural language is challenging because of 'semantic equivalence'-different sentenc... | Published as a conference paper at ICLR 2023 SEMANTIC UNCERTAINTY: LINGUISTIC INVARIANCES FOR UNCERTAINTY ESTIMATION IN NATURAL LANGUAGE GENERATION |
d231985673 | Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. It enables us to learn a meta-initialization of model parameters (that we call meta-model) to rapidly adapt to new tasks using a small amount of labeled training data. Despite the generalization ... | Published as a conference paper at ICLR 2021 ON FAST ADVERSARIAL ROBUSTNESS ADAPTATION IN MODEL-AGNOSTIC META-LEARNING |
d231639408 | Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as images in an unsupervised learning framework. However, only a few works have explored unsupervised disentangled sequential representation lea... | Published as a conference paper at ICLR 2021 DISENTANGLED RECURRENT WASSERSTEIN AUTOEN- CODER |
d252668296 | Geometric image transformations that arise in the real world, such as scaling and rotation, have been shown to easily deceive deep neural networks (DNNs). Hence, training DNNs to be certifiably robust to these perturbations is critical. However, no prior work has been able to incorporate the objective of deterministic ... | Published as a conference paper at ICLR 2023 PROVABLE DEFENSE AGAINST GEOMETRIC TRANSFORMATIONS |
d244714159 | We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial differential equations (PDEs). Starting from a recently proposed Fourier representation of flow fields, the F-FNO bridges the performance gap between pure machine learning approaches to that of the best numerical... | Published as a conference paper at ICLR 2023 FACTORIZED FOURIER NEURAL OPERATORS |
d211132423 | Q-learning suffers from overestimation bias, because it approximates the maximum action value using the maximum estimated action value. Algorithms have been proposed to reduce overestimation bias, but we lack an understanding of how bias interacts with performance, and the extent to which existing algorithms mitigate b... | MAXMIN Q-LEARNING: CONTROLLING THE ESTIMA- TION BIAS OF Q-LEARNING |
d51758422 | The goal of two-sample tests is to assess whether two samples, S P ∼ P n and S Q ∼ Q m , are drawn from the same distribution. Perhaps intriguingly, one relatively unexplored method to build two-sample tests is the use of binary classifiers. In particular, construct a dataset by pairing the n examples in S P with a pos... | Published as a conference paper at ICLR 2017 REVISITING CLASSIFIER TWO-SAMPLE TESTS |
d252668882 | Human visual perception can easily generalize to out-of-distributed visual data, which is far beyond the capability of modern machine learning models. Domain generalization (DG) aims to close this gap, with existing DG methods mainly focusing on the loss function design. In this paper, we propose to explore an orthogon... | SPARSE MIXTURE-OF-EXPERTS ARE DOMAIN GENER- ALIZABLE LEARNERS |
d6667083 | Many machine learning classifiers are vulnerable to adversarial perturbations. An adversarial perturbation modifies an input to change a classifier's prediction without causing the input to seem substantially different to human perception. We deploy three methods to detect adversarial images. Adversaries trying to bypa... | Workshop track -ICLR 2017 EARLY METHODS FOR DETECTING ADVERSARIAL IMAGES |
d898000 | Feedforward multilayer networks trained by supervised learning have recently demonstrated state of the art performance on image labeling problems such as boundary prediction and scene parsing. As even very low error rates can limit practical usage of such systems, methods that perform closer to human accuracy remain de... | Deep and Wide Multiscale Recursive Networks for Robust Image Labeling |
d253158053 | Pre-training is prevalent in nowadays deep learning to improve the learned model's performance. However, in the literature on federated learning (FL), neural networks are mostly initialized with random weights. These attract our interest in conducting a systematic study to explore pre-training for FL. Across multiple v... | Published as a conference paper at ICLR 2023 ON THE IMPORTANCE AND APPLICABILITY OF PRE-TRAINING FOR FEDERATED LEARNING |
d258564695 | Tensor decompositions have been successfully applied to compress neural networks. The compression algorithms using tensor decompositions commonly minimize the approximation error on the weights. Recent work assumes the approximation error on the weights is a proxy for the performance of the model to compress multiple l... | Published as a conference paper at ICLR 2023 HOW INFORMATIVE IS THE APPROXIMATION ERROR FROM TENSOR DECOMPOSITION FOR NEURAL NET- WORK COMPRESSION? |
d251929437 | Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the classical perspective of the bias-variance tradeoff, we propose regularized deep generative model (Reg-DGM), which leverages a nontransferable pre-trained mode... | Published as a conference paper at ICLR 2023 DEEP GENERATIVE MODELING ON LIMITED DATA WITH REGULARIZATION BY NONTRANSFERABLE PRE-TRAINED MODELS |
d248178090 | Deep reinforcement learning can generate complex control policies, but requires large amounts of training data to work effectively. Recent work has attempted to address this issue by leveraging differentiable simulators. However, inherent problems such as local minima and exploding/vanishing numerical gradients prevent... | Published as a conference paper at ICLR 2022 ACCELERATED POLICY LEARNING WITH PARALLEL DIFFERENTIABLE SIMULATION |
d252683876 | This paper uses information-theoretic tools to analyze the generalization error in unsupervised domain adaptation (UDA). We present novel upper bounds for two notions of generalization errors. The first notion measures the gap between the population risk in the target domain and that in the source domain, and the secon... | INFORMATION-THEORETIC ANALYSIS OF UNSUPER- VISED DOMAIN ADAPTATION |
d256389639 | We study the implicit regularization of gradient descent towards structured sparsity via a novel neural reparameterization, which we call a "diagonally grouped linear neural network". We show the following intriguing property of our reparameterization: gradient descent over the squared regression loss, without any expl... | Published as a conference paper at ICLR 2023 IMPLICIT REGULARIZATION FOR GROUP SPARSITY |
d204749519 | Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations. Many works have shown that an SNN for inference can be formed by copying the weights from a trained Artificial Neural Network (ANN) and se... | Published as a conference paper at ICLR 2020 ENABLING DEEP SPIKING NEURAL NETWORKS WITH HYBRID CONVERSION AND SPIKE TIMING DEPENDENT BACKPROPAGATION |
d253735268 | Quantifying similarity between neural representations-e.g. hidden layer activation vectors-is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks that lack stochastic layers) or averaged responses (e.g., trial-averaged firing rates i... | Published as a conference paper at ICLR 2023 REPRESENTATIONAL DISSIMILARITY METRIC SPACES FOR STOCHASTIC NEURAL NETWORKS |
d246441782 | Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information form solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world syst... | Published as a conference paper at ICLR 2022 QUERY EFFICIENT DECISION BASED SPARSE ATTACKS AGAINST BLACK-BOX DEEP LEARNING MODELS |
d238408300 | We propose an asymmetric affinity score for representing the complexity of utilizing the knowledge of one task for learning another one. Our method is based on the maximum bipartite matching algorithm and utilizes the Fisher Information matrix. We provide theoretical analyses demonstrating that the proposed score is ma... | TASK AFFINITY WITH MAXIMUM BIPARTITE MATCH- ING IN FEW-SHOT LEARNING |
d17996690 | This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes u... | PERMUTOHEDRAL LATTICE CNNS |
d219558695 | Learning 3D geometry directly from raw data, such as point clouds, triangle soups, or un-oriented meshes is still a challenging task that feeds many downstream computer vision and graphics applications. In this paper we introduce SAL + + : a method for learning implicit neural representations of shapes directly from su... | SAL ++ : Sign Agnostic Learning with Derivatives |
d249626454 | Knowledge distillation (KD) has shown very promising capabilities in transferring learning representations from large models (teachers) to small models (students). However, as the capacity gap between students and teachers becomes larger, existing KD methods fail to achieve better results. Our work shows that the 'prio... | Published as a conference paper at ICLR 2023 BETTER TEACHER BETTER STUDENT: DYNAMIC PRIOR KNOWLEDGE FOR KNOWLEDGE DISTILLATION |
d257378597 | Rich data and powerful machine learning models allow us to design drugs for a specific protein target in silico. Recently, the inclusion of 3D structures during targeted drug design shows superior performance to other target-free models as the atomic interaction in the 3D space is explicitly modeled. However, current 3... | Published as a conference paper at ICLR 2023 3D EQUIVARIANT DIFFUSION FOR TARGET-AWARE MOLECULE GENERATION AND AFFINITY PREDICTION |
d247058767 | Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. Previous methods tackle such problems by pe... | PESSIMISTIC BOOTSTRAPPING FOR UNCERTAINTY- DRIVEN OFFLINE REINFORCEMENT LEARNING |
d247958259 | Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual learning (SCL) scenarios. Consequently, they are not scalable to real-world applications where the data distribution is often biased and unan... | Published as a conference paper at ICLR 2022 REPRESENTATIONAL CONTINUITY FOR UNSUPERVISED CONTINUAL LEARNING |
d211678036 | We study the convergence of gradient descent (GD) and stochastic gradient descent (SGD) for training L-hidden-layer linear residual networks (ResNets). We prove that for training deep residual networks with certain linear transformations at input and output layers, which are fixed throughout training, both GD and SGD w... | Published as a conference paper at ICLR 2020 ON THE GLOBAL CONVERGENCE OF TRAIN- ING DEEP LINEAR RESNETS |
d3882452 | We propose an approach to address two issues that commonly occur during training of unsupervised GANs. First, since GANs use only a continuous latent distribution to embed multiple classes or clusters of data, they often do not correctly handle the structural discontinuity between disparate classes in a latent space. S... | Published as a conference paper at ICLR 2018 MEMORIZATION PRECEDES GENERATION: LEARNING UNSUPERVISED GANS WITH MEMORY NETWORKS |
d2220097 | GANS are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization by approximating the Laplacian norm using a Monte Carlo approximation that is easily computed with the GAN. When incorporated into the feature-matching GAN of Salimans ... | Workshop track -ICLR 2018 SEMI-SUPERVISED LEARNING WITH GANS: REVISITING MANIFOLD REGULARIZATION |
d4717445 | We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification. The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of... | Published as a conference paper at ICLR 2018 LEARN TO PAY ATTENTION |
d8241258 | The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneou... | ACTOR-MIMIC DEEP MULTITASK AND TRANSFER REINFORCEMENT LEARNING |
d11345245 | In recent years, Deep Learning (DL) has found great success in domains such as multimedia understanding. However, the complex nature of multimedia data makes it difficult to develop DL-based software. The state-of-the-art tools, such as Caffe, TensorFlow, Torch7, and CNTK, while are successful in their applicable domai... | DEEPDSL: A COMPILATION-BASED DOMAIN- SPECIFIC LANGUAGE FOR DEEP LEARNING |
d249097525 | Statistical learning theory provides bounds on the necessary number of training samples needed to reach a prescribed accuracy in a learning problem formulated over a given target class. This accuracy is typically measured in terms of a generalization error, that is, an expected value of a given loss function. However, ... | Published as a conference paper at ICLR 2023 LEARNING RELU NETWORKS TO HIGH UNIFORM ACCURACY IS INTRACTABLE |
d254044293 | Graph neural networks (GNNs) are prominent in the graph machine learning domain, owing to their strong performance across various tasks. A recent focal area is the space of graph self-supervised learning (SSL), which aims to derive useful node representations without labeled data. Notably, many state-of-theart graph SS... | Published as a conference paper at ICLR 2023 LINK PREDICTION WITH NON-CONTRASTIVE LEARNING |
d11189705 | In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specifically, we randomly assign a pseudo parent-class label to each observation which is then modified by applying the domain specific transforma... | Published as a conference paper at ICLR 2018 LEARNING LATENT REPRESENTATIONS IN NEURAL NETWORKS FOR CLUSTERING THROUGH PSEUDO SUPERVISION AND GRAPH-BASED ACTIVITY REGULARIZATION |
d3280568 | While recent progress has spawned very powerful machine learning systems, those agents remain extremely specialized and fail to transfer the knowledge they gain to similar yet unseen tasks. In this paper, we study a simple reinforcement learning problem and focus on learning policies that encode the proper invariances ... | Workshop track -ICLR 2018 LEARNING INVARIANCES FOR POLICY GENERALIZATION |
d219636258 | Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies. At the heart of this is non-commutativity, in the sense that reordering the elements of a sequence can completely change its meaning. We use a classical mathematical object -the... | Published as a conference paper at ICLR 2021 SEQ2TENS: AN EFFICIENT REPRESENTATION OF SE- QUENCES BY LOW-RANK TENSOR PROJECTIONS |
d215827885 | Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly understood. This paper investigates how different normalization techniques affect the performance of neural ODEs. Particularly, we show that... | TOWARDS UNDERSTANDING NORMALIZATION IN NEURAL ODES |
d252762165 | As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless, the effectiveness of GNNs is immensely challenged by issues related to data quality, such as distribution shift, abnormal features and adv... | Published as a conference paper at ICLR 2023 EMPOWERING GRAPH REPRESENTATION LEARNING WITH TEST-TIME GRAPH TRANSFORMATION |
d208637067 | In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, ... | Published as a conference paper at ICLR 2020 SIMPLIFIED ACTION DECODER FOR DEEP MULTI-AGENT REINFORCEMENT LEARNING |
d252715543 | It is unclear how changing the learning rule of a deep neural network alters its learning dynamics and representations. To gain insight into the relationship between learned features, function approximation, and the learning rule, we analyze infinite-width deep networks trained with gradient descent (GD) and biological... | THE INFLUENCE OF LEARNING RULE ON REPRESEN- TATION DYNAMICS IN WIDE NEURAL NETWORKS |
d8217340 | With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. One important factor for continued progress is to... | OBJECT DETECTORS EMERGE IN DEEP SCENE CNNS |
d256697430 | Neural Algorithmic Reasoning is an emerging area of machine learning which seeks to infuse algorithmic computation in neural networks, typically by training neural models to approximate steps of classical algorithms. In this context, much of the current work has focused on learning reachability and shortest path graph ... | Published as a conference paper at ICLR 2023 DUAL ALGORITHMIC REASONING |
d248834106 | This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate visionspecific inductive biases into their architectures, the plain ViT suffers inferior performance on dense predictions due to weak prior assumptions. To address ... | Published as a conference paper at ICLR 2023 VISION TRANSFORMER ADAPTER FOR DENSE PREDICTIONS |
d248798499 | Systematicity, i.e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence. A model with strong systematicity is able to train on small-scale tasks and generalize to large-scale tasks. In this paper, we propose R5, a relational rea... | R5: RULE DISCOVERY WITH REINFORCED AND RE- CURRENT RELATIONAL REASONING |
d237364241 | We propose a framework to analyze how multivariate representations disentangle ground-truth generative factors. A quantitative analysis of disentanglement has been based on metrics designed to compare how one variable explains each generative factor. Current metrics, however, may fail to detect entanglement that involv... | DISENTANGLEMENT ANALYSIS WITH PARTIAL INFOR- MATION DECOMPOSITION |
d203838320 | We present a method for gating deep-learning architectures on a fine-grained level. Individual convolutional maps are turned on/off conditionally on features in the network. This method allows us to train neural networks with a large capacity, but lower inference time than the full network. To achieve this, we introduc... | Batch-Shaped Channel Gated Networks |
d253553209 | Neural Processes (NPs) are popular methods in meta-learning that can estimate predictive uncertainty on target datapoints by conditioning on a context dataset. Previous state-of-the-art method Transformer Neural Processes (TNPs) achieve strong performance but require quadratic computation with respect to the number of ... | Published as a conference paper at ICLR 2023 LATENT BOTTLENECKED ATTENTIVE NEURAL PRO- CESSES |
d235614268 | We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin. When m... | MODEL-BASED MICRO-DATA REINFORCEMENT LEARN- ING: WHAT ARE THE CRUCIAL MODEL PROPERTIES AND WHICH MODEL TO CHOOSE? |
d9747411 | Dataset bias remains a significant barrier towards solving real world computer vision tasks. Though deep convolutional networks have proven to be a competitive approach for image classification, a question remains: have these models have solved the dataset bias problem? In general, training or fine-tuning a state-ofthe... | One-Shot Adaptation of Supervised Deep Convolutional Models |
d29169789 | In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels using a single model. The proposed control problem contains an image restoration dynamic which is modeled by a convolutional RNN. The moving endpoint, which is essentially the... | Dynamically Unfolding Recurrent Restorer DYNAMICALLY UNFOLDING RECURRENT RESTORER: A MOVING ENDPOINT CONTROL METHOD FOR IMAGE RESTORATION |
d262814796 | Batch Normalization (BN) is a commonly used technique to accelerate and stabilize training of deep neural networks. Despite its empirical success, a full theoretical understanding of BN is yet to be developed. In this work, we analyze BN through the lens of convex optimization. We introduce an analytic framework based ... | Published as a conference paper at ICLR 2022 DEMYSTIFYING BATCH NORMALIZATION IN RELU NETWORKS: EQUIVALENT CONVEX OPTIMIZATION MODELS AND IMPLICIT REGULARIZATION |
d15201887 | The creation of practical deep learning data-products often requires parallelization across processors and computers to make deep learning feasible on large data sets, but bottlenecks in communication bandwidth make it difficult to attain good speedups through parallelism. Here we develop and test 8-bit approximation a... | 8-BIT APPROXIMATIONS FOR PARALLELISM IN DEEP LEARNING |
d225094135 | Action-value estimation is a critical component of many reinforcement learning (RL) methods whereby sample complexity relies heavily on how fast a good estimator for action value can be learned. By viewing this problem through the lens of representation learning, good representations of both state and action can facili... | Published as a conference paper at ICLR 2021 LEARNING TO REPRESENT ACTION VALUES AS A HYPERGRAPH ON THE ACTION VERTICES |
d231728364 | 3D pose estimation is a challenging but important task in computer vision. In this work, we show that standard deep learning approaches to 3D pose estimation are not robust when objects are partially occluded or viewed from a previously unseen pose. Inspired by the robustness of generative vision models to partial occl... | Published as a conference paper at ICLR 2021 NEMO: NEURAL MESH MODELS OF CONTRASTIVE FEATURES FOR ROBUST 3D POSE ESTIMATION |
d246863735 | Large language models (LMs) have been shown to memorize parts of their training data, and when prompted appropriately, they will emit the memorized training data verbatim. This is undesirable because memorization violates privacy (exposing user data), degrades utility (repeated easy-to-memorize text is often low qualit... | Published as a conference paper at ICLR 2023 QUANTIFYING MEMORIZATION ACROSS NEURAL LANGUAGE MODELS |
d52912118 | Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance across different data sets, architectures, losses, regularizers, and hyperparameter settings. Intuitively, self-modulation allows the intermedia... | ON SELF MODULATION FOR GENERATIVE ADVER- SARIAL NETWORKS |
d256846732 | Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works present lots of special methods for applying deep neural networks to KT from different perspectives like model architecture, adversarial augment... | SIMPLEKT: A SIMPLE BUT TOUGH-TO-BEAT BASE- LINE FOR KNOWLEDGE TRACING |
d16938012 | Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L 0 norm, however its optimization is NP-hard. Mixed norms, such as L 1 /L ... | Block Coordinate Descent for Sparse NMF |
d251648059 | How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem. A key to solving this problem is to learn low-dimensional state representations from observations, from which an effective policy can be learned. In order to boost t... | Published as a conference paper at ICLR 2022 LEARNING GENERALIZABLE REPRESENTATIONS FOR REINFORCEMENT LEARNING VIA ADAPTIVE META- LEARNER OF BEHAVIORAL SIMILARITIES |
d779900 | We propose the product-of-filters (PoF) model, a generative model that decomposes audio spectra as sparse linear combinations of "filters" in the log-spectral domain. PoF makes similar assumptions to those used in the classic homomorphic filtering approach to signal processing, but replaces decompositions built of basi... | A Generative Product-of-Filters Model of Audio |
d247627899 | Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of every high-level action in existence, but instead only consider ones they deem possible in the first place. By focusing only on what is feas... | Published as a conference paper at ICLR 2022 POSSIBILITY BEFORE UTILITY: LEARNING AND USING HIERARCHICAL AFFORDANCES |
d4739525 | In this paper, we propose a new feature extraction technique for program execution logs. First, we automatically extract complex patterns from a program's behavior graph. Then, we embed these patterns into a continuous space by training an autoencoder. We evaluate the proposed features on a real-world malicious softwar... | Workshop track -ICLR 2017 SEMANTIC EMBEDDINGS FOR PROGRAM BEHAVIOR PATTERNS |
d4986726 | In practice, there are often explicit constraints on what representations or decisions are acceptable in an application of machine learning. For example it may be a legal requirement that a decision must not favour a particular group. Alternatively it can be that that representation of data must not have identifying in... | CENSORING REPRESENTATIONS WITH AN ADVERSARY |
d257254909 | In this paper we present a novel method to estimate 3D human pose and shape from monocular videos. This task requires directly recovering pixel-alignment 3D human pose and body shape from monocular images or videos, which is challenging due to its inherent ambiguity. To improve precision, existing methods highly rely o... | CAPTURING THE MOTION OF EVERY JOINT: 3D HU- MAN POSE AND SHAPE ESTIMATION WITH INDEPEN- DENT TOKENS |
d6600821 | We address a challenging fine-grain classification problem: recognizing a font style from an image of text. In this task, it is very easy to generate lots of rendered font examples but very hard to obtain real-world labeled images. This realto-synthetic domain gap caused poor generalization to new real data in previous... | REAL-WORLD FONT RECOGNITION USING DEEP NET- WORK AND DOMAIN ADAPTATION |
d254877249 | Bilevel optimization plays an essential role in many machine learning tasks, ranging from hyperparameter optimization to meta-learning. Existing studies on bilevel optimization, however, focus on either centralized or synchronous distributed setting. The centralized bilevel optimization approaches require collecting a ... | Published as a conference paper at ICLR 2023 ASYNCHRONOUS DISTRIBUTED BILEVEL OPTIMIZA- TION |
d14741151 | A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain that allow such interac... | Published as a conference paper at ICLR 2017 LEARNING THROUGH DIALOGUE INTERACTIONS BY ASKING QUESTIONS |
d231627799 | Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model's prediction at the test time. Backdoor attacks are notably dangerous since they do not affect the model's performance on clean ... | Published as a conference paper at ICLR 2021 NEURAL ATTENTION DISTILLATION: ERASING BACK- DOOR TRIGGERS FROM DEEP NEURAL NETWORKS |
d7242855 | Recently there has been much interest in understanding why deep neural networks are preferred to shallow networks. We show that, for a large class of piecewise smooth functions, the number of neurons needed by a shallow network to approximate a function is exponentially larger than the corresponding number of neurons n... | Published as a conference paper at ICLR 2017 WHY DEEP NEURAL NETWORKS FOR FUNCTION AP- PROXIMATION? |
d248376976 | In this paper, we propose CLIP-Dissect, a new technique to automatically describe the function of individual hidden neurons inside vision networks. CLIP-Dissect leverages recent advances in multimodal vision/language models to label internal neurons with open-ended concepts without the need for any labeled data or huma... | CLIP-DISSECT: AUTOMATIC DESCRIPTION OF NEU- RON REPRESENTATIONS IN DEEP VISION NETWORKS |
d3633127 | We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model. This approach is in contrast with most frameworks of conditional GANs used in application today, which use the ... | Published as a conference paper at ICLR 2018 CGANS WITH PROJECTION DISCRIMINATOR |
d255569919 | Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks. Learning such representations for complex scenes and tasks remains an open challenge. Towards this goal, we introduce Neural Radiance Field Codebooks (NRC), a scala... | Published as a conference paper at ICLR 2023 NEURAL RADIANCE FIELD CODEBOOKS |
d253098063 | Federated Learning (FL) is a distributed learning paradigm that enables different parties to train a model together for high quality and strong privacy protection. In this scenario, individual participants may get compromised and perform backdoor attacks by poisoning the data (or gradients). Existing work on robust agg... | Published as a conference paper at ICLR 2023 FLIP: A PROVABLE DEFENSE FRAMEWORK FOR BACKDOOR MITIGATION IN FEDERATED LEARNING |
d246294582 | Adversarial examples have posed a severe threat to deep neural networks due to their transferable nature. Currently, various works have paid great efforts to enhance the cross-model transferability, which mostly assume the substitute model is trained in the same domain as the target model. However, in reality, the rele... | Published as a conference paper at ICLR 2022 BEYOND IMAGENET ATTACK: TOWARDS CRAFTING ADVERSARIAL EXAMPLES FOR BLACK-BOX DOMAINS |
d231573479 | We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our m... | Published as a conference paper at ICLR 2021 EVOLVING REINFORCEMENT LEARNING ALGORITHMS |
d257038905 | Deep Policy Gradient (PG) algorithms employ value networks to drive the learning of parameterized policies and reduce the variance of the gradient estimates. However, value function approximation gets stuck in local optima and struggles to fit the actual return, limiting the variance reduction efficacy and leading poli... | Published as a conference paper at ICLR 2023 IMPROVING DEEP POLICY GRADIENTS WITH VALUE FUNCTION SEARCH |
d52876166 | Much attention has been devoted recently to the generalization puzzle in deep learning: large, deep networks can generalize well, but existing theories bounding generalization error are exceedingly loose, and thus cannot explain this striking performance. Furthermore, a major hope is that knowledge may transfer across ... | AN ANALYTIC THEORY OF GENERALIZATION DYNAM- ICS AND TRANSFER LEARNING IN DEEP LINEAR NET- WORKS |
d252355108 | Inspired by Regularized Lottery Ticket Hypothesis, which states that competitive smooth (non-binary) subnetworks exist within a dense network, we propose a fewshot class-incremental learning method referred to as Soft-SubNetworks (SoftNet). Our objective is to learn a sequence of sessions incrementally, where each sess... | Published as a conference paper at ICLR 2023 ON THE SOFT-SUBNETWORK FOR FEW-SHOT CLASS INCREMENTAL LEARNING |
d14403330 | Part-based representations have been shown to be very useful for image classification. Learning part-based models is often viewed as a two-stage problem. First, a collection of informative parts is discovered, using heuristics that promote part distinctiveness and diversity, and then classifiers are trained on the vect... | AUTOMATIC DISCOVERY AND OPTIMIZATION OF PARTS FOR IMAGE CLASSIFICATION |
d209516160 | Sequence generation models are commonly refined with reinforcement learning over user-defined metrics. However, high gradient variance hinders the practical use of this method. To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of corr... | ADAPTIVE CORRELATED MONTE CARLO FOR CON- TEXTUAL CATEGORICAL SEQUENCE GENERATION |
d15075376 | Recursive neural network models and their accompanying vector representations for words have seen success in an array of increasingly semantically sophisticated tasks, but almost nothing is known about their ability to accurately capture the aspects of linguistic meaning that are necessary for interpretation or reasoni... | Can recursive neural tensor networks learn logical reasoning? |
d29778779 | It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. H... | INTRIGUING PROPERTIES OF ADVERSARIAL EXAM- PLES |
d248811447 | Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques. Unfortunately, existing RGB-D SOD methods typically demand large quantity of training images being thoroughly annotated... | Published as a conference paper at ICLR 2022 PROMOTING SALIENCY FROM DEPTH: DEEP UNSUPERVISED RGB-D SALIENCY DETECTION |
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