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d238634102 | The ability to identify whether or not a test sample belongs to one of the semantic classes in a classifier's training set is critical to practical deployment of the model. This task is termed open-set recognition (OSR) and has received significant attention in recent years. In this paper, we first demonstrate that the... | OPEN-SET RECOGNITION: A GOOD CLOSED-SET CLASSIFIER IS ALL YOU NEED? |
d203610479 | This work provides the first unifying theoretical framework for node embeddings and structural graph representations, bridging methods like matrix factorization and graph neural networks. Using invariant theory, we show that the relationship between structural representations and node embeddings is analogous to that of... | ON THE EQUIVALENCE BETWEEN NODE EMBEDDINGS AND STRUCTURAL GRAPH REPRESENTATIONS |
d59294230 | To improve how neural networks function it is crucial to understand their learning process. The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task. However, empirical evid... | ADAPTIVE ESTIMATORS SHOW INFORMATION COM- PRESSION IN DEEP NEURAL NETWORKS |
d256697483 | Variational Autoencoders (VAEs) were originally motivated(Kingma & Welling, 2014)as probabilistic generative models in which one performs approximate Bayesian inference. The proposal of β-VAEs (Higgins et al., 2017) breaks this interpretation and generalizes VAEs to application domains beyond generative modeling (e.g.,... | Published as a conference paper at ICLR 2023 TRADING INFORMATION BETWEEN LATENTS IN HIERARCHICAL VARIATIONAL AUTOENCODERS |
d14249712 | Recent results suggest that state-of-the-art saliency models perform far from optimal in predicting fixations. This lack in performance has been attributed to an inability to model the influence of high-level image features such as objects. Recent seminal advances in applying deep neural networks to tasks like object r... | Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet |
d173991122 | Artificial neural networks suffer from catastrophic forgetting when they are sequentially trained on multiple tasks. To overcome this problem, we present a novel approach based on task-conditioned hypernetworks, i.e., networks that generate the weights of a target model based on task identity. Continual learning (CL) i... | Published as a conference paper at ICLR 2020 CONTINUAL LEARNING WITH HYPERNETWORKS |
d209315655 | We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds. These thresholds can have fine-grained layer-wise adjustments dynamically via backpropagation... | Published as a conference paper at ICLR 2020 DYNAMIC SPARSE TRAINING: FIND EFFICIENT SPARSE NETWORK FROM SCRATCH WITH TRAINABLE MASKED LAYERS |
d232013963 | Hamiltonian mechanics is an effective tool to represent many physical processes with concise yet well-generalized mathematical expressions. A well-modeled Hamiltonian makes it easy for researchers to analyze and forecast many related phenomena that are governed by the same physical law. However, in general, identifying... | IDENTIFYING PHYSICAL LAW OF HAMILTONIAN SYS- TEMS VIA META-LEARNING |
d5882977 | We consider the problem of learning general-purpose, paraphrastic sentence embeddings based on supervision from the Paraphrase Database(Ganitkevitch et al., 2013). We compare six compositional architectures, evaluating them on annotated textual similarity datasets drawn both from the same distribution as the training d... | TOWARDS UNIVERSAL PARAPHRASTIC SENTENCE EMBEDDINGS |
d38234424 | Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian. After a trained model is obtained, one can sample the Generator in various forms for exploration and understanding,... | OPTIMAL TRANSPORT MAPS FOR DISTRIBUTION PRE- SERVING OPERATIONS ON LATENT SPACES OF GENER- ATIVE MODELS |
d53033211 | We introduce a novel method that enables parameter-efficient transfer and multitask learning. The basic approach is to allow a model patch -a small set of parameters -to specialize to each task, instead of fine-tuning the last layer or the entire network. For instance, we show that learning a set of scales and biases a... | K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning |
d235658908 | Perceiving and manipulating 3D articulated objects (e.g., cabinets, doors) in human environments is an important yet challenging task for future home-assistant robots. The space of 3D articulated objects is exceptionally rich in their myriad semantic categories, diverse shape geometry, and complicated part functionalit... | VAT-MART: LEARNING VISUAL ACTION TRAJEC- TORY PROPOSALS FOR MANIPULATING 3D ARTICU- LATED OBJECTS |
d7355762 | The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function.In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge.We train a large 12-layer convolutional neural network by su... | Under review as a conference paper at ICLR 2015 MOVE EVALUATION IN GO USING DEEP CONVOLUTIONAL NEURAL NETWORKS |
d1758096 | Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems during training are overcome by Wasserstein GANs which minimize the distance between the model and the empirical distribution in terms of a dif... | On the regularization of WGANs ON THE REGULARIZATION OF WASSERSTEIN GANS |
d16937325 | Deep networks are successfully used as classification models yielding state-ofthe-art results when trained on a large number of labeled samples. These models, however, are usually much less suited for semi-supervised problems because of their tendency to overfit easily when trained on small amounts of data. In this wor... | SEMI-SUPERVISED DEEP LEARNING BY METRIC EM- BEDDING |
d247154673 | We propose a reinforcement learning based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal formulated using the exemplary set by ordinal metric learning. Our proposed method enables test-time po... | Published as a conference paper at ICLR 2022 LEARNING TRANSFERABLE REWARD FOR QUERY OBJECT LOCALIZATION WITH POLICY ADAPTATION |
d259137792 | Deep learning models have proven to be successful in a wide range of machine learning tasks. Yet, they are often highly sensitive to perturbations on the input data which can lead to incorrect decisions with high confidence, hampering their deployment for practical use-cases. Thus, finding architectures that are (more)... | Published as a conference paper at ICLR 2023 NEURAL ARCHITECTURE DESIGN AND ROBUSTNESS: A DATASET |
d57573752 | We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. Relying on the tridiagonal decomposition of the Lanczos algorithm, we not only efficiently exploit multi-scale information via fast approximated computation of... | LANCZOSNET: MULTI-SCALE DEEP GRAPH CONVO- LUTIONAL NETWORKS |
d252683098 | The recent observation of neural power-law scaling relations has made a significant impact in the field of deep learning. A substantial amount of attention has been dedicated as a consequence to the description of scaling laws, although mostly for supervised learning and only to a reduced extent for reinforcement learn... | SCALING LAWS FOR A MULTI-AGENT REINFORCE- MENT LEARNING MODEL |
d247748621 | Recently, contrastive learning has risen to be a promising approach for large-scale self-supervised learning. However, theoretical understanding of how it works is still unclear. In this paper, we propose a new guarantee on the downstream performance without resorting to the conditional independence assumption that is ... | Published as a conference paper at ICLR 2022 CHAOS IS A LADDER: A NEW THEORETICAL UN- DERSTANDING OF CONTRASTIVE LEARNING VIA AUG- MENTATION OVERLAP |
d257766465 | Graph neural networks (GNNs) for temporal graphs have recently attracted increasing attentions, where a common assumption is that the class set for nodes is closed. However, in real-world scenarios, it often faces the open set problem with the dynamically increased class set as the time passes by. This will bring two b... | TOWARDS OPEN TEMPORAL GRAPH NEURAL NET- WORKS |
d247613106 | We study the optimization of wide neural networks (NNs) via gradient flow (GF) in setups that allow feature learning while admitting non-asymptotic global convergence guarantees. First, for wide shallow NNs under the mean-field scaling and with a general class of activation functions, we prove that when the input dimen... | Published as a conference paper at ICLR 2022 ON FEATURE LEARNING IN NEURAL NETWORKS WITH GLOBAL CONVERGENCE GUARANTEES |
d212956516 | Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of symbolic regression. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. We propose ... | Published as a conference paper at ICLR 2021 DEEP SYMBOLIC REGRESSION: RECOVERING MATHEMATICAL EXPRESSIONS FROM DATA VIA RISK-SEEKING POLICY GRADIENTS |
d246679938 | Gradient-based learning algorithms have an implicit simplicity bias which in effect can limit the diversity of predictors being sampled by the learning procedure. This behavior can hinder the transferability of trained models by (i) favoring the learning of simpler but spurious features -present in the training data bu... | Work under review AGREE TO DISAGREE: DIVERSITY THROUGH DIS- AGREEMENT FOR BETTER TRANSFERABILITY |
d5519195 | We introduce segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments. Representations of the input segments (i.e., contiguous subsequences of the input) are computed by encoding their constituent to... | SEGMENTAL RECURRENT NEURAL NETWORKS |
d231861820 | Searching for network width is an effective way to slim deep neural networks with hardware budgets. With this aim, a one-shot supernet is usually leveraged as a performance evaluator to rank the performance w.r.t. different width. Nevertheless, current methods mainly follow a manually fixed weight sharing pattern, whic... | Published as a conference paper at ICLR 2021 LOCALLY FREE WEIGHT SHARING FOR NETWORK WIDTH SEARCH |
d210064528 | The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent, from household robotic vacuums to autonomous vehicles. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry, but fail to model ... | Published as a conference paper at ICLR 2020 LEARNING TO MOVE WITH AFFORDANCE MAPS |
d247450510 | Vision Transformer (ViT) attains state-of-the-art performance in visual recognition, and the variant, Local Vision Transformer, makes further improvements. The major component in Local Vision Transformer, local attention, performs the attention separately over small local windows. We rephrase local attention as a chann... | Published as a conference paper at ICLR 2022 ON THE CONNECTION BETWEEN LOCAL ATTENTION AND DYNAMIC DEPTH-WISE CONVOLUTION |
d225103018 | REPRESENTATION LEARNING FOR IMPROVED INTER- PRETABILITY AND CLASSIFICATION ACCURACY OF CLINICAL FACTORS FROM EEG X, the Moonshot Factory Mountain View, CA | |
d3728944 | We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. MAC moves away from monolithic black-box neural architectures towards a design that encourages both transparency and versatility. The model approaches problems by decomposing t... | Published as a conference paper at ICLR 2018 COMPOSITIONAL ATTENTION NETWORKS FOR MACHINE REASONING |
d14461677 | While the majority of today's object class models provide only 2D bounding boxes, far richer output hypotheses are desirable including viewpoint, fine-grained category, and 3D geometry estimate. However, models trained to provide richer output require larger amounts of training data, preferably well covering the releva... | Multi-View Priors for Learning Detectors From Sparse Viewpoint Data |
d256105021 | Neural networks that are equivariant to rotations, translations, reflections, and permutations on n-dimensional geometric space have shown promise in physical modeling-from modeling potential energy surfaces to forecasting the time evolution of dynamical systems. Current state-of-the-art methods employ spherical harmon... | SPATIAL ATTENTION KINETIC NETWORK WITH E(N)-EQUIVARIANCE |
d245634541 | We study the problem of learning a good set of policies, so that when combined together, they can solve a wide variety of unseen reinforcement learning tasks with no or very little new data. Specifically, we consider the framework of generalized policy evaluation and improvement, in which the rewards for all tasks of i... | Published as a conference paper at ICLR 2022 CONSTRUCTING A GOOD BEHAVIOR BASIS FOR TRANSFER USING GENERALIZED POLICY UPDATES |
d215828121 | We present a different view on stochastic optimization, which goes back to the splitting schemes for approximate solutions of ODE. In this work, we provide a connection between stochastic gradient descent approach and first-order splitting scheme for ODE. We consider the special case of splitting, which is inspired by ... | Published as a workshop paper at ICLR 2020 DeepDiffEq STOCHASTIC GRADIENT ALGORITHMS FROM ODE SPLITTING PERSPECTIVE |
d21671720 | Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program synthesis, many of which adopt a sequence generation paradigm similar to neural machine translation, in which sequence-to-sequence models are ... | Published as a conference paper at ICLR 2018 LEVERAGING GRAMMAR AND REINFORCEMENT LEARNING FOR NEURAL PROGRAM SYNTHESIS |
d238408395 | Unsupervised detection of anomaly points in time series is a challenging problem, which requires the model to derive a distinguishable criterion. Previous methods tackle the problem mainly through learning pointwise representation or pairwise association, however, neither is sufficient to reason about the intricate dyn... | Published as a conference paper at ICLR 2022 ANOMALY TRANSFORMER: TIME SERIES ANOMALY DETECTION WITH ASSOCIATION DISCREPANCY |
d247518586 | Due to the remarkable progress of deep generative models, animating images has become increasingly efficient, whereas associated results have become increasingly realistic. Current animation-approaches commonly exploit structure representation extracted from driving videos. Such structure representation is instrumental... | Published as a conference paper at ICLR 2022 LATENT IMAGE ANIMATOR: LEARNING TO ANIMATE IMAGES VIA LATENT SPACE NAVIGATION |
d224703455 | We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience. This approach can be applied on top of any learned representation and has the potential to easily support multiple solution objectives as well as zero-shot adjust... | DEEPAVERAGERS: OFFLINE REINFORCEMENT LEARN- ING BY SOLVING DERIVED NON-PARAMETRIC MDPS |
d8125776 | Distillation (Hinton et al., 2015)and privileged information(Vapnik & Izmailov, 2015)are two techniques that enable machines to learn from other machines. This paper unifies the two into generalized distillation, a framework to learn from multiple machines and data representations. We provide theoretical and causal ins... | Published as a conference paper at ICLR 2016 UNIFYING DISTILLATION AND PRIVILEGED INFORMATION |
d246241126 | Code completion is usually cast as a language modelling problem, i.e., continuing an input in a left-to-right fashion. However, in practice, some parts of the completion (e.g., string literals) may be very hard to predict, whereas subsequent parts directly follow from the context. To handle this, we instead consider th... | Published as a conference paper at ICLR 2022 LEARNING TO COMPLETE CODE WITH SKETCHES |
d257365623 | Recently, a variety of methods under the name of non-contrastive learning (like BYOL, SimSiam, SwAV, DINO) show that when equipped with some asymmetric architectural designs, aligning positive pairs alone is sufficient to attain good performance in self-supervised visual learning. Despite some understandings of some ... | Published as a conference paper at ICLR 2023 TOWARDS A UNIFIED THEORETICAL UNDERSTAND- ING OF NON-CONTRASTIVE LEARNING VIA RANK DIFFERENTIAL MECHANISM |
d249152035 | Deploying machine learning models on new tasks is a major challenge due to differences in distributions of the train (source) data and the new (target) data. However, the training data likely captures some of the properties of the new task. We consider the problem of reweighing the training samples to gain insights int... | Published as a conference paper at ICLR 2023 UNDERSTANDING NEW TASKS THROUGH THE LENS OF TRAINING DATA VIA EXPONENTIAL TILTING |
d182953134 | We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both pred... | Published as a conference paper at ICLR 2020 DEEP BATCH ACTIVE LEARNING BY DIVERSE, UNCERTAIN GRADIENT LOWER BOUNDS |
d3707778 | In industrial machine learning pipelines, data often arrive in parts. Particularly in the case of deep neural networks, it may be too expensive to train the model from scratch each time, so one would rather use a previously learned model and the new data to improve performance. However, deep neural networks are prone t... | BAYESIAN INCREMENTAL LEARNING FOR DEEP NEURAL NETWORKS |
d222133066 | The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced whe... | Published as a conference paper at ICLR 2021 REMEMBERING FOR THE RIGHT REASONS: EXPLANATIONS REDUCE CATASTROPHIC FORGETTING |
d16672277 | Conditional belief networks introduce stochastic binary variables in neural networks. Contrary to a classical neural network, a belief network can predict more than the expected value of the output Y given the input X. It can predict a distribution of outputs Y which is useful when an input can admit multiple outputs w... | Published as a conference paper at ICLR 2016 PREDICTING DISTRIBUTIONS WITH LINEARIZING BELIEF NETWORKS |
d208202182 | In this paper, we present a method for learning discrete linguistic units by incorporating vector quantization layers into neural models of visually grounded speech. We show that our method is capable of capturing both word-level and sub-word units, depending on how it is configured. What differentiates this paper from... | Published as a conference paper at ICLR 2020 LEARNING HIERARCHICAL DISCRETE LINGUISTIC UNITS FROM VISUALLY-GROUNDED SPEECH |
d257637187 | Contrastive vision-language models (e.g. CLIP) are typically created by updating all the parameters of a vision model and language model through contrastive training. Can such models be created by a small number of parameter updates to an already-trained language model and vision model? The literature describes techniq... | CONTRASTIVE ALIGNMENT OF VISION TO LANGUAGE THROUGH PARAMETER-EFFICIENT TRANSFER LEARN- ING |
d258352309 | Machine learning systems, especially with overparameterized deep neural networks, can generalize to novel test instances drawn from the same distribution as the training data. However, they fare poorly when evaluated on out-of-support test points. In this work, we tackle the problem of developing machine learning syste... | Published as a conference paper at ICLR 2023 LEARNING TO EXTRAPOLATE: A TRANSDUCTIVE AP- PROACH |
d17910711 | The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels. We show th... | IMPROVING ZERO-SHOT LEARNING BY MITIGATING THE HUBNESS PROBLEM |
d232258048 | Learning task-specific representations of persistence diagrams is an important problem in topological data analysis and machine learning. However, current methods are restricted in terms of their expressivity as they are focused on Euclidean representations. Persistence diagrams often contain features of infinite persi... | LEARNING HYPERBOLIC REPRESENTATIONS OF TOPO- LOGICAL FEATURES |
d15763546 | Generative adversarial networks (GANs) transform latent vectors into visually plausible images. It is generally thought that the original GAN formulation gives no out-of-the-box method to reverse the mapping, projecting images back into latent space. We introduce a simple, gradient-based technique called stochastic cli... | Workshop track -ICLR 2017 PRECISE RECOVERY OF LATENT VECTORS FROM GENERATIVE ADVERSARIAL NETWORKS |
d3178759 | One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the usefulness of a set of proxy tasks that evaluate reading comprehension via question a... | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks |
d239768373 | Existing research on learning with noisy labels mainly focuses on synthetic label noise. The synthetic noise, though has clean structures which greatly enabled statistical analyses, often fails to model the real-world noise patterns. The recent literature has observed several efforts to offer real-world noisy datasets,... | Published as a conference paper at ICLR 2022 LEARNING WITH NOISY LABELS REVISITED: A STUDY USING REAL-WORLD HUMAN ANNOTATIONS |
d231861566 | Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations, we explore negative data augmentation strategies (NDA) that intentionally create out-of-distribution samples. We show that such negative out... | Published as a conference paper at ICLR 2021 NEGATIVE DATA AUGMENTATION |
d244954585 | Many deep neural network architectures loosely based on brain networks have recently been shown to replicate neural firing patterns observed in the brain.One of the most exciting and promising novel architectures, the Transformer neural network, was developed without the brain in mind.In this work, we show that transfo... | REPRESENTATIONS OF THE HIPPOCAMPAL FORMATION |
d227054213 | The attention mechanism has been widely used in deep neural networks as a model component. By now, it has become a critical building block in many state-of-the-art natural language models. Despite its great success established empirically, the working mechanism of attention has not been investigated at a sufficient the... | Under review ON THE DYNAMICS OF TRAINING ATTENTION MODELS |
d57761103 | The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the ... | SELF-MONITORING NAVIGATION AGENT VIA AUXIL- IARY PROGRESS ESTIMATION |
d108297495 | Deep networks realize complex mappings that are often understood by their locally linear behavior at or around points of interest. For example, we use the derivative of the mapping with respect to its inputs for sensitivity analysis, or to explain (obtain coordinate relevance for) a prediction. One key challenge is tha... | TOWARDS ROBUST, LOCALLY LINEAR DEEP NET- WORKS |
d247187735 | Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task based on sets of nodes such as link/motif prediction and so on. Many works have recently proposed to address this problem by using random node features or node distance features. Ho... | Published as a conference paper at ICLR 2022 EQUIVARIANT AND STABLE POSITIONAL ENCODING FOR MORE POWERFUL GRAPH NEURAL NETWORKS |
d246015626 | Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time. Consequently, their use during online inference on time-series data entails considerable redundancy due to the overlap in successive token sequences. In this work, we propose novel formulatio... | Published as a conference paper at ICLR 2023 CONTINUAL TRANSFORMERS: REDUNDANCY-FREE ATTENTION FOR ONLINE INFERENCE |
d231786396 | Despite their success in massive engineering applications, deep neural networks are vulnerable to various perturbations due to their black-box nature. Recent study has shown that a deep neural network can misclassify the data even if the input data is perturbed by an imperceptible amount. In this paper, we address the ... | Published as a conference paper at ICLR 2021 TOWARDS ROBUST NEURAL NETWORKS VIA CLOSE- LOOP CONTROL |
d235613468 | Understanding the algorithmic regularization effect of stochastic gradient descent (SGD) is one of the key challenges in modern machine learning and deep learning theory. Most of the existing works, however, focus on very small or even infinitesimal learning rate regime, and fail to cover practical scenarios where the ... | Under review DIRECTION MATTERS: ON THE IMPLICIT REGU- LARIZATION EFFECT OF STOCHASTIC GRADIENT DE- SCENT WITH MODERATE LEARNING RATE |
d2449128 | Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data. Recent work on advancing the state of the art has been focused on the optimization or modelling of RNNs, mostly motivated by adressing the problems of the vanishing and exploding gradients. The control of overfitting has seen consid... | On Fast Dropout and its Applicability to Recurrent Networks |
d3523429 | Natural language processing (NLP) models often require a massive number of parameters for word embeddings, resulting in a large storage or memory footprint. Deploying neural NLP models to mobile devices requires compressing the word embeddings without any significant sacrifices in performance. For this purpose, we prop... | Under review as a conference paper at ICLR 2018 COMPRESSING WORD EMBEDDINGS VIA DEEP COMPOSITIONAL CODE LEARNING |
d252715485 | Few-shot prompting is a surprisingly powerful way to use Large Language Models (LLMs) to solve various tasks. However, this approach struggles as the task complexity increases or when the individual reasoning steps of the task themselves are hard to learn, especially when embedded in more complex tasks. To address this... | Decomposed Prompting : A MODULAR APPROACH FOR SOLVING COMPLEX TASKS |
d246904892 | One of the most important properties of neural networks is the clustering of local minima of the loss function near the global minimum, enabling efficient training. Though generative models implemented on quantum computers are known to be more expressive than their traditional counterparts, it has empirically been obse... | CRITICAL POINTS IN QUANTUM GENERATIVE MOD- ELS |
d235613631 | Much recent effort has been invested in non-autoregressive neural machine translation, which appears to be an efficient alternative to state-of-the-art autoregressive machine translation on modern GPUs. In contrast to the latter, where generation is sequential, the former allows generation to be parallelized across tar... | Published as a conference paper at ICLR 2021 DEEP ENCODER, SHALLOW DECODER: REEVALUATING NON-AUTOREGRESSIVE MACHINE TRANSLATION |
d2723173 | While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate th... | FITNETS: HINTS FOR THIN DEEP NETS |
d204743943 | Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing nearoptimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience r... | SINGLE EPISODE POLICY TRANSFER IN REINFORCE- MENT LEARNING |
d252762087 | Training dynamic models, such as neural ODEs, on long trajectories is a hard problem that requires using various tricks, such as trajectory splitting, to make model training work in practice. These methods are often heuristics with poor theoretical justifications, and require iterative manual tuning. We propose a princ... | Published as a conference paper at ICLR 2023 LATENT NEURAL ODES WITH SPARSE BAYESIAN MULTIPLE SHOOTING |
d234357520 | Recent self-supervised methods for image representation learning maximize the agreement between embedding vectors produced by encoders fed with different views of the same image. The main challenge is to prevent a collapse in which the encoders produce constant or non-informative vectors. We introduce VICReg (Variance-... | VICREG: VARIANCE-INVARIANCE-COVARIANCE RE- GULARIZATION FOR SELF-SUPERVISED LEARNING |
d233481638 | Factorized layers-operations parameterized by products of two or more matrices-occur in a variety of deep learning contexts, including compressed model training, certain types of knowledge distillation, and multi-head selfattention architectures. We study how to initialize and regularize deep nets containing such layer... | Published as a conference paper at ICLR 2021 INITIALIZATION AND REGULARIZATION OF FACTORIZED NEURAL LAYERS |
d247958146 | We propose a method for 3D shape reconstruction from unoriented point clouds. Our method consists of a novel SE(3)-equivariant coordinate-based network (TF-ONet), that parametrizes the occupancy field of the shape and respects the inherent symmetries of the problem. In contrast to previous shape reconstruction methods ... | SE(3)-EQUIVARIANT ATTENTION NETWORKS FOR SHAPE RECONSTRUCTION IN FUNCTION SPACE |
d195886569 | Point clouds, as a form of Lagrangian representation, allow for powerful and flexible applications in a large number of computational disciplines. We propose a novel deep-learning method to learn stable and temporally coherent feature spaces for points clouds that change over time. We identify a set of inherent problem... | Published as a conference paper at ICLR 2020 TRANQUIL CLOUDS: NEURAL NETWORKS FOR LEARNING TEMPORALLY COHERENT FEATURES IN POINT CLOUDS |
d252735252 | We study first-order methods for constrained min-max optimization. Existing methods either require two gradient calls or two projections in each iteration, which may be costly in some applications. In this paper, we first show that a variant of the Optimistic Gradient (OG) method, a singlecall single-projection algorit... | ACCELERATED SINGLE-CALL METHODS FOR CON- STRAINED MIN-MAX OPTIMIZATION |
d238857061 | We consider the problem of finding the best memoryless stochastic policy for an infinite-horizon partially observable Markov decision process (POMDP) with finite state and action spaces with respect to either the discounted or mean reward criterion. We show that the (discounted) state-action frequencies and the expecte... | THE GEOMETRY OF MEMORYLESS STOCHASTIC POL- ICY OPTIMIZATION IN INFINITE-HORIZON POMDPS |
d199442113 | Capsule networks are constrained by their, relative, inability to deeper in a parameter-inexpensive manner, and also by the general lack of equivariance guarantees. As a step towards bridging these two gaps, we present a new variation of capsule networks termed Space-of-Variation networks (SOVNET). Each layer in SOVNET... | BUILDING DEEP, EQUIVARIANT CAPSULE NETWORKS A PREPRINT |
d225066797 | Deep generative models provide a powerful set of tools to understand real-world data. But as these models improve, they increase in size and complexity, so their computational cost in memory and execution time grows. Using binary weights in neural networks is one method which has shown promise in reducing this cost. Ho... | Published as a conference paper at ICLR 2021 REDUCING THE COMPUTATIONAL COST OF DEEP GENERATIVE MODELS WITH BINARY NEURAL NETWORKS |
d5180869 | Machine learning methods in general and Deep Neural Networks in particular have shown to be vulnerable to adversarial perturbations. So far this phenomenon has mainly been studied in the context of whole-image classification. In this contribution, we analyse how adversarial perturbations can affect the task of semantic... | Workshop track -ICLR 2017 ADVERSARIAL EXAMPLES FOR SEMANTIC IMAGE SEGMENTATION |
d3366315 | One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator. Our new normalization technique is computationally light and easy to in... | Published as a conference paper at ICLR 2018 SPECTRAL NORMALIZATION FOR GENERATIVE ADVERSARIAL NETWORKS |
d1740355 | At present, designing convolutional neural network (CNN) architectures requires both human expertise and labor. New architectures are handcrafted by careful experimentation or modified from a handful of existing networks. We propose a meta-modeling approach based on reinforcement learning to automatically generate high... | Under review as a conference paper at ICLR 2017 DESIGNING NEURAL NETWORK ARCHITECTURES USING REINFORCEMENT LEARNING |
d257219541 | With the advantages of fast inference and human-friendly flexible manipulation, image-agnostic style manipulation via text guidance enables new applications that were not previously available.The state-of-the-art text-guided image-agnostic manipulation method embeds the representation of each channel of StyleGAN indepe... | LEARNING INPUT-AGNOSTIC MANIPULATION DIREC-TIONS IN STYLEGAN WITH TEXT GUIDANCE |
d258426737 | We present a comparative study on how and why contrastive learning (CL) and masked image modeling (MIM) differ in their representations and in their performance of downstream tasks. In particular, we demonstrate that self-supervised Vision Transformers (ViTs) have the following properties: (1) CL trains selfattentions ... | Published as a conference paper at ICLR 2023 WHAT DO SELF-SUPERVISED VISION TRANSFORMERS LEARN? |
d259243844 | We propose ADCLR: Accurate and Dense Contrastive Representation Learning, a novel self-supervised learning framework for learning accurate and dense vision representation. To extract spatial-sensitive information, ADCLR introduces query patches for contrasting in addition with global contrasting. Compared with previous... | PATCH-LEVEL CONTRASTING WITHOUT PATCH COR- RESPONDENCE FOR ACCURATE AND DENSE CON- TRASTIVE REPRESENTATION LEARNING |
d247011082 | Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps. We propose a faster and cheaper appro... | Published as a conference paper at ICLR 2023 TRUNCATED DIFFUSION PROBABILISTIC MODELS AND DIFFUSION-BASED ADVERSARIAL AUTO-ENCODERS |
d238583011 | Stochastic differential equations provide a rich class of flexible generative models, capable of describing a wide range of spatio-temporal processes. A host of recent work looks to learn data-representing SDEs, using neural networks and other flexible function approximators. Despite these advances, learning remains co... | Under review as a conference paper at ICLR 2022 ROBUST AND SCALABLE SDE LEARNING: A FUNCTIONAL PERSPECTIVE |
d49214673 | Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their application... | HIERARCHICAL INTERPRETATIONS FOR NEURAL NET- WORK PREDICTIONS |
d16326763 | We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algo... | CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING |
d231855409 | Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models. For object detection, the well-established classification and regression loss functions have been carefully designed by considering diverse learning challenges (e.g. class imbalance... | Published as a conference paper at ICLR 2021 LOSS FUNCTION DISCOVERY FOR OBJECT DE- TECTION VIA CONVERGENCE-SIMULATION DRIVEN SEARCH |
d235623747 | The information bottleneck (IB) principle has been suggested as a way to analyze deep neural networks. The learning dynamics are studied by inspecting the mutual information (MI) between the hidden layers and the input and output. Notably, separate fitting and compression phases during training have been reported. This... | Published as a conference paper at ICLR 2022 INFORMATION BOTTLENECK: EXACT ANALYSIS OF (QUANTIZED) NEURAL NETWORKS |
d244798691 | Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-stage stochastic optimization, widely used for modeling real-world process optimization tasks. Unfortunately, SDDP has a worst-case complexity that scales exponentially in the number of decision variables, which severely limits ap... | NEURAL STOCHASTIC DUAL DYNAMIC PROGRAMMING A PREPRINT |
d227745459 | The recent success of Transformers in the language domain has motivated adapting it to a multimodal setting, where a new visual model is trained in tandem with an already pretrained language model. However, due to the excessive memory requirements from Transformers, existing work typically fixes the language model and ... | PARAMETER EFFICIENT MULTIMODAL TRANSFORM- ERS FOR VIDEO REPRESENTATION LEARNING |
d85529011 | Visual representations are functions of visual data that are minimal sufficient statistics for a class of tasks and maximally invariant to nuisance variability. Minimal sufficiency guarantees that we can store the statistic in lieu of the raw data with no performance loss and smallest complexity. Maximal invariance gua... | VISUAL SCENE REPRESENTATIONS: SUFFICIENCY, MINIMALITY, INVARIANCE AND APPROXIMATIONS |
d3337127 | Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. We provide a simple yet rigorous explanation for this behaviour by introducing the concept of an optimal representation space, in which semantically close symbols are mapped to representations that... | Workshop track -ICLR 2018 DECODING DECODERS: FINDING OPTIMAL REPRESENTATION SPACES FOR UNSUPERVISED SIMILARITY TASKS |
d258236448 | We introduce a novel optimization algorithm for image recovery under learned sparse and low-rank constraints, which we parameterize as weighted extensions of the p p -vector and S p p Schatten-matrix quasi-norms for 0 < p ≤ 1, respectively. Our proposed algorithm generalizes the Iteratively Reweighted Least Squares (IR... | Published as a conference paper at ICLR 2023 LEARNING SPARSE AND LOW-RANK PRIORS FOR IMAGE RECOVERY VIA ITERATIVE REWEIGHTED LEAST SQUARES MINIMIZATION |
d4347685 | Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The recent pruning based work ESE (Han et al., 2017) suffers from degradation of performance/energy efficiency due to the irregular network structure after pru... | Workshop track -ICLR 2018 EFFICIENT RECURRENT NEURAL NETWORKS USING STRUCTURED MATRICES IN FPGAS |
d6961760 | Attention networks have proven to be an effective approach for embedding categorical inference within a deep neural network. However, for many tasks we may want to model richer structural dependencies without abandoning end-to-end training. In this work, we experiment with incorporating richer structural distributions,... | Published as a conference paper at ICLR 2017 STRUCTURED ATTENTION NETWORKS |
d248377377 | Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not utilize readily available prior beliefs the practitioner has on the potential location of the optimum. Thus... | πBO: AUGMENTING ACQUISITION FUNCTIONS WITH USER BELIEFS FOR BAYESIAN OPTIMIZATION |
d253018768 | Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DIFFEDIT, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to e... | DIFFEDIT: DIFFUSION-BASED SEMANTIC IMAGE EDIT- ING WITH MASK GUIDANCE |
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