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d22332081 | Learning a better representation with neural networks is a challenging problem, which was tackled extensively from different prospectives in the past few years. In this work, we focus on learning a representation that could be used for a clustering task and introduce two novel loss components that substantially improve... | Forced to Learn: Discovering Disentangled Representations Without Exhaustive Labels |
d18946424 | We introduce a new method for training deep Boltzmann machines jointly. Prior methods of training DBMs require an initial learning pass that trains the model greedily, one layer at a time, or do not perform well on classification tasks. In our approach, we train all layers of the DBM simultaneously, using a novel train... | Joint Training of Deep Boltzmann Machines for Classification |
d236924584 | Neural networks and other machine learning models compute continuous representations, while humans communicate mostly through discrete symbols. Reconciling these two forms of communication is desirable for generating human-readable interpretations or learning discrete latent variable models, while maintaining endto-end... | Published as a conference paper at ICLR 2022 SPARSE COMMUNICATION VIA MIXED DISTRIBUTIONS |
d257102348 | Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context. At the same time, there is a lack of clear understanding about both the presence and further characteristics of this capability in deep lea... | Published as a conference paper at ICLR 2023 DOES DEEP LEARNING LEARN TO ABSTRACT? A SYSTEMATIC PROBING FRAMEWORK |
d246431036 | Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features -rationale -which guides the model prediction. Unfortunately, the leading rationalization models often rely on data biases, especially shortcut features, to compose rationales and make predictions without ... | Published as a conference paper at ICLR 2022 DISCOVERING INVARIANT RATIONALES FOR GRAPH NEURAL NETWORKS |
d220871147 | We develop an approach to growing deep network architectures over the course of training, driven by a principled combination of accuracy and sparsity objectives. Unlike existing pruning or architecture search techniques that operate on full-sized models or supernet architectures, our method can start from a small, simp... | Published as a conference paper at ICLR 2021 GROWING EFFICIENT DEEP NETWORKS BY STRUCTURED CONTINUOUS SPARSIFICATION |
d213085920 | Many applications of machine learning require a model to make accurate predictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and ... | Published as a conference paper at ICLR 2020 STRATEGIES FOR PRE-TRAINING GRAPH NEURAL NETWORKS |
d252408513 | Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tasks is to employ a retrieve-then-read pipeline that first retrieves a handful of relevant contextual documents from an external corpus such a... | Published as a conference paper at ICLR 2023 GENERATE RATHER THAN RETRIEVE: LARGE LANGU- AGE MODELS ARE STRONG CONTEXT GENERATORS |
d257632075 | This paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW). Existing approaches to differentiable DTW either differentiate through a fixed warping path or apply a differentiable relaxation to the min operator found in the recursive steps us... | Published as a conference paper at ICLR 2023 DEEP DECLARATIVE DYNAMIC TIME WARPING FOR END-TO-END LEARNING OF ALIGNMENT PATHS |
d233004606 | There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors. The idea is to exploit the label hierarchy (e.g., the WordNet ontology) and consider graph distances as a proxy for mistake severity. Surpri... | Published as a conference paper at ICLR 2021 NO COST LIKELIHOOD MANIPULATION AT TEST TIME FOR MAKING BETTER MISTAKES IN DEEP NETWORKS |
d246294519 | We propose to identify directions invariant to a given classifier so that these directions can be controlled in tasks such as style transfer. While orthogonal decomposition is directly identifiable when the given classifier is linear, we formally define a notion of orthogonality in the non-linear case. We also provide ... | Published as a conference paper at ICLR 2022 CONTROLLING DIRECTIONS ORTHOGONAL TO A CLASSIFIER |
d229331643 | Numerical experiments demonstrate that deep neural network classifiers progressively separate class distributions around their mean, achieving linear separability on the training set, and increasing the Fisher discriminant ratio. We explain this mechanism with two types of operators. We prove that a rectifier without b... | SEPARATION AND CONCENTRATION IN DEEP NET- WORKS |
d236635216 | Time series data introduces two key challenges for explainability methods: firstly, observations of the same feature over subsequent time steps are not independent, and secondly, the same feature can have varying importance to model predictions over time. In this paper, we propose Windowed Feature Importance in Time (W... | Published as a conference paper at ICLR 2023 TEMPORAL DEPENDENCIES IN FEATURE IMPORTANCE FOR TIME SERIES PREDICTION |
d256389851 | Since the introduction of Vision Transformers, the landscape of many computer vision tasks (e.g., semantic segmentation), which has been overwhelmingly dominated by CNNs, recently has significantly revolutionized. However, the computational cost and memory requirement render these methods unsuitable on the mobile devic... | Published as a conference paper at ICLR 2023 SEAFORMER: SQUEEZE-ENHANCED AXIAL TRANS- FORMER FOR MOBILE SEMANTIC SEGMENTATION |
d231648154 | Graph-structured data ubiquitously appears in science and engineering. Graph neural networks (GNNs) are designed to exploit the relational inductive bias exhibited in graphs; they have been shown to outperform other forms of neural networks in scenarios where structure information supplements node features. The most co... | Published as a conference paper at ICLR 2021 DIRECTED ACYCLIC GRAPH NEURAL NETWORKS |
d232046284 | The privacy leakage of the model about the training data can be bounded in the differential privacy mechanism. However, for meaningful privacy parameters, a differentially private model degrades the utility drastically when the model comprises a large number of trainable parameters. In this paper, we propose an algorit... | Published as a conference paper at ICLR 2021 DO NOT LET PRIVACY OVERBILL UTILITY: GRADIENT EMBEDDING PERTURBATION FOR PRIVATE LEARNING |
d247594025 | Sequential training from task to task is becoming one of the major objects in deep learning applications such as continual learning and transfer learning. Nevertheless, it remains unclear under what conditions the trained model's performance improves or deteriorates. To deepen our understanding of sequential training, ... | Published as a conference paper at ICLR 2022 LEARNING CURVES FOR CONTINUAL LEARNING IN NEURAL NETWORKS: SELF-KNOWLEDGE TRANSFER AND FORGETTING |
d15538683 | Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples. Taking images as an example, such distortions are often imperceptible, but can result in 100% mis-classification for a state of the art DNN. We study th... | TOWARDS DEEP NEURAL NETWORK ARCHITECTURES ROBUST TO ADVERSARIAL EXAMPLES |
d258967945 | Large pre-trained language models (PLMs) have demonstrated strong performance on natural language understanding (NLU) tasks through fine-tuning. However, fine-tuned models still suffer from overconfident predictions, especially in out-of-domain settings. In this paper, we tackle the problem of calibrating finetuned lan... | Published as a conference paper at ICLR 2023 PRESERVING PRE-TRAINED FEATURES HELPS CALIBRATE FINE-TUNED LANGUAGE MODELS |
d2479619 | Recent work has established an empirically successful framework for adapting learning rates for stochastic gradient descent (SGD). This effectively removes all needs for tuning, while automatically reducing learning rates over time on stationary problems, and permitting learning rates to grow appropriately in nonstatio... | Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients |
d7774489 | Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then levera... | Published as a conference paper at ICLR 2017 STOCHASTIC NEURAL NETWORKS FOR HIERARCHICAL REINFORCEMENT LEARNING |
d3635880 | Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resourcelimited scenarios. A widely-used practice in relevant work assumes that a smallernorm parameter or feature plays a less informative role at the inference time. In th... | Published as a conference paper at ICLR 2018 RETHINKING THE SMALLER-NORM-LESS- INFORMATIVE ASSUMPTION IN CHANNEL PRUNING OF CONVOLUTION LAYERS |
d1793573 | Deep learning has recently led to great successes in tasks such as image recognition (e.gKrizhevsky et al., 2012). However, deep networks are still outmatched by the power and versatility of the brain, perhaps in part due to the richer neuronal computations available to cortical circuits. The challenge is to identify w... | Neuronal Synchrony in Complex-Valued Deep Networks |
d256459658 | Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary predictions trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of... | Published as a conference paper at ICLR 2023 COMPOSITIONAL PROMPT TUNING WITH MOTION CUES FOR OPEN-VOCABULARY VIDEO RELATION DETECTION |
d231627730 | Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks. In this paper, we introduce a simple modelfree algorithm, Randomized Ensembled Double Q-Learning (REDQ), and show that its performance ... | Preprint, under review. RANDOMIZED ENSEMBLED DOUBLE Q-LEARNING: LEARNING FAST WITHOUT A MODEL |
d219531210 | Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techn... | Published as a conference paper at ICLR 2021 DEBERTA: DECODING-ENHANCED BERT WITH DIS- ENTANGLED ATTENTION |
d238744365 | Recently, developed a new neural network architecture based on ∞ -distance functions, which naturally possesses certified ∞ robustness by its construction. Despite the novel design and theoretical foundation, so far the model only achieved comparable performance to conventional networks. In this paper, we make the fol... | Published as a conference paper at ICLR 2022 BOOSTING THE CERTIFIED ROBUSTNESS OF L-INFINITY DISTANCE NETS |
d3513418 | Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of th... | Under review as a conference paper at ICLR 2018 GENERATING NATURAL ADVERSARIAL EXAMPLES |
d213597045 | Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images. In this paper, we propose a simple technique to improve a generalization ability of deep RL agents by... | Published as a conference paper at ICLR 2020 NETWORK RANDOMIZATION: A SIMPLE TECHNIQUE FOR GENERALIZATION IN DEEP REINFORCEMENT LEARNING |
d52938034 | We propose and study a method for learning interpretable representations for the task of regression. Features are represented as networks of multi-type expression trees comprised of activation functions common in neural networks in addition to other elementary functions. Differentiable features are trained via gradient... | LEARNING CONCISE REPRESENTATIONS FOR REGRES- SION BY EVOLVING NETWORKS OF TREES |
d246442139 | There is a fundamental limitation in the prediction performance that a machine learning model can achieve due to the inevitable uncertainty of the prediction target. In classification problems, this can be characterized by the Bayes error, which is the best achievable error with any classifier. The Bayes error can be u... | IS THE PERFORMANCE OF MY DEEP NETWORK TOO GOOD TO BE TRUE? A DIRECT APPROACH TO ESTIMAT- ING THE BAYES ERROR IN BINARY CLASSIFICATION |
d18424928 | The human visual system has a hierarchical structure consisting of layers of processing, such as the retina, V1, V2, etc. Understanding the functional roles of these visual processing layers would help to integrate the psychophysiological and neurophysiological models into a consistent theory of human vision, and would... | Efficient Visual Coding: From Retina To V2 |
d59600012 | Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight generalization error bounds is a central question in statistical learning theory. In this paper, we obtain generalization error bounds for learnin... | Published as a conference paper at ICLR 2020 ON GENERALIZATION ERROR BOUNDS OF NOISY GRADIENT METHODS FOR NON-CONVEX LEARNING |
d253107541 | Overparameterization in deep learning typically refers to settings where a trained neural network (NN) has representational capacity to fit the training data in many ways, some of which generalize well, while others do not. In the case of Recurrent Neural Networks (RNNs), there exists an additional layer of overparamet... | Published as a conference paper at ICLR 2023 LEARNING LOW DIMENSIONAL STATE SPACES WITH OVERPARAMETERIZED RECURRENT NEURAL NETS |
d256827133 | Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization. However, evaluating the goodness of a schedule on the target hardware can be very time-consuming. Traditional approaches as well as previous machine learning ones typically optimi... | Published as a conference paper at ICLR 2023 ROBUST SCHEDULING WITH GFLOWNETS |
d225075866 | Graph Neural Networks (GNNs) are the predominant technique for learning over graphs. However, there is relatively little understanding of why GNNs are successful in practice and whether they are necessary for good performance. Here, we show that for many standard transductive node classification benchmarks, we can exce... | COMBINING LABEL PROPAGATION AND SIMPLE MOD- ELS OUT-PERFORMS GRAPH NEURAL NETWORKS |
d231839638 | Convolutional neural networks (CNNs) constructed natively on the sphere have been developed recently and shown to be highly effective for the analysis of spherical data. While an efficient framework has been formulated, spherical CNNs are nevertheless highly computationally demanding; typically they cannot scale beyond... | Published as a conference paper at ICLR 2022 SCATTERING NETWORKS ON THE SPHERE FOR SCALABLE AND ROTATIONALLY EQUIVARIANT SPHERICAL CNNS |
d250334642 | We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences between data instances across the datasets, to a common low-dimensional Euclidean space. Our approach integrates Multidimensional Scaling (... | Published as a conference paper at ICLR 2023 UNSUPERVISED MANIFOLD ALIGNMENT WITH JOINT MULTIDIMENSIONAL SCALING |
d252683179 | Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variational Bayesian causal inference framework to predict a cell's gene expressions under counterfactual perturbations (perturbations that this cel... | Published as a conference paper at ICLR 2023 PREDICTING CELLULAR RESPONSES WITH VARIATIONAL CAUSAL INFERENCE AND REFINED RELATIONAL INFORMATION |
d249605546 | Estimating the distance of objects is a safety-critical task for autonomous driving. Focusing on short-range objects, existing methods and datasets neglect the equally important long-range objects. In this paper, we introduce a challenging and underexplored task, which we refer to as Long-Range Distance Estimation, as ... | R4D: UTILIZING REFERENCE OBJECTS FOR LONG- RANGE DISTANCE ESTIMATION |
d231603232 | Deep ensembles perform better than a single network thanks to the diversity among their members. Recent approaches regularize predictions to increase diversity; however, they also drastically decrease individual members' performances. In this paper, we argue that learning strategies for deep ensembles need to tackle th... | DICE: DIVERSITY IN DEEP ENSEMBLES VIA CONDI- TIONAL REDUNDANCY ADVERSARIAL ESTIMATION |
d214743496 | Model selection when designing deep learning systems for specific use-cases can be a challenging task as many options exist and it can be difficult to know the trade-off between them. Therefore, we investigate a number of state of the art CNN models for the task of measuring kernel fragmentation in harvested corn silag... | EVALUATION OF MODEL SELECTION FOR KERNEL FRAGMENT RECOGNITION IN CORN SILAGE |
d219708387 | While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost. The sliced Wasserstein distance and its variants improve the computational efficiency through the random projection, yet they suffer from low accur... | Published as a conference paper at ICLR 2022 AUGMENTED SLICED WASSERSTEIN DISTANCES |
d231662243 | Graph neural networks (GNNs) are powerful models that have been successful in various graph representation learning tasks. Whereas gradient boosted decision trees (GBDT) often outperform other machine learning methods when faced with heterogeneous tabular data. But what approach should be used for graphs with tabular n... | Published as a conference paper at ICLR 2021 BOOST THEN CONVOLVE: GRADIENT BOOSTING MEETS GRAPH NEURAL NETWORKS |
d246634193 | We introduce Corrupted Image Modeling (CIM) for self-supervised visual pretraining. CIM uses an auxiliary generator with a small trainable BEiT (Bao et al., 2021) to corrupt the input image instead of using artificial [MASK] tokens, where some patches are randomly selected and replaced with plausible alternatives sam... | Published as a conference paper at ICLR 2023 CORRUPTED IMAGE MODELING FOR SELF-SUPERVISED VISUAL PRE-TRAINING |
d195346934 | We consider the problem of representing a large population's behavior policy that drives the evolution of the population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual acti... | Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations |
d3461154 | Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a m... | Published as a conference paper at ICLR 2018 DORA THE EXPLORER: DIRECTED OUTREACHING REINFORCEMENT ACTION-SELECTION |
d247519194 | Language models typically need to be trained or finetuned in order to acquire new knowledge, which involves updating their weights. We instead envision language models that can simply read and memorize new data at inference time, thus acquiring new knowledge immediately. In this work, we extend language models with the... | Published as a conference paper at ICLR 2022 MEMORIZING TRANSFORMERS |
d210932569 | Recent deep generative models are able to provide photo-realistic images as well as visual or textual content embeddings useful to address various tasks of computer vision and natural language processing. Their usefulness is nevertheless often limited by the lack of control over the generative process or the poor under... | Published as a conference paper at ICLR 2020 CONTROLLING GENERATIVE MODELS WITH CONTINU- OUS FACTORS OF VARIATIONS |
d235358191 | While recent work has shown that scores from models trained by the ubiquitous masked language modeling (MLM) objective effectively discriminate probable from improbable sequences, it is still an open question if these MLMs specify a principled probability distribution over the space of possible sequences. In this paper... | Published as a conference paper at ICLR 2022 EXPOSING THE IMPLICIT ENERGY NETWORKS BEHIND MASKED LANGUAGE MODELS VIA METROPOLIS-HASTINGS |
d253255129 | Today's computer vision models achieve human or near-human level performance across a wide variety of vision tasks. However, their architectures, data, and learning algorithms differ in numerous ways from those that give rise to human vision. In this paper, we investigate the factors that affect the alignment between t... | HUMAN ALIGNMENT OF NEURAL NETWORK REPRE- SENTATIONS |
d8606632 | Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world data sets contain erroneously labeled samples and those errors substantially hinder the learning of very accurate CNN models. In this work, w... | Published as a conference paper at ICLR 2016 AUXILIARY IMAGE REGULARIZATION FOR DEEP CNNS WITH NOISY LABELS |
d13576081 | Neural networks with low-precision weights and activations offer compelling efficiency advantages over their full-precision equivalents. The two most frequently discussed benefits of quantization are reduced memory consumption, and a faster forward pass when implemented with efficient bitwise operations. We propose a t... | Published as a conference paper at ICLR 2018 ATTACKING BINARIZED NEURAL NETWORKS |
d252383681 | Inspired by humans' exceptional ability to master arithmetic and generalize to new problems, we present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machines' capability of learning generalizable concepts at three levels: perception, syntax, and semantics. In HINT, machines are tasked with lea... | A MINIMALIST DATASET FOR SYSTEMATIC GENERAL- IZATION OF PERCEPTION, SYNTAX, AND SEMANTICS |
d256616076 | Previous studies have shown that leveraging domain index can significantly boost domain adaptation performance (Wang et al., 2020; Xu et al., 2022). However, such domain indices are not always available. To address this challenge, we first provide a formal definition of domain index from the probabilistic perspective, ... | DOMAIN-INDEXING VARIATIONAL BAYES: INTER- PRETABLE DOMAIN INDEX FOR DOMAIN ADAPTATION |
d209439505 | Knowledge graph embedding research has overlooked the problem of probability calibration. We show popular embedding models are indeed uncalibrated. That means probability estimates associated to predicted triples are unreliable. We present a novel method to calibrate a model when ground truth negatives are not availabl... | Published as a conference paper at ICLR 2020 PROBABILITY CALIBRATION FOR KNOWLEDGE GRAPH EMBEDDING MODELS |
d258823285 | A Markov network characterizes the conditional independence structure, or Markov property, among a set of random variables. Existing work focuses on specific families of distributions (e.g., exponential families) and/or certain structures of graphs, and most of them can only handle variables of a single data type (cont... | Published as a conference paper at ICLR 2023 GENERALIZED PRECISION MATRIX FOR SCALABLE ESTIMATION OF NONPARAMETRIC MARKOV NET- WORKS |
d159298330 | resolution of past human rights violations through extra-judicial organizations is an advanced step towards resolving the case, whereas a conflict approach can be used to settle the case.The existence of the Human Rights Law provides a new frontier in implementing the principle of restorative justice in the approach of... | Human Rights Court and Truth Reconciliation Commission for the Settlement of Human Rights in Indonesia |
d238583582 | Real world tournaments are almost always intransitive. Recent works have noted that parametric models which assume d dimensional node representations can effectively model intransitive tournaments. However, nothing is known about the structure of the class of tournaments that arise out of any fixed d dimensional repres... | A THEORY OF TOURNAMENT REPRESENTATIONS A PREPRINT |
d90262267 | We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder ... | Published as a conference paper at ICLR 2020 ROBUST SUBSPACE RECOVERY LAYER FOR UNSUPERVISED ANOMALY DETECTION |
d48519159 | Many hallmarks of human intelligence, such as generalizing from limited experience, abstract reasoning and planning, analogical reasoning, creative problem solving, and capacity for language require the ability to consolidate experience into concepts, which act as basic building blocks of understanding and reasoning. W... | Concept Learning with Energy-Based Models |
d254070104 | In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions. Due to the scarcity of explicit user feedback, modern recommender systems typically optimize for the same fixed combination of implicit feedback signals across all users... | Published as a conference paper at ICLR 2023 PERSONALIZED REWARD LEARNING WITH INTERACTION-GROUNDED LEARNING (IGL) |
d17225395 | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | Training Neural Networks with Stochastic Hessian-Free Optimization |
d8990548 | Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based problems, but their efficacy and execution time are dependent on the size of the network. Following recent work in simplifying these networks with model pruning and a novel mapping of work onto GPUs, we design an efficient implementation for... | Published as a conference paper at ICLR 2018 SPARSE PERSISTENT RNNS: SQUEEZING LARGE RECURRENT NETWORKS ON- CHIP |
d257771340 | Molecular representation learning plays a crucial role in AI-assisted drug discovery research. Encoding 3D molecular structures through Euclidean neural networks has become the prevailing method in the geometric deep learning community. However, the equivariance constraints and message passing in Euclidean space may li... | Published as a conference paper at ICLR 2023 LEARNING HARMONIC MOLECULAR REPRESENTA- TIONS ON RIEMANNIAN MANIFOLD |
d250334344 | Interpretable machine learning offers insights into what factors drive a certain prediction of a black-box system. A large number of interpreting methods focus on identifying explanatory input features, which generally fall into two main categories: attribution and selection. A popular attribution-based approach is to ... | Published as a conference paper at ICLR 2023 AN ADDITIVE INSTANCE-WISE APPROACH TO MULTI- CLASS MODEL INTERPRETATION |
d208921206 | Recently, progress has been made in the application of neural networks to the numerical analysis of partial differential equations (PDEs) (cf.[Weinan et al., 2017],[Weinan and Yu, 2018]). In the latter the variational formulation of the Poisson problem is used in order to obtain an objective function a regularised Diri... | DEEP RITZ REVISITED A PREPRINT |
d238419652 | Plug-and-Play (PnP) methods constitute a class of iterative algorithms for imaging problems where regularization is performed by an off-the-shelf denoiser. Although PnP methods can lead to tremendous visual performance for various image problems, the few existing convergence guarantees are based on unrealistic (or subo... | Published as a conference paper at ICLR 2022 GRADIENT STEP DENOISER FOR CONVERGENT PLUG- AND-PLAY |
d3199842 | We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron. The key aspect of this reformulati... | VARIATIONAL AUTO-ENCODED DEEP GAUSSIAN PRO- CESSES |
d2135897 | While most approaches to automatically recognizing entailment relations have used classifiers employing hand engineered features derived from complex natural language processing pipelines, in practice their performance has been only slightly better than bag-of-word pair classifiers using only lexical similarity. The on... | Published as a conference paper at ICLR 2016 REASONING ABOUT ENTAILMENT WITH NEURAL ATTENTION |
d223957176 | Convolutional neural networks often dominate fully-connected counterparts in generalization performance, especially on image classification tasks. This is often explained in terms of "better inductive bias." However, this has not been made mathematically rigorous, and the hurdle is that the sufficiently wide fully-conn... | Published as a conference paper at ICLR 2021 WHY ARE CONVOLUTIONAL NETS MORE SAMPLE- EFFICIENT THAN FULLY-CONNECTED NETS? |
d253117079 | This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both the text and graph structure information with large language models and graph neural networks (GNNs). However, the problem becomes very chal... | Published as a conference paper at ICLR 2023 LEARNING ON LARGE-SCALE TEXT-ATTRIBUTED GRAPHS VIA VARIATIONAL INFERENCE |
d247762924 | The interventional nature of recommendation has attracted increasing attention in recent years. It particularly motivates researchers to formulate learning and evaluating recommendation as causal inference and data missing-not-at-random problems. However, few take seriously the consequence of violating the critical ass... | Published as a conference paper at ICLR 2022 FROM INTERVENTION TO DOMAIN TRANSPORTATION: A NOVEL PERSPECTIVE TO OPTIMIZE RECOMMENDA- TION |
d254974436 | Figure 1: Comparison of GOOD with different baselines. Images in the first column are from validation sets of ADE20K (Zhou et al., 2019). From the second to fourth columns we show the detection results of three open-world object detection methods: OLN Kim et al. (2021), GGN Wang et al. (2022), and our Geometry-guided O... | Published as a conference paper at ICLR 2023 GOOD: EXPLORING GEOMETRIC CUES FOR DETECT- ING OBJECTS IN AN OPEN WORLD (a) Ground truth (20) (b) OLN (13) (c) GGN (14) (d) GOOD (18) (e) Ground truth (22) (f) OLN (6) (g) GGN (6) (h) GOOD (15) AR@100 VOC to Non-VOC of COCO Base Novel |
d238531375 | Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network (GNN), in which each node's representation is computed recursively by aggregating representations ("messages") from its immediate neighbors akin to a star-shaped pattern. MPNNs are appealing for being efficient and scalable, however their... | Published as a conference paper at ICLR 2022 FROM STARS TO SUBGRAPHS: UPLIFTING ANY GNN WITH LOCAL STRUCTURE AWARENESS |
d246485648 | Convolutional neural networks typically contain several downsampling operators, such as strided convolutions or pooling layers, that progressively reduce the resolution of intermediate representations. This provides some shift-invariance while reducing the computational complexity of the whole architecture. A critical ... | Published as a conference paper at ICLR 2022 LEARNING STRIDES IN CONVOLUTIONAL NEURAL NETWORKS |
d247996737 | In this paper, we propose a new, simplified high probability analysis of AdaGrad for smooth, non-convex problems. More specifically, we focus on a particular accelerated gradient (AGD) template (Lan, 2020), through which we recover the original AdaGrad and its variant with averaging, and prove a convergence rate of O(1... | HIGH PROBABILITY BOUNDS FOR A CLASS OF NON- CONVEX ALGORITHMS WITH ADAGRAD STEPSIZE |
d53216389 | We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision. This paper extends this line of work to the problem of aligning multiple languages ... | UNSUPERVISED HYPERALIGNMENT FOR MULTILIN- GUAL WORD EMBEDDINGS |
d258108311 | We propose the gradient-weighted Object Detector Activation Maps (ODAM), a visualized explanation technique for interpreting the predictions of object detectors. Utilizing the gradients of detector targets flowing into the intermediate feature maps, ODAM produces heat maps that show the influence of regions on the dete... | Published as a conference paper at ICLR 2023 ODAM: GRADIENT-BASED INSTANCE-SPECIFIC VI- SUAL EXPLANATIONS FOR OBJECT DETECTION |
d231632854 | The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely on domain-specific data augmentations, which are not easy to generate for all data modalities. Pseudo-labeling (PL) is a general SSL approach... | IN DEFENSE OF PSEUDO-LABELING: AN UNCERTAINTY-AWARE PSEUDO-LABEL SELEC- TION FRAMEWORK FOR SEMI-SUPERVISED LEARNING |
d233241158 | A Recent work has highlighted several advantages of enforcing orthogonality in the weight layers of deep networks, such as maintaining the stability of activations, preserving gradient norms, and enhancing adversarial robustness by enforcing low Lipschitz constants. Although numerous methods exist for enforcing the ort... | |
d13995862 | We propose a deep learning framework for modeling complex high-dimensional densities via Nonlinear Independent Component Estimation (NICE). It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of... | NICE: Non-linear Independent Components Estimation |
d252815535 | Successful and effective communication between humans and AI relies on a shared experience of the world. By training solely on written text, current language models (LMs) miss the grounded experience of humans in the real-world-their failure to relate language to the physical world causes knowledge to be misrepresented... | MIND'S EYE: GROUNDED LANGUAGE MODEL REA- SONING THROUGH SIMULATION |
d233405190 | The learning rate (LR) schedule is one of the most important hyper-parameters needing careful tuning in training DNNs. However, it is also one of the least automated parts of machine learning systems and usually costs significant manual effort and computing. Though there are pre-defined LR schedules and optimizers with... | Published as a conference paper at ICLR 2021 AUTOLRS: AUTOMATIC LEARNING-RATE SCHEDULE BY BAYESIAN OPTIMIZATION ON THE FLY |
d24029589 | Machine learning models with very low test error have been shown to be consistently vulnerable to small, adversarially chosen perturbations of the input. We hypothesize that this counterintuitive behavior is a result of the high-dimensional geometry of the data manifold, and explore this hypothesis on a simple highdime... | The Relationship Between High-Dimensional Geometry and Adversarial Examples |
d3532489 | Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, fault localization, etc. However, most existing program embeddings are based on syntactic features of programs, such as raw token sequences or abstract syntax trees. Unlike im... | Published as a conference paper at ICLR 2018 DYNAMIC NEURAL PROGRAM EMBEDDINGS FOR PRO- GRAM REPAIR |
d256598360 | There is a recent trend of applying multi-agent reinforcement learning (MARL) to train an agent that can cooperate with humans in a zero-shot fashion without using any human data. The typical workflow is to first repeatedly run self-play (SP) to build a policy pool and then train the final adaptive policy against thi... | LEARNING ZERO-SHOT COOPERATION WITH HU- MANS, ASSUMING HUMANS ARE BIASED |
d257663689 | Although deep reinforcement learning (DRL) has many success stories, the largescale deployment of policies learned through these advanced techniques in safetycritical scenarios is hindered by their lack of formal guarantees. Variational Markov Decision Processes (VAE-MDPs) are discrete latent space models that provide ... | WASSERSTEIN AUTO-ENCODED MDPS FORMAL VERIFICATION OF EFFICIENTLY DISTILLED RL POLICIES WITH MANY- SIDED GUARANTEES |
d252735292 | Federated Learning (FL) is a setting for training machine learning models in distributed environments where the clients do not share their raw data but instead send model updates to a server. However, model updates can be subject to attacks and leak private information. Differential Privacy (DP) is a leading mitigation... | CANIFE: CRAFTING CANARIES FOR EMPIRICAL PRI- VACY MEASUREMENT IN FEDERATED LEARNING |
d250334377 | Gradient-based multilevel optimization (MLO) has gained attention as a framework for studying numerous problems, ranging from hyperparameter optimization and meta-learning to neural architecture search and reinforcement learning. However, gradients in MLO, which are obtained by composing best-response Jacobians via the... | Published as a conference paper at ICLR 2023 BETTY: AN AUTOMATIC DIFFERENTIATION LIBRARY FOR MULTILEVEL OPTIMIZATION |
d195345577 | Sample inefficiency is a long-lasting problem in reinforcement learning (RL). The state-ofthe-art uses value function to derive policy while it usually requires an extensive search over the state-action space, which is one reason for the inefficiency. Towards the sample-efficient RL, we propose ranking policy gradient ... | Ranking Policy Gradient Ranking Policy Gradient |
d207880633 | Federated learning improves data privacy and efficiency in machine learning performed over networks of distributed devices, such as mobile phones, IoT and wearable devices, etc. Yet models trained with federated learning can still fail to generalize to new devices due to the problem of domain shift. Domain shift occurs... | Published as a conference paper at ICLR 2020 FEDERATED ADVERSARIAL DOMAIN ADAPTATION |
d30293072 | Several recent works have discussed tree structured sparse coding[8,10,7,3], where N data points in R d written as the d × N matrix X are approximately decomposed into the product of matrices W Z. Here W is a d×K dictionary matrix, and Z is a K ×N matrix of coefficients. In tree structured sparse coding, the rows of Z ... | Tree structured sparse coding on cubes |
d249192278 | We study the Neural Optimal Transport (NOT) algorithm which uses the general optimal transport formulation and learns stochastic transport plans. We show that NOT with the weak quadratic cost may learn fake plans which are not optimal. To resolve this issue, we introduce kernel weak quadratic costs. We show that they p... | Published as a conference paper at ICLR 2023 KERNEL NEURAL OPTIMAL TRANSPORT |
d232146022 | Shapley values have become one of the most popular feature attribution explanation methods. However, most prior work has focused on post-hoc Shapley explanations, which can be computationally demanding due to its exponential time complexity and preclude model regularization based on Shapley explanations during training... | Published as a conference paper at ICLR 2021 SHAPLEY EXPLANATION NETWORKS |
d238419578 | Figure 1: Transform2Act learns a transform-and-control policy that first applies transform actions to design an agent and then controls the designed agent to interact with the environment. The giraffelike agent obtained by Transform2Act can run extremely fast and remain stable (see video).ABSTRACTAn agent's functionali... | TRANSFORM2ACT: LEARNING A TRANSFORM-AND- CONTROL POLICY FOR EFFICIENT AGENT DESIGN |
d258179227 | Decision Transformers (DT) have demonstrated strong performances in offline reinforcement learning settings, but quickly adapting to unseen novel tasks remains challenging. To address this challenge, we propose a new framework, called Hyper-Decision Transformer (HDT), that can generalize to novel tasks from a handful o... | HYPER-DECISION TRANSFORMER FOR EFFICIENT ON- LINE POLICY ADAPTATION |
d246276212 | We introduce a new approach for speech pre-training named SPIRAL which works by learning denoising representation of perturbed data in a teacher-student framework. Specifically, given a speech utterance, we first feed the utterance to a teacher network to obtain corresponding representation. Then the same utterance is ... | SPIRAL: SELF-SUPERVISED PERTURBATION- INVARIANT REPRESENTATION LEARNING FOR SPEECH PRE-TRAINING |
d204512179 | Reinforcement learning encounters major challenges in multi-agent settings, such as scalability and non-stationarity. Recently, value function factorization learning emerges as a promising way to address these challenges in collaborative multi-agent systems. However, existing methods have been focusing on learning full... | LEARNING NEARLY DECOMPOSABLE VALUE FUNC- TIONS VIA COMMUNICATION MINIMIZATION |
d260446846 | We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation th... | SESSION-BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS |
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