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d240070849 | Is it possible to design an universal API for federated learning using which an ad-hoc group of data-holders (agents) collaborate with each other and perform federated learning? Such an API would necessarily need to be model-agnostic i.e. make no assumption about the model architecture being used by the agents, and als... | TOWARDS MODEL-AGNOSTIC FEDERATED LEARNING USING KNOWLEDGE DISTILLATION |
d174802411 | Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries. However, due to lack of prior and inefficiency in leveraging the query and feedback information, existing methods are mostly query-intensive for obtainin... | Published as a conference paper at ICLR 2020 QUERY-EFFICIENT META ATTACK TO DEEP NEURAL NETWORKS |
d8685592 | Deep reinforcement learning (RL) can acquire complex behaviors from low-level inputs, such as images. However, real-world applications of such methods require generalizing to the vast variability of the real world. Deep networks are known to achieve remarkable generalization when provided with massive amounts of labele... | Published as a conference paper at ICLR 2017 GENERALIZING SKILLS WITH SEMI-SUPERVISED REINFORCEMENT LEARNING |
d253202023 | Learning with few labeled tabular samples is often an essential requirement for industrial machine learning applications as varieties of tabular data suffer from high annotation costs or have difficulties in collecting new samples for novel tasks. Despite the utter importance, such a problem is quite under-explored in ... | Published as a conference paper at ICLR 2023 STUNT: FEW-SHOT TABULAR LEARNING WITH SELF-GENERATED TASKS FROM UNLABELED TABLES |
d249375397 | Inverse reinforcement learning (IRL) methods assume that the expert data is generated by an agent optimizing some reward function. However, in many settings, the agent may optimize a reward function subject to some constraints, where the constraints induce behaviors that may be otherwise difficult to express with just ... | Published as a conference paper at ICLR 2023 LEARNING SOFT CONSTRAINTS FROM CONSTRAINED EXPERT DEMONSTRATIONS |
d199441876 | This paper studies learning the representations of whole graphs in both unsupervised and semisupervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting the properties of molecules and community analysis in social networks. Traditional graph kernel based met... | INFOGRAPH: UNSUPERVISED AND SEMI-SUPERVISED GRAPH-LEVEL REPRESENTATION LEARNING VIA MUTUAL INFORMATION MAXIMIZATION A PREPRINT |
d18373301 | We present a feature learning model that learns to encode relationships between images. The model is defined as a Gated Boltzmann Machine, which is constrained such that hidden units that are nearby in space can gate each other's connections. We show how frequency/orientation "columns" as well as topographic filter map... | Feature grouping from spatially constrained multiplicative interaction |
d222291172 | A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL). The procedure uses a computationally efficient Wasserstein natural gradient (WNG) descent that takes advantage of the geometry induced by a Wasserstein penalty to speed optimiza... | Pre-print. Under review. EFFICIENT WASSERSTEIN NATURAL GRADIENTS FOR REINFORCEMENT LEARNING |
d247594888 | Due to numerous breakthroughs in real-world applications brought by machine intelligence, deep neural networks (DNNs) are widely employed in critical applications.However, predictions of DNNs are easily manipulated with imperceptible adversarial perturbations, which impedes the further deployment of DNNs and may result... | SELF-ENSEMBLE ADVERSARIAL TRAINING FOR IM-PROVED ROBUSTNESS |
d238419507 | Federated learning allows multiple parties to collaboratively train a joint model without having to share any local data.It enables applications of machine learning in settings where data is inherently distributed and undisclosable, such as in the medical domain.Joint training is usually achieved by aggregating local m... | |
d233181840 | Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such feedback. Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans h... | Published as a conference paper at ICLR 2021 LEARNING WHAT TO DO BY SIMULATING THE PAST |
d52940306 | Explaining the output of a complicated machine learning model like a deep neural network (DNN) is a central challenge in machine learning. Several proposed local explanation methods address this issue by identifying what dimensions of a single input are most responsible for a DNN's output. The goal of this work is to a... | Workshop track -ICLR 2018 LOCAL EXPLANATION METHODS FOR DEEP NEURAL NETWORKS LACK SENSITIVITY TO PARAMETER VAL- UES |
d257365313 | Humans manipulate various kinds of fluids in their everyday life: creating latte art, scooping floating objects from water, rolling an ice cream cone, etc. Using robots to augment or replace human labors in these daily settings remain as a challenging task due to the multifaceted complexities of fluids. Previous resear... | Published as a conference paper at ICLR 2023 FLUIDLAB: A DIFFERENTIABLE ENVIRONMENT FOR BENCHMARKING COMPLEX FLUID MANIPULATION |
d233306976 | Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicates that CNNs trained on ImageNet are biased towards features that encode textures and that these alone are sufficient to generalize to unseen... | Published as a conference paper at ICLR 2021 DOES ENHANCED SHAPE BIAS IMPROVE NEURAL NETWORK ROBUSTNESS TO COMMON CORRUPTIONS? |
d202750348 | Pairwise Choice Markov Chains (PCMC) have been recently introduced to overcome limitations of choice models based on traditional axioms unable to express empirical observations from modern behavior economics like context effects occurring when a choice between two options is altered by adding a third alternative. The i... | Published as a conference paper at ICLR 2020 PCMC-NET: FEATURE-BASED PAIRWISE CHOICE MARKOV CHAINS |
d248085573 | Population dynamics is the study of temporal and spatial variation in the size of populations of organisms and is a major part of population ecology. One of the main difficulties in analyzing population dynamics is that we can only obtain observation data with coarse time intervals from fixed-point observations due to ... | Published as a conference paper at ICLR 2023 NEURAL LAGRANGIAN SCHRÖDINGER BRIDGE: DIF- FUSION MODELING FOR POPULATION DYNAMICS |
d231627578 | In the present work we study classifiers' decision boundaries via Brownian motion processes in ambient data space and associated probabilistic techniques. Intuitively, our ideas correspond to placing a heat source at the decision boundary and observing how effectively the sample points warm up. We are largely motivated... | Published as a conference paper at ICLR 2021 HEATING UP DECISION BOUNDARIES: ISOCAPACITORY SATURATION, ADVERSARIAL SCENARIOS AND GENERALIZATION BOUNDS |
d255546299 | We identify and overcome two key obstacles in extending the success of BERT-style pre-training, or masked image modeling, to convolutional networks (convnets): (i) convolution operation cannot handle irregular, randomly masked input images; (ii) the single-scale nature of BERT pre-training is inconsistent with convnet'... | Under review as a conference paper at ICLR 2023 DESIGNING BERT FOR CONVOLUTIONAL NETWORKS: SPARSE AND HIERARCHICAL MASKED MODELING |
d56895534 | We propose a new sample-efficient methodology, called Supervised Policy Update (SPU), for deep reinforcement learning. Starting with data generated by the current policy, SPU formulates and solves a constrained optimization problem in the non-parameterized proximal policy space. Using supervised regression, it then con... | SUPERVISED POLICY UPDATE FOR DEEP REINFORCE- MENT LEARNING |
d18634770 | This paper introduces the Metric-Free Natural Gradient (MFNG) algorithm for training Boltzmann Machines. Similar in spirit to the Hessian-Free method of Martens [8], our algorithm belongs to the family of truncated Newton methods and exploits an efficient matrix-vector product to avoid explicitly storing the natural gr... | Metric-Free Natural Gradient for Joint-Training of Boltzmann Machines |
d252683122 | The method of random Fourier features (RFF), proposed in a seminal paper by Rahimi and Recht (NIPS'07), is a powerful technique to find approximate lowdimensional representations of points in (high-dimensional) kernel space, for shift-invariant kernels. While RFF has been analyzed under various notions of error guarant... | Published as a conference paper at ICLR 2023 ON THE RELATIVE ERROR OF RANDOM FOURIER FEATURES FOR PRESERVING KERNEL DISTANCE |
d9453593 | Latent Relation Representations for Universal Schemas | |
d211132493 | Machine learning has shown growing success in recent years. However, current machine learning systems are highly specialized, trained for particular problems or domains, and typically on a single narrow dataset. Human learning, on the other hand, is highly general and adaptable. Never-ending learning is a machine learn... | Published as a conference paper at ICLR 2020 JELLY BEAN WORLD: A TESTBED FOR NEVER-ENDING LEARNING |
d3612479 | To overcome the limitations of Neural Programmer-Interpreters (NPI) in its universality and learnability, we propose the incorporation of combinator abstraction into neural programing and a new NPI architecture to support this abstraction, which we call Combinatory Neural Programmer-Interpreter (CNPI). Combinator abstr... | Published as a conference paper at ICLR 2018 IMPROVING THE UNIVERSALITY AND LEARNABIL- ITY OF NEURAL PROGRAMMER-INTERPRETERS WITH COMBINATOR ABSTRACTION |
d256389689 | Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov decision processes (MDPs), which are used to rollout simulated trajectories and estimate the performance of policies. Model-based OPE methods f... | Published as a conference paper at ICLR 2023 VARIATIONAL LATENT BRANCHING MODEL FOR OFF-POLICY EVALUATION |
d257913312 | Studies on benign overfitting provide insights for the success of overparameterized deep learning models. In this work, we examine whether overfitting is truly benign in real-world classification tasks. We start with the observation that a ResNet model overfits benignly on Cifar10 but not benignly on ImageNet. To under... | Published as a conference paper at ICLR 2023 BENIGN OVERFITTING IN CLASSIFICATION: PROVABLY COUNTER LABEL NOISE WITH LARGER MODELS |
d257365860 | Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years. Existing literature mainly focus on selecting a subgraph, through combinatorial optimization, to provide faithful explanations. However, the exponential size of candidate subgraphs limits the appli... | Published as a conference paper at ICLR 2023 DAG MATTERS! GFLOWNETS ENHANCED EXPLAINER FOR GRAPH NEURAL NETWORKS |
d239016966 | Self-supervised visual representation learning aims to learn useful representations without relying on human annotations. Joint embedding approach bases on maximizing the agreement between embedding vectors from different views of the same image. Various methods have been proposed to solve the collapsing problem where ... | UNDERSTANDING DIMENSIONAL COLLAPSE IN CON- TRASTIVE SELF-SUPERVISED LEARNING |
d246241075 | Standard model-free reinforcement learning algorithms optimize a policy that generates the action to be taken in the current time step in order to maximize expected future return. While flexible, it faces difficulties arising from the inefficient exploration due to its single step nature. In this work, we present Gener... | GENERATIVE PLANNING FOR TEMPORALLY COORDI- NATED EXPLORATION IN REINFORCEMENT LEARNING |
d257279766 | Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due to the double robustness property, that is, DR is unbiased when either ... | TDR-CL: TARGETED DOUBLY ROBUST COLLABORA- TIVE LEARNING FOR DEBIASED RECOMMENDATIONS |
d236318431 | Recent studies show that deep neural networks (DNN) are vulnerable to adversarial examples, which aim to mislead DNNs by adding perturbations with small magnitude. To defend against such attacks, both empirical and theoretical defense approaches have been extensively studied for a single ML model. In this work, we aim ... | Published as a conference paper at ICLR 2022 ON THE CERTIFIED ROBUSTNESS FOR ENSEMBLE MODELS AND BEYOND |
d219401872 | Pretrained Transformer-based language models (LMs) display remarkable natural language generation capabilities. With their immense potential, controlling text generation of such LMs is getting attention. While there are studies that seek to control high-level attributes (such as sentiment and topic) of generated text, ... | Published as a conference paper at ICLR 2021 COCON: A SELF-SUPERVISED APPROACH FOR CONTROLLED TEXT GENERATION |
d257364758 | We consider representation learning for proteins with 3D structures. We build 3D graphs based on protein structures and develop graph networks to learn their representations. Depending on the levels of details that we wish to capture, protein representations can be computed at different levels, e.g., the amino acid, ba... | Published as a conference paper at ICLR 2023 LEARNING HIERARCHICAL PROTEIN REPRESENTA- TIONS VIA COMPLETE 3D GRAPH NETWORKS |
d15147584 | Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data. However, they are still prohibitively expensive to train and apply for problems containing millions of classes in the output layer. Based on the observ... | DEEP NETWORKS WITH LARGE OUTPUT SPACES |
d2009318 | Learning an algorithm from examples is a fundamental problem that has been widely studied. It has been addressed using neural networks too, in particular by Neural Turing Machines (NTMs). These are fully differentiable computers that use backpropagation to learn their own programming. Despite their appeal NTMs have a w... | NEURAL GPUS LEARN ALGORITHMS |
d257254892 | Kohn-Sham Density Functional Theory (KS-DFT) has been traditionally solved by the Self-Consistent Field (SCF) method.Behind the SCF loop is the physics intuition of solving a system of non-interactive single-electron wave functions under an effective potential.In this work, we propose a deep learning approach to KS-DFT... | |
d3942288 | Motivated by recent work on deep neural network (DNN)-based image compression methods showing potential improvements in image quality, savings in storage, and bandwidth reduction, we propose to perform image understanding tasks such as classification and segmentation directly on the compressed representations produced ... | Published as a conference paper at ICLR 2018 TOWARDS IMAGE UNDERSTANDING FROM DEEP COMPRESSION WITHOUT DECODING |
d255546240 | A general framework of unsupervised learning for combinatorial optimization (CO) is to train a neural network (NN) whose output gives a problem solution by directly optimizing the CO objective. Albeit with some advantages over traditional solvers, the current framework optimizes an averaged performance over the distrib... | UNSUPERVISED LEARNING FOR COMBINATORIAL OP- TIMIZATION NEEDS META LEARNING |
d8605623 | Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries. Recently further developed Deep Neural Network sheds light on automatically learning high-level image repre... | Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data |
d231967791 | We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation d... | Published as a conference paper at ICLR 2021 LEARNING NEURAL GENERATIVE DYNAMICS FOR MOLECULAR CONFORMATION GENERATION |
d252089424 | Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they learn a new task from a few task demonstrations, without any parameter updates. This work examines the implications of in-context learning fo... | SELECTIVE ANNOTATION MAKES LANGUAGE MOD- ELS BETTER FEW-SHOT LEARNERS |
d18380109 | A key characteristic of work on deep learning and neural networks in general is that it relies on representations of the input that support generalization, robust inference, domain adaptation and other desirable functionalities. Much recent progress in the field has focused on efficient and effective methods for comput... | Two SVDs produce more focal deep learning representations |
d52926194 | Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. In this paper, we present AutoLoss,... | AutoLoss: Learning Discrete Schedules for Alternate Optimization |
d14630648 | Recently, nested dropout was proposed as a method for ordering representation units in autoencoders by their information content, without diminishing reconstruction cost(Rippel et al., 2014). However, it has only been applied to training fully-connected autoencoders in an unsupervised setting. We explore the impact of ... | Under review as a workshop contribution at ICLR 2015 LEARNING COMPACT CONVOLUTIONAL NEURAL NETWORKS WITH NESTED DROPOUT |
d238857085 | Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, we propose and study the problem of graph condensation for graph neural networks (GNNs). Specifically, we aim to condense the large, original... | GRAPH CONDENSATION FOR GRAPH NEURAL NET- WORKS |
d252668617 | Statistical inference under market equilibrium effects has attracted increasing attention recently. In this paper we focus on the specific case of linear Fisher markets. They have been widely use in fair resource allocation of food/blood donations and budget management in large-scale Internet ad auctions. In resource a... | Under review STATISTICAL INFERENCE FOR FISHER MARKET EQUI- LIBRIUM |
d12529428 | Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polic... | Workshop track -ICLR 2017 DELVING INTO ADVERSARIAL ATTACKS ON DEEP POLICIES |
d256868328 | In this paper, we introduce the notion of replicable policies in the context of stochastic bandits, one of the canonical problems in interactive learning. A policy in the bandit environment is called replicable if it pulls, with high probability, the exact same sequence of arms in two different and independent executio... | |
d5176587 | We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights. NoisyNet ... | Published as a conference paper at ICLR 2018 NOISY NETWORKS FOR EXPLORATION |
d252668454 | The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images. However, it remains unknown whether this capability can also apply to the medical image domain. This paper thoroughly studies the knowledge transferability of pre-trained VLMs to the medical doma... | MEDICAL IMAGE UNDERSTANDING WITH PRE- TRAINED VISION LANGUAGE MODELS: A COM- PREHENSIVE STUDY |
d246867139 | We strive to learn a model from a set of source domains that generalizes well to unseen target domains. The main challenge in such a domain generalization scenario is the unavailability of any target domain data during training, resulting in the learned model not being explicitly adapted to the unseen target domains. W... | Published as a conference paper at ICLR 2022 LEARNING TO GENERALIZE ACROSS DOMAINS ON SINGLE TEST SAMPLES |
d247447665 | Offline reinforcement learning algorithms promise to be applicable in settings where a fixed dataset is available and no new experience can be acquired. However, such formulation is inevitably offline-data-hungry and, in practice, collecting a large offline dataset for one specific task over one specific environment is... | Published as a conference paper at ICLR 2022 DARA: DYNAMICS-AWARE REWARD AUGMENTATION IN OFFLINE REINFORCEMENT LEARNING |
d209444912 | In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy. In this study, we propose an RL algorith... | Published as a conference paper at ICLR 2020 VARIATIONAL RECURRENT MODELS FOR SOLVING PARTIALLY OBSERVABLE CONTROL TASKS |
d3611540 | We describe an end-to-end trainable model for image compression based on variational autoencoders. The model incorporates a hyperprior to effectively capture spatial dependencies in the latent representation. This hyperprior relates to side information, a concept universal to virtually all modern image codecs, but larg... | Published as a conference paper at ICLR 2018 VARIATIONAL IMAGE COMPRESSION WITH A SCALE HYPERPRIOR |
d67855429 | Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data. Nevertheless, these networks often generalize well in practice. It has also been observed that trained networks can often be "compressed" to much smaller representations. The purpose of this paper is to connect... | NON-VACUOUS GENERALIZATION BOUNDS AT THE IM- AGENET SCALE: A PAC-BAYESIAN COMPRESSION APPROACH |
d232076011 | We empirically demonstrate that full-batch gradient descent on neural network training objectives typically operates in a regime we call the Edge of Stability. In this regime, the maximum eigenvalue of the training loss Hessian hovers just above the value 2/(step size), and the training loss behaves non-monotonically o... | Published as a conference paper at ICLR 2021 GRADIENT DESCENT ON NEURAL NETWORKS TYPI- CALLY OCCURS AT THE EDGE OF STABILITY |
d4043645 | Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces. To mitigate this issue, we derive a bias-free action-dependent ba... | Published as a conference paper at ICLR 2018 VARIANCE REDUCTION FOR POLICY GRADIENT WITH ACTION-DEPENDENT FACTORIZED BASELINES |
d233454709 | Most supervised machine learning tasks are subject to irreducible prediction errors. Probabilistic predictive models address this limitation by providing probability distributions that represent a belief over plausible targets, rather than point estimates. Such models can be a valuable tool in decision-making under unc... | Published as a conference paper at ICLR 2021 CALIBRATION TESTS BEYOND CLASSIFICATION |
d7788178 | Deep Learning models enjoy considerable success in Natural Language Processing. While deep architectures produce useful representations that lead to improvements in various tasks, they are often difficult to interpret. This makes the analysis of learned structures particularly difficult. In this paper, we rely on empir... | Cutting Recursive Autoencoder Trees |
d235605948 | We introduce a new family of particle evolution samplers suitable for constrained domains and non-Euclidean geometries. Stein Variational Mirror Descent and Mirrored Stein Variational Gradient Descent minimize the Kullback-Leibler (KL) divergence to constrained target distributions by evolving particles in a dual space... | Published as a conference paper at ICLR 2022 SAMPLING WITH MIRRORED STEIN OPERATORS |
d3330768 | We present a parameterized synthetic dataset called Moving Symbols to support the objective study of video prediction networks. Using several instantiations of the dataset in which variation is explicitly controlled, we highlight issues in an existing state-of-the-art approach and propose the use of a performance metri... | Workshop track -ICLR 2018 A DATASET TO EVALUATE THE REPRESENTATIONS LEARNED BY VIDEO PREDICTION MODELS |
d227227885 | Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have used variational autoe... | SELF-SUPERVISED VISUAL REINFORCEMENT LEARN- ING WITH OBJECT-CENTRIC REPRESENTATIONS |
d14282237 | In this paper we propose a model that combines the strengths of RNNs and SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used for efficient, large scale unsupervised learning on time series data, mapping the time series data to a latent vector representation. The model is generative, such that ... | Under review as a workshop contribution at ICLR 2015 VARIATIONAL RECURRENT AUTO-ENCODERS |
d246864044 | We explain why directly changing the prior can be a surprisingly ineffective mechanism for incorporating inductive biases into variational auto-encoders (VAEs), and introduce a simple and effective alternative approach: Intermediary Latent Space VAEs (InteL-VAEs). InteL-VAEs use an intermediary set of latent variables ... | Published as a conference paper at ICLR 2022 ON INCORPORATING INDUCTIVE BIASES INTO VAES |
d257102934 | Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning.Interestingly, we found that although both RNN and SAM could lead to a good performance, in practice neither of them is always necessary.In this paper, we propos... | DO WE REALLY NEED COMPLICATED MODEL ARCHI-TECTURES FOR TEMPORAL NETWORKS? |
d196831891 | The performance of deep network learning strongly depends on the choice of the non-linear activation function associated with each neuron. However, deciding on the best activation is non-trivial, and the choice depends on the architecture, hyper-parameters, and even on the dataset. Typically these activations are fixed... | PADÉ ACTIVATION UNITS: END-TO-END LEARNING OF FLEXIBLE ACTIVATION FUNCTIONS IN DEEP NET- WORKS |
d229156392 | In this paper, we derive generalization bounds for the two primary classes of graph neural networks (GNNs), namely graph convolutional networks (GCNs) and message passing GNNs (MPGNNs), via a PAC-Bayesian approach. Our result reveals that the maximum node degree and spectral norm of the weights govern the generalizatio... | Vector Institute 3 , Canadian Institute for Advanced Research 4 |
d257205792 | Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations. Invariances established during pre-training can be interpreted as strong inductive biases. However these may or may not be helpful, depending on if they match ... | Published as a conference paper at ICLR 2023 AMORTISED INVARIANCE LEARNING FOR CONTRASTIVE SELF-SUPERVISION |
d16589282 | A rekindled the interest in auto-encoder algorithms has been spurred by recent work on deep learning. Current efforts have been directed towards effective training of auto-encoder architectures with a large number of coding units. Here, we propose a learning algorithm for auto-encoders based on a rate-distortion object... | Rate-Distortion Auto-Encoders |
d246485884 | The discovery of structure from time series data is a key problem in fields of study working with complex systems. Most identifiability results and learning algorithms assume the underlying dynamics to be discrete in time. Comparatively few, in contrast, explicitly define dependencies in infinitesimal intervals of time... | Published as a conference paper at ICLR 2022 NEURAL GRAPHICAL MODELLING IN CONTINUOUS- TIME: CONSISTENCY GUARANTEES AND ALGORITHMS |
d3548196 | In practice it is often found that large over-parameterized neural networks generalize better than their smaller counterparts, an observation that appears to conflict with classical notions of function complexity, which typically favor smaller models. In this work, we investigate this tension between complexity and gen... | Published as a conference paper at ICLR 2018 SENSITIVITY AND GENERALIZATION IN NEURAL NETWORKS: AN EMPIRICAL STUDY |
d59536625 | Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons. Though, given the lack of sample efficiency in current learning methods, reaching this goal may require substantial research efforts. We introduce the BabyAI research plat... | BABYAI: A PLATFORM TO STUDY THE SAMPLE EFFI- CIENCY OF GROUNDED LANGUAGE LEARNING |
d249625742 | Sequential data naturally have different lengths in many domains, with some very long sequences. As an important modeling tool, neural attention should capture long-range interaction in such sequences. However, most existing neural attention models admit only short sequences, or they have to employ chunking or padding ... | CHORDMIXER: A SCALABLE NEURAL ATTENTION MODEL FOR SEQUENCES WITH DIFFERENT LENGTHS |
d237154262 | Graph matching (GM) has been a building block in various areas including computer vision and pattern recognition. Despite recent impressive progress, existing deep GM methods often have obvious difficulty in handling outliers, which are ubiquitous in practice. We propose a deep reinforcement learning based approach RGM... | Published as a conference paper at ICLR 2023 REVOCABLE DEEP REINFORCEMENT LEARNING WITH AFFINITY REGULARIZATION FOR OUTLIER-ROBUST GRAPH MATCHING |
d256358906 | This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e.g., Synonym Replacement) or fine-tune general-purpose pretrained language models (e.g., GPT2) using the limited training instances to produ... | KN O WDA: ALL-IN-ONE KNOWLEDGE MIXTURE MODEL FOR DATA AUGMENTATION IN LOW- RESOURCE NLP TASKS |
d226226438 | One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm. We introduce the novel concept ofapproximation of datasets, obtaining datasets which are much smaller than or are significant corruptions of the original training data while maintaining similar model perfor... | DATASET META-LEARNING FROM KERNEL RIDGE- REGRESSION |
d15602035 | Although the latest high-end smartphone has powerful CPU and GPU, running deeper convolutional neural networks (CNNs) for complex tasks such as Ima-geNet classification on mobile devices is challenging. To deploy deep CNNs on mobile devices, we present a simple and effective scheme to compress the entire CNN, which we ... | COMPRESSION OF DEEP CONVOLUTIONAL NEURAL NETWORKS FOR FAST AND LOW POWER MOBILE AP- PLICATIONS |
d203591459 | Background: Recent developments have made it possible to accelerate neural networks training significantly using large batch sizes and data parallelism. Training in an asynchronous fashion, where delay occurs, can make training even more scalable. However, asynchronous training has its pitfalls, mainly a degradation in... | ASYNCHRONOUS TRAINING OF NEURAL NETWORKS? |
d231719413 | While most neural generative models generate outputs in a single pass, the human creative process is usually one of iterative building and refinement. Recent work has proposed models of editing processes, but these mostly focus on editing sequential data and/or only model a single editing pass. In this paper, we presen... | Published as a conference paper at ICLR 2021 LEARNING STRUCTURAL EDITS VIA INCREMENTAL TREE TRANSFORMATIONS |
d257038366 | Although sparse training has been successfully used in various resource-limited deep learning tasks to save memory, accelerate training, and reduce inference time, the reliability of the produced sparse models remains unexplored. Previous research has shown that deep neural networks tend to be over-confident, and we fi... | Published as a conference paper at ICLR 2023 CALIBRATING THE RIGGED LOTTERY: MAKING ALL TICKETS RELIABLE |
d52909682 | We present a new algorithm to train a robust neural network against adversarial attacks. Our algorithm is motivated by the following two ideas. First, although recent work has demonstrated that fusing randomness can improve the robustness of neural networks(Liu et al., 2017), we noticed that adding noise blindly to all... | AD V-BNN: IMPROVED ADVERSARIAL DEFENSE THROUGH ROBUST BAYESIAN NEURAL NETWORK |
d238408070 | Cross-domain object detection is more challenging than object classification since multiple objects exist in an image and the location of each object is unknown in the unlabeled target domain. As a result, when we adapt features of different objects to enhance the transferability of the detector, the features of the fo... | Published as a conference paper at ICLR 2022 DECOUPLED ADAPTATION FOR CROSS-DOMAIN OBJECT DETECTION |
d17968003 | Deep convolutional networks have witnessed unprecedented success in various machine learning applications. Formal understanding on what makes these networks so successful is gradually unfolding, but for the most part there are still significant mysteries to unravel. The inductive bias, which reflects prior knowledge em... | Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design Amnon Shashua |
d233864801 | Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supervised generative models approximating an ELBO are not able to fulfill all desired requirements of multimodal models: their posterior approx... | Published as a conference paper at ICLR 2021 GENERALIZED MULTIMODAL ELBO |
d7147309 | Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image. However, at test time the model is expected to generate the entire sequence from scratch. This discre... | SEQUENCE LEVEL TRAINING WITH RECURRENT NEURAL NETWORKS |
d94224 | Recently proposed neural network activation functions such as rectified linear, maxout, and local winner-take-all have allowed for faster and more effective training of deep neural architectures on large and complex datasets. The common trait among these functions is that they implement local competition between small ... | Understanding Locally Competitive Networks |
d59222711 | Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically requires to explicitly label all the factors of interest in training images. To alleviate the annotat... | Learning Disentangled Representations with Reference-Based Variational Autoencoders |
d6706414 | Several machine learning models, including neural networks, consistently misclassify adversarial examples-inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early... | Published as a conference paper at ICLR 2015 EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES |
d248987086 | Federated Learning (FL) is a machine learning paradigm where many clients collaboratively learn a shared global model with decentralized training data. Personalized FL additionally adapts the global model to different clients, achieving promising results on consistent local training and test distributions. However, for... | TEST-TIME ROBUST PERSONALIZATION FOR FEDER- ATED LEARNING |
d247594725 | Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change. However, the GNNs that have proven most effective for this task are memory inten... | Published as a conference paper at ICLR 2022 TOWARDS TRAINING BILLION PARAMETER GRAPH NEURAL NETWORKS FOR ATOMIC SIMULATIONS |
d5217869 | The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound. We give an alternate interpretation of this procedure: that it optimizes the standard variational lower bound, but using a more complex distribut... | Workshop track -ICLR 2017 REINTERPRETING IMPORTANCE-WEIGHTED AUTOENCODERS |
d238744430 | Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer. This distance sensitivity with respect to the data aids in tasks such as uncertainty calibration and out-of-distribution (OOD) detection. In previ... | META LEARNING LOW RANK COVARIANCE FACTORS FOR ENERGY-BASED DETERMINISTIC UNCERTAINTY |
d256846467 | Diffusion-based generative models (DBGMs) perturb data to a target noise distribution and reverse this process to generate samples. The choice of noising process, or inference diffusion process, affects both likelihoods and sample quality. For example, extending the inference process with auxiliary variables leads to i... | Published as a conference paper at ICLR 2023 WHERE TO DIFFUSE, HOW TO DIFFUSE, AND HOW TO GET BACK: AUTOMATED LEARNING FOR MULTIVARI- ATE DIFFUSIONS |
d13880 | Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations. For each patient visit (or episode), sensor data and lab test results are recorded in the patient's Electronic Health Record (EHR). While potentially containing a wealth of insights, the data is diff... | Published as a conference paper at ICLR 2016 LEARNING TO DIAGNOSE WITH LSTM RECURRENT NEURAL NETWORKS |
d67855617 | We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the relations. In this paper, we present a new approach for knowledge graph embeddi... | ROTATE: KNOWLEDGE GRAPH EMBEDDING BY RELA- TIONAL ROTATION IN COMPLEX SPACE |
d238582670 | We propose a novel method called Long Expressive Memory (LEM) for learning long-term sequential dependencies. LEM is gradient-based, it can efficiently process sequential tasks with very long-term dependencies, and it is sufficiently expressive to be able to learn complicated input-output maps. To derive LEM, we consid... | Published as a conference paper at ICLR 2022 LONG EXPRESSIVE MEMORY FOR SEQUENCE MODELING |
d252668479 | The extent to which text-only language models (LMs) learn to represent features of the non-linguistic world is an open question. Prior work has shown that pretrained LMs can be taught to caption images when a vision model's parameters are optimized to encode images in the language space. We test a stronger hypothesis: ... | Published as a conference paper at ICLR 2023 LINEARLY MAPPING FROM IMAGE TO TEXT SPACE |
d257353885 | Semi-supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model's performance. Even though the domain has received a considerable amount of attention in the past years, most methods present the common drawback of lacking theoretical guarantees. Our starting point is to notice... | SAFE SEMI-SUPERVISED LEARNING VIA DEBIASING |
d209444454 | We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neural architecture search (NAS), FasterSeg is discovered from a novel and broader search space integrating multi-resolution branches, that has b... | Published as a conference paper at ICLR 2020 FASTERSEG: SEARCHING FOR FASTER REAL-TIME SEMANTIC SEGMENTATION |
d238419702 | We perform approximate inference in state-space models with nonlinear state transitions. Without parameterizing a generative model, we apply Bayesian update formulas using a local linearity approximation parameterized by neural networks. This comes accompanied by a maximum likelihood objective that requires no supervis... | SELF-SUPERVISED INFERENCE IN STATE-SPACE MOD- ELS |
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