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d235376822 | We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one. With the goal of generality, we introduce AdaMatch, a unified solution for unsupervised domain adaptation (UDA), semi-supervised learning ... | ADAMATCH: A UNIFIED APPROACH TO SEMI- SUPERVISED LEARNING AND DOMAIN ADAPTATION |
d248968007 | Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to operate safely.Small, far-away, or highly occluded objects are particularly challenging because there is limited information in the LiDAR point clouds for detecting them.To address this challenge, we leverage valuable info... | |
d257623048 | There has been growing interest in automatically predicting missing type annotations in programs written in Python and JavaScript. While prior methods have achieved impressive accuracy when predicting the most common types, they often perform poorly on rare or complex types. In this paper, we present a new type inferen... | Published as a conference paper at ICLR 2023 TYPET5: SEQ2SEQ TYPE INFERENCE USING STATIC ANALYSIS |
d257767364 | Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI. Existing work in Hyperparameter Optimization (HPO) trains surrogate models for approximating the response surface of hyperparameters as a regression task. In contrast, we hypothesize that the optimal ... | Published as a conference paper at ICLR 2023 DEEP RANKING ENSEMBLES FOR HYPERPARAMETER OPTIMIZATION |
d199551982 | Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive responses(Holtzman et al., 2019). While some post-hoc fixes have been proposed, in particular top-k and ... | NEURAL TEXT DEGENERATION WITH UNLIKELIHOOD TRAINING |
d238856973 | Recent work (e.g. LAMA (Petroni et al., 2019)) has found that the quality of the factual information extracted from Large Language Models (LLMs) depends on the prompts used to query them. This inconsistency is problematic because different users will query LLMs for the same information using different wording, but shou... | P-ADAPTERS: ROBUSTLY EXTRACTING FACTUAL IN- FORMATION FROM LANGUAGE MODELS WITH DI- VERSE PROMPTS |
d49310753 | We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relativeentropy objective. We show that several existing methods can directly be related to our derivation. We develop two off-policy algorithms and demonstrate that they are comp... | Published as a conference paper at ICLR 2018 MAXIMUM A POSTERIORI POLICY OPTIMISATION |
d15207478 | 5Non-negative matrix factorization (NMF) has become a popular machine 6 learning approach to many problems in text mining, speech and image 7 processing, bio-informatics and seismic data analysis to name a few. In 8 NMF, a matrix of non-negative data is approximated by the low-rank 9product of two matrices with non-neg... | The Diagonalized Newton Algorithm for Non- negative Matrix Factorization |
d256105559 | To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance. Our empirical study, however, suggests that mainstream uncertainty-based and diversitybased ... | Published as a conference paper at ICLR 2023 EXPLORING ACTIVE 3D OBJECT DETECTION FROM A GENERALIZATION PERSPECTIVE |
d3062491 | Convolutional autoregressive models have recently demonstrated state-of-the-art performance on a number of generation tasks. While fast, parallel training methods have been crucial for their success, generation is typically implemented in a naïve fashion where redundant computations are unnecessarily repeated. This res... | Workshop track -ICLR 2017 FAST GENERATION FOR CONVOLUTIONAL AUTOREGRESSIVE MODELS |
d256808230 | Neural image compression has surpassed state-of-the-art traditional codecs (H.266/VVC) for rate-distortion (RD) performance, but suffers from large complexity and separate models for different rate-distortion trade-offs. In this paper, we propose an Efficient single-model Variable-bit-rate Codec (EVC), which is able to... | EVC: TOWARDS REAL-TIME NEURAL IMAGE COM- PRESSION WITH MASK DECAY |
d224803224 | In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents. In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently. To succeed, the AI agent needs to i) understand the underlying goal of the task by watching a single demonstration o... | WATCH-AND-HELP: A CHALLENGE FOR SOCIAL PER- CEPTION AND HUMAN-AI COLLABORATION |
d256416448 | Monocular scene reconstruction from posed images is challenging due to the complexity of a large environment. Recent volumetric methods learn to directly predict the TSDF volume and have demonstrated promising results in this task. However, most methods focus on how to extract and fuse the 2D features to a 3D feature v... | Published as a conference paper at ICLR 2023 3D FORMER: MONOCULAR SCENE RECONSTRUCTION WITH 3D SDF TRANSFORMERS |
d248377370 | Reasoning about visual relationships is central to how humans interpret the visual world. This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) identifying object entities and their properties, 2) inferring semantic relations between pai... | Published as a conference paper at ICLR 2022 RELVIT: CONCEPT-GUIDED VISION TRANSFORMER FOR VISUAL RELATIONAL REASONING |
d211677318 | Unsupervised text encoding models have recently fueled substantial progress in Natural Language Processing (NLP). The key idea is to use neural networks to convert words in texts to vector space representations (embeddings) based on word positions in a sentence and their contexts, which are suitable for end-to-end trai... | MULTI-SCALE REPRESENTATION LEARNING FOR SPA- TIAL FEATURE DISTRIBUTIONS USING GRID CELLS |
d246822749 | Advanced representation learning techniques require reliable and general evaluation methods. Recently, several algorithms based on the common idea of geometric and topological analysis of a manifold approximated from the learned data representations have been proposed. In this work, we introduce Delaunay Component Anal... | Published as a conference paper at ICLR 2022 DELAUNAY COMPONENT ANALYSIS FOR EVALUATION OF DATA REPRESENTATIONS |
d256390161 | We present a subset selection algorithm designed to work with arbitrary model families in a practical batch setting. In such a setting, an algorithm can sample examples one at a time but, in order to limit overhead costs, is only able to update its state (i.e. further train model weights) once a large enough batch of e... | Published as a conference paper at ICLR 2023 LEVERAGING IMPORTANCE WEIGHTS IN SUBSET SE- LECTION |
d11322298 | Hyper-parameter selection remains a daunting task when building a pattern recognition architecture which performs well, particularly in recently constructed visual pipeline models for feature extraction. We re-formulate pooling in an existing pipeline as a function of adjustable pooling map weight parameters and propos... | Gradient Driven Learning for Pooling in Visual Pipeline Feature Extraction Models |
d222291168 | Massively multilingual models subsuming tens or even hundreds of languages pose great challenges to multi-task optimization. While it is a common practice to apply a language-agnostic procedure optimizing a joint multilingual task objective, how to properly characterize and take advantage of its underlying problem stru... | GRADIENT VACCINE: INVESTIGATING AND IMPROV- ING MULTI-TASK OPTIMIZATION IN MASSIVELY MUL- TILINGUAL MODELS |
d222125075 | Energy-based models (EBMs) have recently been successful in representing complex distributions of small images. However, sampling from them requires expensive Markov chain Monte Carlo (MCMC) iterations that mix slowly in high dimensional pixel space. Unlike EBMs, variational autoencoders (VAEs) generate samples quickly... | VAEBM: A SYMBIOSIS BETWEEN VARIATIONAL AU- TOENCODERS AND ENERGY-BASED MODELS |
d257254877 | Despite that going deep has proven successful in many neural architectures, the existing graph transformers are relatively shallow. In this work, we explore whether more layers are beneficial to graph transformers, and find that current graph transformers suffer from the bottleneck of improving performance by increasin... | Published as a conference paper at ICLR 2023 ARE MORE LAYERS BENEFICIAL TO GRAPH TRANS- FORMERS? |
d229371253 | We describe the convex semi-infinite dual of the two-layer vector-output ReLU neural network training problem. This semi-infinite dual admits a finite dimensional representation, but its support is over a convex set which is difficult to characterize. In particular, we demonstrate that the non-convex neural network tra... | VECTOR-OUTPUT RELU NEURAL NETWORK PROB- LEMS ARE COPOSITIVE PROGRAMS: CONVEX ANAL- YSIS OF TWO LAYER NETWORKS AND POLYNOMIAL- TIME ALGORITHMS |
d1923568 | Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance. In this work, we present a novel method called policy distillati... | POLICY DISTILLATION |
d254974335 | We propose a new neural network design paradigm Reversible Column Network (RevCol). The main body of RevCol is composed of multiple copies of subnetworks, named columns respectively, between which multi-level reversible connections are employed. Such architectural scheme attributes RevCol very different behavior from c... | Published as a conference paper at ICLR 2023 REVERSIBLE COLUMN NETWORKS MEGVII Technology 1 Beijing Academy of Artificial Intelligence |
d244908535 | This paper proposes an algorithm, RMDA, for training neural networks (NNs) with a regularization term for promoting desired structures. RMDA does not incur computation additional to proximal SGD with momentum, and achieves variance reduction without requiring the objective function to be of the finite-sum form. Through... | Training Structured Neural Networks Through Mani- fold Identification and Variance Reduction |
d232168371 | Deep object recognition models have been very successful over benchmark datasets such as ImageNet. How accurate and robust are they to distribution shifts arising from natural and synthetic variations in datasets? Prior research on this problem has primarily focused on ImageNet variations (e.g., ImageNetV2, ImageNet-A)... | Published as a conference paper at ICLR 2021 CONTEMPLATING REAL-WORLD OBJECT CLASSIFICATION |
d246240825 | Graph-based next-step prediction models have recently been very successful in modeling complex high-dimensional physical systems on irregular meshes. However, due to their short temporal attention span, these models suffer from error accumulation and drift. In this paper, we propose a new method that captures long-term... | Published as a conference paper at ICLR 2022 PREDICTING PHYSICS IN MESH-REDUCED SPACE WITH TEMPORAL ATTENTION |
d14228101 | This article exposes the failure of some big neural networks to leverage added capacity to reduce underfitting. Past research suggest diminishing returns when increasing the size of neural networks. Our experiments on ImageNet LSVRC-2010 show that this may be due to the fact there are highly diminishing returns for cap... | Big Neural Networks Waste Capacity |
d238744253 | Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic pathway generation. We report an amortized approach to generate synthetic pathways as a Markov decision process conditioned on a target molec... | Published as a conference paper at ICLR 2022 AMORTIZED TREE GENERATION FOR BOTTOM-UP SYNTHESIS PLANNING AND SYNTHESIZABLE MOLECULAR DESIGN |
d246823485 | Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing focus on curating benchmark datasets that capture distribution shifts. While valuable, the existing benchmarks are limited in that many of t... | METASHIFT: A DATASET OF DATASETS FOR EVALUAT- ING CONTEXTUAL DISTRIBUTION SHIFTS AND TRAIN- ING CONFLICTS |
d254069728 | Graph Neural Networks usually rely on the assumption that the graph topology is available to the network as well as optimal for the downstream task. Latent graph inference allows models to dynamically learn the intrinsic graph structure of problems where the connectivity patterns of data may not be directly accessible.... | Published as a conference paper at ICLR 2023 LATENT GRAPH INFERENCE USING PRODUCT MANIFOLDS |
d233444001 | In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this property may be problematic for value-based deep reinforcement learning agents, particularly if the agent must first succeed in relatively u... | Published as a conference paper at ICLR 2021 ADAPTING TO REWARD PROGRESSIVITY VIA SPECTRAL REINFORCEMENT LEARNING |
d14915449 | We introduce a new test of how well language models capture meaning in children's books. Unlike standard language modelling benchmarks, it distinguishes the task of predicting syntactic function words from that of predicting lowerfrequency words, which carry greater semantic content. We compare a range of state-of-the-... | THE GOLDILOCKS PRINCIPLE: READING CHILDREN'S BOOKS WITH EXPLICIT MEMORY REPRESENTATIONS |
d204206524 | Reinforcement learning requires manual specification of a reward function to learn a task. While in principle this reward function only needs to specify the task goal, in practice reinforcement learning can be very time-consuming or even infeasible unless the reward function is shaped so as to provide a smooth gradient... | Published as a conference paper at ICLR 2020 DYNAMICAL DISTANCE LEARNING FOR SEMI-SUPERVISED AND UNSUPERVISED SKILL DISCOVERY |
d253446984 | Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In th... | Published as a conference paper at ICLR 2023 UNBIASED SUPERVISED CONTRASTIVE LEARNING |
d256390383 | We introduce Progressive Prompts -a simple and efficient approach for continual learning in language models. Our method allows forward transfer and resists catastrophic forgetting, without relying on data replay or a large number of task-specific parameters. Progressive Prompts learns a new soft prompt for each task an... | Published as a conference paper at ICLR 2023 PROGRESSIVE PROMPTS: CONTINUAL LEARNING FOR LANGUAGE MODELS |
d259224350 | Most offline reinforcement learning (RL) algorithms return a target policy maximizing a trade-off between (1) the expected performance gain over the behavior policy that collected the dataset, and (2) the risk stemming from the out-of-distributionness of the induced state-action occupancy. It follows that the performan... | Published as a conference paper at ICLR 2023 HARNESSING MIXED OFFLINE REINFORCEMENT LEARNING DATASETS VIA TRAJECTORY WEIGHTING |
d46928091 | In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportuni... | GAMEPAD: A LEARNING ENVIRONMENT FOR THEO- REM PROVING |
d201486 | paper at ICLR 2015 EXAMPLE SELECTION FOR DICTIONARY LEARNING
29 Dec 20140B64DBCA30B4E43D251A3DD02A3D5414arXiv:1412.6177v2[cs.LG]
In unsupervised learning, an unbiased uniform sampling strategy is typically used, in order that the learned features faithfully encode the statistical structure of the training data.In this... | Under review as a conference paper at ICL |
d1163588 | We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and concentrate on aspects critical to leveraging the GPU's computational power. We introduce a ... | Published as a conference paper at ICLR 2017 REINFORCEMENT LEARNING THROUGH ASYN- CHRONOUS ADVANTAGE ACTOR-CRITIC ON A GPU |
d211020900 | How to generate descriptions from structured data organized in tables? Existing approaches using neural encoder-decoder models often suffer from lacking diversity. We claim that an open set of templates is crucial for enriching the phrase constructions and realizing varied generations. Learning such templates is prohib... | Published as a conference paper at ICLR 2020 VARIATIONAL TEMPLATE MACHINE FOR DATA-TO- TEXT GENERATION |
d257366019 | We develop an interior-point approach to solve constrained variational inequality (cVI) problems. Inspired by the efficacy of the alternating direction method of multipliers (ADMM) method in the single-objective context, we generalize ADMM to derive a first-order method for cVIs, that we refer to as ADMM-based interior... | Published as a conference paper at ICLR 2023 SOLVING CONSTRAINED VARIATIONAL INEQUALITIES VIA A FIRST-ORDER INTERIOR POINT-BASED METHOD |
d256615229 | Few-shot class-incremental learning (FSCIL) has been a challenging problem as only a few training samples are accessible for each novel class in the new sessions. Finetuning the backbone or adjusting the classifier prototypes trained in the prior sessions would inevitably cause a misalignment between the feature and cl... | Published as a conference paper at ICLR 2023 NEURAL COLLAPSE INSPIRED FEATURE-CLASSIFIER ALIGNMENT FOR FEW-SHOT CLASS INCREMENTAL LEARNING |
d202888885 | Frankle & Carbin, 2019)shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve a comparable accuracy to the latter in a similar number of iterations. However, the identification of these winning tickets still requires the cos... | Published as a conference paper at ICLR 2020 DRAWING EARLY-BIRD TICKETS: TOWARDS MORE EF- FICIENT TRAINING OF DEEP NETWORKS |
d231627759 | Temporal networks serve as abstractions of many real-world dynamic systems. | INDUCTIVE REPRESENTATION LEARNING IN TEMPO- RAL NETWORKS VIA CAUSAL ANONYMOUS WALKS |
d252683227 | We present Gradient Gating (G 2 ), a novel framework for improving the performance of Graph Neural Networks (GNNs). Our framework is based on gating the output of GNN layers with a mechanism for multi-rate flow of message passing information across nodes of the underlying graph. Local gradients are harnessed to further... | Published as a conference paper at ICLR 2023 GRADIENT GATING FOR DEEP MULTI-RATE LEARNING ON GRAPHS |
d247446712 | Human decision making is well known to be imperfect and the ability to analyse such processes individually is crucial when attempting to aid or improve a decisionmaker's ability to perform a task, e.g. to alert them to potential biases or oversights on their part. To do so, it is necessary to develop interpretable repr... | INVERSE ONLINE LEARNING: UNDERSTANDING NON- STATIONARY AND REACTIONARY POLICIES |
d257255242 | In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling, based on text region-level image masking. The proposed method randomly mas... | STRUCTEXTV2: MASKED VISUAL-TEXTUAL PREDIC- TION FOR DOCUMENT IMAGE PRE-TRAINING |
d16550910 | Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model w... | Matrix Approximation under Local Low-Rank Assumption |
d801438 | Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples. Such inputs are typically generated by adding small but purposeful modifications that lead to incorrect outputs while imperceptible to human eyes. The goal of this paper is not to introduce a single method, but to... | Workshop track -ICLR 2017 A THEORETICAL FRAMEWORK FOR ROBUSTNESS OF (DEEP) CLASSIFIERS AGAINST ADVERSARIAL EXAMPLES |
d251649222 | Reasoning is a fundamental problem for computers and deeply studied in Artificial Intelligence. In this paper, we specifically focus on answering multi-hop logical queries on Knowledge Graphs (KGs). This is a complicated task because, in real-world scenarios, the graphs tend to be large and incomplete. Most previous wo... | Published as a conference paper at ICLR 2022 NEURAL METHODS FOR LOGICAL REASONING OVER KNOWLEDGE GRAPHS |
d21727726 | Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be o... | Published as a conference paper at ICLR 2018 GENERATIVE NETWORKS AS INVERSE PROBLEMS WITH SCATTERING TRANSFORMS |
d237485233 | Meta-learning empowers artificial intelligence to increase its efficiency by learning how to learn. Unlocking this potential involves overcoming a challenging metaoptimisation problem. We propose an algorithm that tackles this problem by letting the meta-learner teach itself. The algorithm first bootstraps a target fro... | Published as a conference paper at ICLR 2022 BOOTSTRAPPED META-LEARNING |
d244463196 | How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for practical applications. Recent works have shown that using expressive policy function approximators and conditioning on future trajectory inf... | Published as a conference paper at ICLR 2022 GENERALIZED DECISION TRANSFORMER FOR OFFLINE HINDSIGHT INFORMATION MATCHING |
d51678464 | Many theories of deep learning have shown that a deep network can require dramatically fewer resources to represent a given function compared to a shallow network. But a question remains: can these efficient representations be learned using current deep learning techniques? In this work, we test whether standard deep l... | Workshop track -ICLR 2018 ARE EFFICIENT DEEP REPRESENTATIONS LEARNABLE? |
d203837683 | Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accur... | Published as a conference paper at ICLR 2020 GRAPHZOOM: A MULTI-LEVEL SPECTRAL APPROACH FOR ACCURATE AND SCALABLE GRAPH EMBEDDING |
d6628106 | We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions. The method is straightforward to implement and is based on adaptive estimates of lower-order moments of the gradients. The method is computationally efficient, has little memory requirements and is well suited... | ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION |
d235606453 | Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector. Such a shallow lookup results in a linear growth of memory consumption for storing the embedding matrix and incurs high computational costs when working with real-world KGs. Drawing parallels with sub... | NODEPIECE: COMPOSITIONAL AND PARAMETER- EFFICIENT REPRESENTATIONS OF LARGE KNOWL- EDGE GRAPHS |
d257102992 | Human similarity judgments are a powerful supervision signal for machine learning applications based on techniques such as contrastive learning, information retrieval, and model alignment, but classical methods for collecting human similarity judgments are too expensive to be used at scale. Recent methods propose using... | Published as a conference paper at ICLR 2023 WORDS ARE ALL YOU NEED? LANGUAGE AS AN AP- PROXIMATION FOR HUMAN SIMILARITY JUDGMENTS |
d245124014 | The quantitative analysis of non-invasive electrophysiology signals from electroencephalography (EEG) and magnetoencephalography (MEG) boils down to the identification of temporal patterns such as evoked responses, transient bursts of neural oscillations but also blinks or heartbeats for data cleaning. Several works ha... | DRIPP: DRIVEN POINT PROCESSES TO MODEL STIM- ULI INDUCED PATTERNS IN M/EEG SIGNALS |
d209439545 | Semmelhack et al. (2014)have achieved high classification accuracy in distinguishing swim bouts of zebrafish using a Support Vector Machine (SVM). Convolutional Neural Networks (CNNs) have reached superior performance in various image recognition tasks over SVMs, but their learnt features are not immediately visible. R... | ANALYSIS OF VIDEO FEATURE LEARNING IN TWO- STREAM CNNS ON THE EXAMPLE OF ZEBRAFISH SWIM BOUT CLASSIFICATION |
d235293778 | Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. In this paper, we propose a self-supervised framework for learning generalizable representations for non-stationary time series. Our approach, called Temporal Neighborhood Coding (TNC), takes advantage of the ... | Published as a conference paper at ICLR 2021 UNSUPERVISED REPRESENTATION LEARNING FOR TIME SERIES WITH TEMPORAL NEIGHBORHOOD CODING |
d56538189 | Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of informati... | Jitter-Adaptive Dictionary Learning -Application to Multi-Trial Neuroelectric Signals |
d210157251 | In multi-agent systems, complex interacting behaviors arise due to the high correlations among agents. However, previous work on modeling multi-agent interactions from demonstrations is primarily constrained by assuming the independence among policies and their reward structures. In this paper, we cast the multi-agent ... | MULTI-AGENT INTERACTIONS MODELING WITH COR- RELATED POLICIES |
d231951546 | Semi-supervised learning, i.e., training networks with both labeled and unlabeled data, has made significant progress recently. However, existing works have primarily focused on image classification tasks and neglected object detection which requires more annotation effort. In this work, we revisit the Semi-Supervised ... | Published as a conference paper at ICLR 2021 UNBIASED TEACHER FOR SEMI-SUPERVISED OBJECT DETECTION |
d7777777 | This paper introduces a new architecture for human pose estimation using a multilayer convolutional network architecture and a modified learning technique that learns low-level features and a higher-level weak spatial model.Unconstrained human pose estimation is one of the hardest problems in computer vision, and our n... | Learning Human Pose Estimation Features with Convolutional Networks |
d257687184 | Dropped into an unknown environment, what should an agent do to quickly learn about the environment and how to accomplish diverse tasks within it? We address this question within the goal-conditioned reinforcement learning paradigm, by identifying how the agent should set its goals at training time to maximize explorat... | Published as a conference paper at ICLR 2023 PLANNING GOALS FOR EXPLORATION |
d211171773 | Incorporating hierarchical structures like constituency trees has been shown to be effective for various natural language processing (NLP) tasks. However, it is evident that state-of-the-art (SOTA) sequence-based models like the Transformer struggle to encode such structures inherently. On the other hand, dedicated mod... | TREE-STRUCTURED ATTENTION WITH HIERARCHI- CAL ACCUMULATION |
d256826794 | Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but underperform in underrepresented population subgroups, especially when there are imbalanced group distributions in the long-tailed training data. Therefore, approaches that improve the accur... | PUSHING THE ACCURACY-GROUP ROBUSTNESS FRON- TIER WITH INTROSPECTIVE SELF-PLAY |
d196831582 | An open secret in contemporary machine learning is that many models work beautifully on standard benchmarks but fail to generalize outside the lab. This has been attributed to biased training data, which provide poor coverage over real world events. Generative models are no exception, but recent advances in generative ... | Published as a conference paper at ICLR 2020 ON THE "STEERABILITY" OF GENERATIVE ADVERSARIAL NETWORKS |
d16636683 | We propose a novel deep network structure called "Network In Network"(NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with ... | Network In Network |
d257280093 | Scaling transformers has led to significant breakthroughs in many domains, leading to a paradigm in which larger versions of existing models are trained and released on a periodic basis. New instances of such models are typically trained completely from scratch, despite the fact that they are often just scaled-up versi... | Published as a conference paper at ICLR 2023 LEARNING TO GROW PRETRAINED MODELS FOR EFFICIENT TRANSFORMER TRAINING |
d2187805 | Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way these models are formulated, trained,... | A NOTE ON THE EVALUATION OF GENERATIVE MODELS |
d252693237 | Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts (examples with intermediate reasoning steps). Existing benchmarks measure reasoning ability indirectly, by evaluating accuracy on downstream tasks such as mathematical reasoning. However, it is unclear how these mod... | LANGUAGE MODELS ARE GREEDY REASONERS: A SYSTEMATIC FORMAL ANALYSIS OF CHAIN-OF- THOUGHT |
d247958276 | We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both locally optimal and sufficiently different from existing ones. To encourage the learning policy to consistently converge towards a previously u... | Published as a conference paper at ICLR 2022 CONTINUOUSLY DISCOVERING NOVEL STRATEGIES VIA REWARD-SWITCHING POLICY OPTIMIZATION |
d229156351 | Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being expo... | CONTRASTIVE LEARNING WITH ADVERSARIAL PER- TURBATIONS FOR CONDITIONAL TEXT GENERATION |
d220703030 | As increasingly complex AI systems are introduced into our daily lives, it becomes important for such systems to be capable of explaining the rationale for their decisions and allowing users to contest these decisions. A significant hurdle to allowing for such explanatory dialogue could be the vocabulary mismatch betwe... | Published as a conference paper at ICLR 2022 BRIDGING THE GAP: PROVIDING POST-HOC SYMBOLIC EXPLANATIONS FOR SEQUENTIAL DECISION-MAKING PROBLEMS WITH INSCRUTABLE REPRESENTATIONS |
d159386152 | ments and practices of the old constitution and tradition?Because the source of Islamic Shariah, the foundation of the Aceh community life is articulated in the modern perspective of democratic and responsible state government.Basic aspirations of the specificity of Acehnese religious community life not only in the fie... | Model of Local Wisdom Legal Source and State Law in Aceh Government ZULFAN |
d7047554 | Scalability properties of deep neural networks raise key research questions, particularly as the problems considered become larger and more challenging. This paper expands on the idea of conditional computation introduced in [2], where the nodes of a deep network are augmented by a set of gating units that determine wh... | Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks |
d6587686 | People can learn a wide range of tasks from their own experience, but can also learn from observing other creatures. This can accelerate acquisition of new skills even when the observed agent differs substantially from the learning agent in terms of morphology. In this paper, we examine how reinforcement learning algor... | LEARNING INVARIANT FEATURE SPACES TO TRANS- FER SKILLS WITH REINFORCEMENT LEARNING |
d16561904 | Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of selfsupervised tasks that incorporate states, act... | Loss is its own Reward: Self-Supervision for Reinforcement Learning |
d256389841 | Modern studies in radiograph representation learning (R 2 L) rely on either selfsupervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self-and report-completion as two comple... | Published as a conference paper at ICLR 2023 ADVANCING RADIOGRAPH REPRESENTATION LEARN- ING WITH MASKED RECORD MODELING |
d57375723 | End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering... | COARSE-GRAIN FINE-GRAIN COATTENTION NET- WORK FOR MULTI-EVIDENCE QUESTION ANSWERING |
d14809601 | We propose a randomized relax-and-round inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field. We experiment on MAP inference tasks in several restricted Boltzmann machines. We also use our underlying sampler to estimate the log-partition function of restricted Boltzmann mach... | Relaxations for inference in restricted Boltzmann machines |
d257353585 | widely used in commercial acoustic systems in recent years.However, adversarial audio examples can cause abnormal behaviors for those acoustic systems, while being hard for humans to perceive.Various methods, such as transformation-based defenses and adversarial training, have been proposed to protect acoustic systems ... | DEFENDING AGAINST ADVERSARIAL AUDIO VIA DIFFUSION MODEL
2 Mar 2023
Shutong Wu
Arizona State Univer |
d8393918 | Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks(Scarselli et al., 2009), which we modify... | Published as a conference paper at ICLR 2016 GATED GRAPH SEQUENCE NEURAL NETWORKS |
d257353428 | Sparse Neural Networks (SNNs) have received voluminous attention predominantly due to growing computational and memory footprints of consistently exploding parameter count in large-scale models. Similar to their dense counterparts, recent SNNs generalize just as well and are equipped with numerous favorable benefits (e... | Published as a conference paper at ICLR 2023 SPARSITY MAY CRY: LET US FAIL (CURRENT) SPARSE NEURAL NETWORKS TOGETHER! |
d209478429 | Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. B... | Published as a conference paper at ICLR 2020 RESTRICTING THE FLOW: INFORMATION BOTTLE- NECKS FOR ATTRIBUTION |
d14249389 | For image recognition and labeling tasks, recent results suggest that machine learning methods that rely on manually specified feature representations may be outperformed by methods that automatically derive feature representations based on the data. Yet for problems that involve analysis of 3d objects, such as mesh se... | Learned versus Hand-Designed Feature Representations for 3d Agglomeration |
d209531600 | We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees-i.e., the confidence set for a given input contains the true label with high probability. We demonstrate how our approach can be used to constr... | PAC CONFIDENCE SETS FOR DEEP NEURAL NET- WORKS VIA CALIBRATED PREDICTION |
d235376961 | We present implicit displacement fields, a novel representation for detailed 3D geometry. Inspired by a classic surface deformation technique, displacement mapping, our method represents a complex surface as a smooth base surface plus a displacement along the base's normal directions, resulting in a frequency-based sha... | Published as a conference paper at ICLR 2022 GEOMETRY-CONSISTENT NEURAL SHAPE REPRESENTATION WITH IMPLICIT DISPLACEMENT FIELDS |
d257232602 | Semi-supervised learning aims to train a model using limited labels. State-of-theart semi-supervised methods for image classification such as PAWS rely on selfsupervised representations learned with large-scale unlabeled but curated data. However, PAWS is often less effective when using real-world unlabeled data that i... | Published as a conference paper at ICLR 2023 ROPAWS: ROBUST SEMI-SUPERVISED REPRESENTA- TION LEARNING FROM UNCURATED DATA |
d211117443 | Graph neural networks (GNNs) are a class of deep models that operate on data with arbitrary topology represented as graphs. We introduce an efficient memory layer for GNNs that can jointly learn node representations and coarsen the graph. We also introduce two new networks based on this layer: memory-based GNN (MemGNN)... | Published as a conference paper at ICLR 2020 MEMORY-BASED GRAPH NETWORKS |
d232135120 | With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and many variants have been proposed. Currently, almost all these models assume that the layer order is fixed and kept the same across data samp... | Published as a conference paper at ICLR 2021 IOT: INSTANCE-WISE LAYER REORDERING FOR TRANSFORMER STRUCTURES |
d258865243 | Understanding the interaction between multiple agents is crucial for realistic vehicle trajectory prediction. Existing methods have attempted to infer the interaction from the observed past trajectories of agents using pooling, attention, or graph-based methods, which rely on a deterministic approach. However, these me... | Published as a conference paper at ICLR 2023 LEVERAGING FUTURE RELATIONSHIP REASONING FOR VEHICLE TRAJECTORY PREDICTION |
d12122362 | Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thu... | Published as a conference paper at ICLR 2017 LEARNING TO REMEMBER RARE EVENTS |
d399358 | Representation learning is the dominant technique for unsupervised domain adaptation, but existing approaches have two major weaknesses. First, they often require the specification of "pivot features" that generalize across domains, which are selected by taskspecific heuristics. We show that a novel but simple feature ... | Unsupervised Multi-Domain Adaptation with Feature Embeddings |
d256627465 | Rapidly learning from ongoing experiences and remembering past events with a flexible memory system are two core capacities of biological intelligence. While the underlying neural mechanisms are not fully understood, various evidence supports that synaptic plasticity plays a critical role in memory formation and fast l... | Published as a conference paper at ICLR 2023 HEBBIAN AND GRADIENT-BASED PLASTICITY EN- ABLES ROBUST MEMORY AND RAPID LEARNING IN RNNS |
d257405409 | In many retrieval systems the original high dimensional data (e.g., images) is mapped to a lower dimensional feature through a learned embedding model. The task of retrieving the most similar data from a gallery set to a given query data is performed through a similarity comparison on features. When the embedding model... | Published as a conference paper at ICLR 2023 FASTFILL: EFFICIENT COMPATIBLE MODEL UPDATE |
d220935959 | We explore value-based multi-agent reinforcement learning (MARL) in the popular paradigm of centralized training with decentralized execution (CTDE). CTDE has an important concept, Individual-Global-Max (IGM) principle, which requires the consistency between joint and local action selections to support efficient local ... | Published as a conference paper at ICLR 2021 QPLEX: DUPLEX DUELING MULTI-AGENT Q-LEARNING |
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