conference stringclasses 3
values | year int32 2.02k 2.02k | paper_id int32 5.89k 80k | title stringlengths 12 188 | abstract stringlengths 1 4.65k | topics listlengths 1 20 | image_url stringlengths 54 89 |
|---|---|---|---|---|---|---|
NeurIPS | 2,023 | 70,456 | Softmax Output Approximation for Activation Memory-Efficient Training of Attention-based Networks | In this paper, we propose to approximate the softmax output, which is the key product of the attention mechanism, to reduce its activation memory usage when training attention-based networks (aka Transformers). During the forward pass of the network, the proposed softmax output approximation method stores only a small ... | [
"Neural Networks",
"Natural Language Processing",
"Model Optimization",
"Memory-Efficient Computing"
] | |
NeurIPS | 2,022 | 53,156 | On the Stability and Scalability of Node Perturbation Learning | To survive, animals must adapt synaptic weights based on external stimuli and rewards. And they must do so using local, biologically plausible, learning rules -- a highly nontrivial constraint. One possible approach is to perturb neural activity (or use intrinsic, ongoing noise to perturb it), determine whether perform... | [
"Computational Neuroscience",
"Neural Networks",
"Biological Learning Models",
"Synaptic Plasticity"
] | |
ICML | 2,022 | 16,835 | EqR: Equivariant Representations for Data-Efficient Reinforcement Learning | We study a variety of notions of equivariance as an inductive bias in Reinforcement Learning (RL). In particular, we propose new mechanisms for learning representations that are equivariant to both the agent’s action, as well as symmetry transformations of the state-action pairs. Whereas prior work on exploiting symmet... | [
"Reinforcement Learning",
"Representation Learning",
"Symmetry and Group Theory in Machine Learning"
] | |
NeurIPS | 2,022 | 64,155 | Rethinking Learning Dynamics in RL using Adversarial Networks | Recent years have seen tremendous progress in methods of reinforcement learning. However, most of these approaches have been trained in a straightforward fashion and are generally not robust to adversity, especially in the meta-RL setting. To the best of our knowledge, our work is the first to propose an adversarial tr... | [
"Reinforcement Learning",
"Adversarial Machine Learning",
"Multi-Task Learning",
"Meta-Reinforcement Learning"
] | |
ICLR | 2,024 | 17,915 | Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation | We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit. This model is motivated by applications in online recommendation where the choice of recommended items depends on both the click-through rates and the post-click rewards. Like in classical bandits, rewards follow a... | [
"Mechanism Design",
"Online Algorithms",
"Game Theory",
"Multi-Armed Bandit Problems",
"Recommender Systems"
] | |
ICLR | 2,024 | 19,129 | Simplicial Representation Learning with Neural $k$-Forms | Geometric deep learning extends deep learning to incorporate information about the geometry and topology data, especially in complex domains like graphs. Despite the popularity of message passing in this field, it has limitations such as the need for graph rewiring, ambiguity in interpreting data, and over-smoothing. I... | [
"Geometric Deep Learning",
"Topological Data Analysis",
"Simplicial Complexes",
"Differential Geometry",
"Graph Neural Networks"
] | |
NeurIPS | 2,022 | 54,858 | NS3: Neuro-symbolic Semantic Code Search | Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to struggle with longer, compositional sentences, and multi-step... | [
"Natural Language Processing",
"Information Retrieval",
"Software Engineering",
"Semantic Code Search",
"Neuro-symbolic Computing"
] | |
NeurIPS | 2,022 | 55,254 | A Spectral Approach to Item Response Theory | The Rasch model is one of the most fundamental models in item response theory and has wide-ranging applications from education testing to recommendation systems. In a universe with $n$ users and $m$ items, the Rasch model assumes that the binary response $X_{li} \in \{0,1\}$ of a user $l$ with parameter $\theta^*_l$ to... | [
"Item Response Theory",
"Educational Testing",
"Recommendation Systems",
"Machine Learning Algorithms",
"Markov Chains",
"Statistical Modeling"
] | |
ICML | 2,023 | 24,962 | End-to-End Full-Atom Antibody Design | Antibody design is an essential yet challenging task in various domains like therapeutics and biology. There are two major defects in current learning-based methods: 1) tackling only a certain subtask of the whole antibody design pipeline, making them suboptimal or resource-intensive. 2) omitting either the framework r... | [
"Computational Biology",
"Bioinformatics",
"Structural Biology",
"Protein Engineering",
"Machine Learning in Biology"
] | |
NeurIPS | 2,023 | 79,168 | Syllabus: Curriculum Learning Made Easy | Curriculum learning has been a quiet yet crucial component of many of the high-profile successes of reinforcement learning. Despite this, none of the major reinforcement learning libraries support curriculum learning or include curriculum learning algorithms. Curriculum learning methods can provide general and compleme... | [
"Reinforcement Learning",
"Curriculum Learning",
"Machine Learning Libraries",
"Artificial Intelligence Tools and Frameworks"
] | |
ICLR | 2,022 | 5,914 | Inductive Relation Prediction Using Analogy Subgraph Embeddings | Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i.e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training. Here, we propose ANalogy SubGraphEmbeddingLearning (Gr... | [
"Graph Neural Networks",
"Knowledge Graphs",
"Inductive Learning",
"Relation Prediction",
"Explainable AI"
] | |
ICML | 2,023 | 24,271 | How Bad is Top-$K$ Recommendation under Competing Content Creators? | This study explores the impact of content creators' competition on user welfare in recommendation platforms, as well as the long-term dynamics of relevance-driven recommendations. We establish a model of creator competition, under the setting where the platform uses a top-$K$ recommendation policy, user decisions are g... | [
"Recommender Systems",
"Game Theory",
"User Behavior Analysis",
"Economics of Information Systems"
] | |
NeurIPS | 2,023 | 71,311 | A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process | Learning rich skills under the option framework without supervision of external rewards is at the frontier of reinforcement learning research. Existing works mainly fall into two distinctive categories: variational option discovery that maximizes the diversity of the options through a mutual information loss (while ign... | [
"Reinforcement Learning",
"Unsupervised Learning",
"Skill Discovery",
"Determinantal Point Process",
"Machine Learning Algorithms"
] | |
NeurIPS | 2,023 | 70,045 | VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models | Diffusion Models (DMs) are state-of-the-art generative models that learn a reversible corruption process from iterative noise addition and denoising. They are the backbone of many generative AI applications, such as text-to-image conditional generation. However, recent studies have shown that basic unconditional DMs (e... | [
"Machine Learning Security",
"Generative Models",
"Adversarial Machine Learning",
"Backdoor Attacks",
"Diffusion Models"
] | |
NeurIPS | 2,022 | 60,359 | Robust Forecasting for Robotic Control: A Game-Theoretic Approach | Modern robots require accurate forecasts to make optimal decisions in the real world. For example, self-driving cars need an accurate forecast of other agents' future actions to plan safe trajectories. Current methods rely heavily on historical time series to accurately predict the future. However, relying entirely on ... | [
"Robotics",
"Control Systems",
"Game Theory",
"Forecasting and Prediction",
"Autonomous Vehicles"
] | |
ICML | 2,022 | 16,757 | Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum | Despite considerable advances in deep reinforcement learning, it has been shown to be highly vulnerable to adversarial perturbations to state observations. Recent efforts that have attempted to improve adversarial robustness of reinforcement learning can nevertheless tolerate only very small perturbations, and remain f... | [
"Deep Reinforcement Learning",
"Adversarial Machine Learning",
"Robustness in Machine Learning",
"Curriculum Learning"
] | |
NeurIPS | 2,023 | 70,375 | Learning from Active Human Involvement through Proxy Value Propagation | Learning from active human involvement enables the human subject to actively intervene and demonstrate to the AI agent during training. The interaction and corrective feedback from human brings safety and AI alignment to the learning process. In this work, we propose a new reward-free active human involvement method ca... | [
"Reinforcement Learning",
"Human-Computer Interaction",
"AI Safety and Alignment"
] | |
ICML | 2,023 | 25,267 | Towards Omni-generalizable Neural Methods for Vehicle Routing Problems | Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This pape... | [
"Operations Research",
"Meta-Learning",
"Combinatorial Optimization",
"Neural Networks",
"Vehicle Routing Problems "
] | |
ICLR | 2,024 | 18,783 | Fake It Till Make It: Federated Learning with Consensus-Oriented Generation | In federated learning (FL), data heterogeneity is one key bottleneck that causes model divergence and limits performance. Addressing this, existing methods often regard data heterogeneity as an inherent property and propose to mitigate its adverse effects by correcting models. In this paper, we seek to break this inher... | [
"Federated Learning",
"Data Generation",
"Model Training",
"Knowledge Distillation",
"Data Heterogeneity"
] | |
NeurIPS | 2,022 | 62,684 | Transformer Based Kenyan Election Misinformation and Hatespeech monitoring | Abstract revised as required, with additional extended abstract pdf added.Kenyan presidential elections are a tense and problematic time. There are documented cases of voter-directed social media manipulation campaigns and incidents of post-election violence during election season. We build and test a dashboard to moni... | [
"Natural Language Processing",
"Misinformation Detection",
"Hate Speech Detection",
"Social Media Analysis",
"Election Monitoring",
"Sentiment Analysis",
"Computational Social Science"
] | |
ICLR | 2,023 | 11,914 | AANG : Automating Auxiliary Learning | Auxiliary objectives, supplementary learning signals that are introduced to help aid learning on data-starved or highly complex end-tasks, are commonplace in machine learning. Whilst much work has been done to formulate useful auxiliary objectives, their construction is still an art which proceeds by slow and tedious h... | [
"Auxiliary Learning",
"Automated Learning",
"Natural Language Processing",
"Algorithm Design",
"Generalization in Machine Learning"
] | |
NeurIPS | 2,022 | 57,183 | Neural DAG Scheduling via One-Shot Priority Sampling | We consider the problem of scheduling operations/nodes, the dependency among which is characterized by a Directed Acyclic Graph (DAG). Due to its NP-hard nature, heuristic algorithms were traditionally used to acquire reasonably good solutions, and more recent works have proposed Machine Learning (ML) heuristics that c... | [
"Neural Networks",
"Scheduling Algorithms",
"Graph Theory",
"Computational Optimization"
] | |
ICLR | 2,024 | 22,162 | Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study | The foundation agent is one of the promising ways to achieve Artificial General Intelligence. Recent studies have demonstrated its success in specific tasks or scenarios. However, existing foundation agents cannot generalize across different scenarios, mainly due to their diverse observation and action spaces and seman... | [
"Human-Computer Interaction",
"Multimodal Systems",
"Game AI"
] | |
ICLR | 2,024 | 18,534 | 3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation | Prior methods that tackle the problem of generalizable object pose estimation highly rely on having dense views of the unseen object. By contrast, we address the scenario where only a single reference view of the object is available. Our goal then is to estimate the relative object pose between this reference view and ... | [
"Computer Vision",
"3D Object Recognition",
"Pose Estimation",
"Robotics"
] | |
ICLR | 2,024 | 18,112 | Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks | We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product network... | [
"Graph Representation Learning",
"Probabilistic Graph Models",
"Neural Networks",
"Sum-Product Networks",
"Graph Neural Networks"
] | |
NeurIPS | 2,022 | 55,320 | When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment | AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, interpret and predict human moral judgments and decisions. Human moral judgments are often guided by rules, but not always. A central challenge f... | [
"AI Safety",
"Moral Psychology",
"Cognitive Science",
"Natural Language Processing",
"Human-AI Interaction"
] | |
NeurIPS | 2,023 | 78,780 | Agile Modeling: From Concept to Classifier in Minutes | The application of computer vision methods to nuanced, subjective concepts is growing. While crowdsourcing has served the vision community well for most objective tasks (such as labeling a "zebra"), it now falters on tasks where there is substantial subjectivity in the concept (such as identifying "gourmet tuna"). Howe... | [
"Computer Vision",
"Human-Computer Interaction",
"User-Centered Design",
"Crowdsourcing",
"Image Classification"
] | |
NeurIPS | 2,022 | 55,230 | Inception Transformer | Recent studies show that transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose $\textit{Inception Transformer}$, or $\textit{iFormer}$ for short, th... | [
"Computer Vision",
"Deep Learning",
"Neural Networks",
"Image Processing"
] | |
ICLR | 2,024 | 19,470 | Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding | Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions an... | [
"Natural Language Processing",
"Table Understanding",
"Large Language Models ",
"Reasoning and Inference",
"Data Representation and Transformation"
] | |
ICML | 2,022 | 18,185 | Marginal Tail-Adaptive Normalizing Flows | Learning the tail behavior of a distribution is a notoriously difficult problem. By definition, the number of samples from the tail is small, and deep generative models, such as normalizing flows, tend to concentrate on learning the body of the distribution. In this paper, we focus on improving the ability of normalizi... | [
"Deep Learning",
"Generative Models",
"Statistical Modeling",
"Probability and Statistics",
"Climate Science"
] | |
NeurIPS | 2,022 | 56,948 | GAN-Flow: A dimension-reduced variational framework for physics-based inverse problems | We propose GAN-Flow -- a modular inference approach that combines generative adversarial network (GAN) prior with a normalizing flow (NF) model to solve inverse problems in the lower-dimensional latent space of the GAN prior using variational inference. GAN-Flow leverages the intrinsic dimension reduction and superior ... | [
"Inverse Problems",
"Computational Physics",
"Variational Inference",
"Generative Models"
] | |
ICML | 2,024 | 32,974 | Latent Space Symmetry Discovery | Equivariant neural networks require explicit knowledge of the symmetry group. Automatic symmetry discovery methods aim to relax this constraint and learn invariance and equivariance from data. However, existing symmetry discovery methods are limited to simple linear symmetries and cannot handle the complexity of real-w... | [
"Neural Networks",
"Symmetry Discovery",
"Generative Models",
"Computational Mathematics",
"Dynamical Systems"
] | |
ICLR | 2,024 | 19,374 | Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow | We investigate a challenging task of nighttime optical flow, which suffers from weakened texture and amplified noise. These degradations weaken discriminative visual features, thus causing invalid motion feature matching. Typically, existing methods employ domain adaptation to transfer knowledge from auxiliary domain t... | [
"Computer Vision",
"Optical Flow",
"Domain Adaptation",
"Image Processing"
] | |
ICLR | 2,024 | 17,522 | Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic | For object re-identification (re-ID), learning from synthetic data has become a promising strategy to cheaply acquire large-scale annotated datasets and effective models, with few privacy concerns. Many interesting research problems arise from this strategy, e.g., how to reduce the domain gap between synthetic source a... | [
"Computer Vision",
"Domain Adaptation",
"Synthetic Data",
"Object Re-Identification",
"Benchmarking",
"Data Annotation"
] | |
ICML | 2,024 | 34,429 | Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics | A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforcement learning have led to groundbreaking achievements to solve complex continuous control tasks. However, most approaches return only one so... | [
"Deep Reinforcement Learning",
"Quality-Diversity Optimization",
"Continuous Control",
"Robotics",
"Machine Learning Algorithms"
] | |
NeurIPS | 2,022 | 57,712 | Image Manipulation via Neuro-Symbolic Networks | We are interested in image manipulation via natural language text – a task that is extremely useful for multiple AI applications but requires complex reasoning over multi-modal spaces. Recent work on neuro-symbolic approaches has been quite effective in solving such tasks as they offer better modularity, interpretabili... | [
"Computer Vision",
"Natural Language Processing",
"Multi-Modal Learning",
"Neuro-Symbolic AI",
"Image Processing",
"Artificial Intelligence Applications"
] | |
NeurIPS | 2,022 | 60,828 | Moving Frame Net: SE(3)-Equivariant Network for Volumes | Equivariance of neural networks to transformations helps to improve their performance and reduce generalization error in computer visions tasks, as they apply to datasets presenting symmetries (e.g. scalings, rotations, translations). The method of moving frames is classical for deriving operators invariant to the acti... | [
"Computer Vision",
"Neural Networks",
"Equivariant Neural Networks",
"3D Image Processing",
"Medical Image Analysis"
] | |
NeurIPS | 2,022 | 53,246 | LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning | Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new samples. This setting is particularly well-suited for continuous control robotic applications for which online data collection based on trial-and-error is costly and... | [
"Offline Reinforcement Learning",
"Robotics",
"Continuous Control"
] | |
ICLR | 2,024 | 18,812 | Learning to Act from Actionless Videos through Dense Correspondences | In this work, we present an approach to construct a video-based robot policy capable of reliably executing diverse tasks across different robots and environments from few video demonstrations without using any action annotations. Our method leverages images as a task-agnostic representation, encoding both the state and... | [
"Robotics",
"Computer Vision",
"Video Analysis",
"Robot Learning",
"Imitation Learning"
] | |
ICLR | 2,022 | 6,060 | Learning Weakly-supervised Contrastive Representations | We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary information, we can hypothesize that an Instagram image will be semantically more similar with the same hashtags. With this intuition, we pre... | [
"Weakly-supervised Learning",
"Contrastive Learning",
"Representation Learning",
"Clustering",
"Unsupervised Learning"
] | |
NeurIPS | 2,022 | 57,467 | A Simple Phoneme-based Error Simulator for ASR Error Correction | Despite the recent advances brought by deep neural networks, the real-world applications of Automatic Speech Recognition (ASR) inevitably suffer from various errors mostly caused by incorrectly captured phonetic features. This is of particular consequence in our work which involves the transcription of real patient cli... | [
"Automatic Speech Recognition ",
"Error Correction",
"Phonetics",
"Deep Learning",
"Natural Language Processing",
"Clinical Transcription"
] | |
NeurIPS | 2,023 | 73,490 | Scalable 3D Captioning with Pretrained Models | We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects. This approach utilizes pretrained models from image captioning, image-text alignment, and LLM to consolidate captions from multiple views of a 3D asset, completely side-stepping the time-consuming and costly process of manual anno... | [
"Computer Vision",
"Natural Language Processing",
"3D Modeling",
"Data Annotation and Labeling"
] | |
ICML | 2,024 | 33,310 | Learning the Target Network in Function Space | We focus on the task of learning the value function in the reinforcement learning (RL) setting. This task is often solved by updating a pair of online and target networks while ensuring that the parameters of these two networks are equivalent. We propose Lookahead-Replicate (LR), a new value-function approximation algo... | [
"Reinforcement Learning",
"Value Function Approximation",
"Deep Learning",
"Machine Learning Algorithms"
] | |
ICML | 2,023 | 24,120 | Structure-informed Language Models Are Protein Designers | This paper demonstrates that language models are strong structure-based protein designers. We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs), that have learned massive sequential evolutionary knowledge from the universe of natural protein sequences, to acquire an im... | [
"Computational Biology",
"Protein Design",
"Machine Learning in Biology",
"Structural Bioinformatics",
"Bioinformatics and Computational Biology"
] | |
NeurIPS | 2,022 | 60,481 | Train Offline, Test Online: A Real Robot Learning Benchmark | Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotics data. We take on these challenges via a new benchmark: Train Offline, Test Online (TOTO). TOTO pro... | [
"Robotics",
"Robot Learning",
"Machine Learning Benchmarks",
"Offline Learning",
"Online Testing",
"Manipulation Tasks",
"Generalization in Robotics"
] | |
ICLR | 2,023 | 11,214 | Revisit Finetuning strategy for Few-Shot Learning to Transfer the Emdeddings | Few-Shot Learning (FSL) aims to learn a simple and effective bias on limited novel samples. Recently, many methods have been focused on re-training a randomly initialized linear classifier to adapt it to the novel features extracted by the pre-trained feature extractor (called Linear-Probing-based methods). These metho... | [
"Few-Shot Learning",
"Transfer Learning",
"Feature Extraction",
"Model Finetuning"
] | |
ICLR | 2,024 | 18,027 | Rethinking Branching on Exact Combinatorial Optimization Solver: The First Deep Symbolic Discovery Framework | Machine learning (ML) has been shown to successfully accelerate solving NP-hard combinatorial optimization (CO) problems under the branch and bound framework. However, the high training and inference cost and limited interpretability of ML approaches severely limit their wide application to modern exact CO solvers. In ... | [
"Combinatorial Optimization",
"Symbolic Discovery",
"Operations Research",
"Algorithm Design"
] | |
ICLR | 2,023 | 12,073 | Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning | Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks written in text form, such as math word problems (MWP... | [
"Natural Language Processing",
"Mathematical Reasoning",
"Data Science",
"Educational Technology"
] | |
ICLR | 2,022 | 6,011 | Measuring CLEVRness: Black-box Testing of Visual Reasoning Models | How can we measure the reasoning capabilities of intelligence systems? Visual question answering provides a convenient framework for testing the model's abilities by interrogating the model through questions about the scene. However, despite scores of various visual QA datasets and architectures, which sometimes yield ... | [
"Computer Vision",
"Visual Question Answering",
"Neural Networks",
"Adversarial Machine Learning",
"Cognitive Computing"
] | |
NeurIPS | 2,023 | 72,075 | Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization | Feature visualization has gained significant popularity as an explainability method, particularly after the influential work by Olah et al. in 2017. Despite its success, its widespread adoption has been limited due to issues in scaling to deeper neural networks and the reliance on tricks to generate interpretable image... | [
"Deep Learning",
"Explainable AI",
"Neural Networks",
"Computer Vision"
] | |
ICML | 2,024 | 34,943 | Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces | In many reinforcement learning (RL) applications, we want policies that reach desired states and then keep the controlled system within an acceptable region around the desired states over an indefinite period of time. This latter objective is calledstabilityand is especially important when the state space is unbounded,... | [
"Reinforcement Learning",
"Control Systems",
"Stability Analysis",
"Queueing Theory",
"Operations Research"
] | |
ICLR | 2,022 | 6,166 | Auto-Transfer: Learning to Route Transferable Representations | Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications. Existing approaches typically constrain the target deep neural network (DNN) feature representations to ... | [
"Transfer Learning",
"Deep Learning",
"Neural Networks",
"Computer Vision"
] | |
NeurIPS | 2,023 | 70,801 | Knowledge Distillation for High Dimensional Search Index | Lightweight compressed models are prevalent in Approximate Nearest Neighbor Search (ANNS) and Maximum Inner Product Search (MIPS) owing to their superiority of retrieval efficiency in large-scale datasets. However, results given by compressed methods are less accurate due to the curse of dimension and the limitations o... | [
"Information Retrieval",
"Knowledge Distillation",
"Approximate Nearest Neighbor Search ",
"Maximum Inner Product Search ",
"High Dimensional Data",
"Model Compression"
] | |
ICML | 2,024 | 37,078 | Diffusion Models with Group Equivariance | In recent years, diffusion models have risen to prominence as the foremost technique for distribution learning. This paper focuses on structure-preserving diffusion models (SPDM), a subset of diffusion processes tailored to distributions with inherent structures, such as group symmetries. We complement existing suffici... | [
"Generative Models",
"Equivariant Machine Learning",
"Geometric Deep Learning",
"Image Processing",
"Medical Imaging"
] | |
NeurIPS | 2,023 | 71,309 | $\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning | Distributed training of Deep Learning models has been critical to many recent successes in the field. Current standard methods primarily rely on synchronous centralized algorithms which induce major communication bottlenecks and synchronization locks at scale. Decentralized asynchronous algorithms are emerging as a pot... | [
"Distributed Deep Learning",
"Decentralized Algorithms",
"Asynchronous Communication",
"Optimization Algorithms",
"Parallel Computing"
] | |
NeurIPS | 2,022 | 53,426 | Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space | In this paper, we revisit the regret of undiscounted reinforcement learning in MDPs with a birth and death structure. Specifically, we consider a controlled queue with impatient jobs and the main objective is to optimize a trade-off between energy consumption and user-perceived performance. Within this setting, th... | [
"Reinforcement Learning",
"Markov Decision Processes ",
"Queueing Theory",
"Regret Analysis",
"Algorithmic Efficiency"
] | |
NeurIPS | 2,022 | 58,551 | Conditional Contrastive Networks | A vast amount of structured information associated with unstructured data, such as images or text, is stored online. This structured information implies different similarity relationships among unstructured data. Recently, contrastive learned embeddings trained on web-scraped unstructured data have been shown to have s... | [
"Computer Vision",
"Contrastive Learning",
"Representation Learning",
"Deep Learning"
] | |
ICLR | 2,024 | 17,702 | S$2$AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic | Learning expressive stochastic policies instead of deterministic ones has been proposed to achieve better stability, sample complexity and robustness. Notably, in Maximum Entropy reinforcement learning (MaxEnt RL), the policy is modeled as an expressive energy-based model (EBM) over the Q-values. However, this formulat... | [
"Reinforcement Learning",
"Energy-Based Models",
"Stochastic Policies",
"Variational Inference"
] | |
ICML | 2,024 | 34,669 | Interpreting and Improving Large Language Models in Arithmetic Calculation | Large language models (LLMs) have demonstrated remarkable potential across numerous applications and have shown an emergent ability to tackle complex reasoning tasks, such as mathematical computations. However, even for the simplest arithmetic calculations, the intrinsic mechanisms behind LLMs remains mysterious, makin... | [
"Natural Language Processing",
"Computational Linguistics",
"Mathematical Computation",
"Model Interpretability",
"Neural Networks"
] | |
ICML | 2,024 | 33,096 | How Spurious Features are Memorized: Precise Analysis for Random and NTK Features | Deep learning models are known to overfit and memorize spurious features in the training dataset. While numerous empirical studies have aimed at understanding this phenomenon, a rigorous theoretical framework to quantify it is still missing. In this paper, we consider spurious features that are uncorrelated with the le... | [
"Machine Learning Theory",
"Deep Learning",
"Generalization and Overfitting",
"Neural Networks",
"Feature Analysis"
] | |
ICML | 2,024 | 33,880 | An Empirical Study of Realized GNN Expressiveness | Research on the theoretical expressiveness of Graph Neural Networks (GNNs) has developed rapidly, and many methods have been proposed to enhance the expressiveness. However, most methods do not have a uniform expressiveness measure except for a few that strictly follow the $k$-dimensional Weisfeiler-Lehman ($k$-WL) tes... | [
"Graph Neural Networks",
"Computational Graph Theory",
"Neural Network Expressiveness",
"Empirical Studies in AI"
] | |
NeurIPS | 2,022 | 63,186 | Intentional Dance Choreography with Semi-Supervised Recurrent VAEs | We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder. Given a set of dance sequences of which 1% include qualitative choreographic annotations, PirouNet conditionally generates dance sequences in the style and intention of the choreographer. | [
"Computer Vision",
"Computational Creativity",
"Dance Technology"
] | |
NeurIPS | 2,023 | 70,378 | Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models | The Video-to-Audio (V2A) model has recently gained attention for its practical application in generating audio directly from silent videos, particularly in video/film production. However, previous methods in V2A have limited generation quality in terms of temporal synchronization and audio-visual relevance. We present ... | [
"Audio-Visual Synthesis",
"Deep Learning",
"Latent Diffusion Models",
"Video Processing",
"Audio Processing",
"Multimedia Computing"
] | |
NeurIPS | 2,023 | 71,897 | Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization | This paper studies delayed stochastic algorithms for weakly convex optimization in a distributed network with workers connected to a master node. Recently, Xu~et~al.~2022 showed that an inertial stochastic subgradient method converges at a rate of $\mathcal{O}(\tau_{\text{max}}/\sqrt{K})$ which depends on the maxim... | [
"Distributed Optimization",
"Stochastic Optimization",
"Convex Optimization",
"Algorithm Design",
"Numerical Analysis"
] | |
ICML | 2,024 | 33,478 | Asymptotically Optimal and Computationally Efficient Average Treatment Effect Estimation in A/B testing | Motivated by practical applications in clinical trials and online platforms, we study A/B testing with the aim of estimating a confidence interval (CI) for the average treatment effect (ATE) using the minimum expected sample size. This CI should have a width at most $\epsilon$ while ensuring that the probability of the... | [
"Statistics",
"Experimental Design",
"Clinical Trials",
"Online Experimentation",
"Optimization"
] | |
ICML | 2,024 | 35,016 | LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views | Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various tasks, recent studies have observed challenges in the generalization of fine-tuned models to unseen distributions (i.e., out-of-distribution; OO... | [
"Transfer Learning",
"Model Fine-tuning",
"Out-of-Distribution Generalization",
"Ensemble Methods",
"Representation Learning"
] | |
NeurIPS | 2,023 | 73,626 | MLFMF: Data Sets for Machine Learning for Mathematical Formalization | We introduce MLFMF, a collection of data sets for benchmarking recommendation systems used to support formalization of mathematics with proof assistants. These systems help humans identify which previous entries (theorems, constructions, datatypes, and postulates) are relevant in proving a new theorem or carrying out a... | [
"Mathematical Formalization",
"Proof Assistants",
"Data Sets",
"Recommender Systems",
"Formalized Mathematics",
"Computational Mathematics"
] | |
ICLR | 2,023 | 11,608 | Learning to Decompose Visual Features with Latent Textual Prompts | Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations. Nonetheless, for downstream inference, CLIP-like models suffer from either 1) degraded accuracy and robustness in the case of inaccurate text descriptions during retrieval-based i... | [
"Computer Vision",
"Vision-Language Models",
"Natural Language Processing",
"Transfer Learning",
"Representation Learning"
] | |
NeurIPS | 2,022 | 58,554 | Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers | Machine learning models used for predictive modeling tasks spanning across personalization, recommender systems, ad response prediction, fraud detection etc. typically require a variety of tabular as well as sequential activity features about the user. For tasks like click-through or conversion (purchase) rate predicti... | [
"Self-supervised Learning",
"Representation Learning",
"Transformers",
"Sequential Data",
"Tabular Data",
"Recommender Systems",
"Fraud Detection",
"Predictive Modeling"
] | |
ICML | 2,024 | 37,071 | Improving Flow Matching for Posterior Inference with Physics-based Controls | Flow-based generative modeling is a powerful tool for solving inverse problems in physical sciences that can be used for sampling and likelihood evaluation with much lower inference times than traditional methods. We propose to refine flows with additional control signals based on an underlying physics model. In our ex... | [
"Inverse Problems",
"Physics-based Modeling",
"Generative Models",
"Computational Physics",
"Astronomy",
"Bayesian Inference"
] | |
ICML | 2,023 | 24,678 | Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR | In this paper, we study risk-sensitive Reinforcement Learning (RL), focusing on the objective of Conditional Value at Risk (CVaR) with risk tolerance $\tau$. Starting with multi-arm bandits (MABs), we show the minimax CVaR regret rate is $\Omega(\sqrt{\tau^{-1}AK})$, where $A$ is the number of actions and $K$ is the nu... | [
"Reinforcement Learning",
"Risk-Sensitive Optimization",
"Multi-Armed Bandits",
"Markov Decision Processes",
"Computational Efficiency in Algorithms"
] | |
NeurIPS | 2,023 | 76,857 | Can Deep Learning help to forecast deforestation in the Amazonian Rainforest? | Deforestation is a major driver of climate change. To mitigate deforestation, carbon offset projects aim to protect forest areas at risk. However, existing literature shows that most projects have substantially overestimated the risk of deforestation, thereby issuing carbon credits without equivalent emissions reductio... | [
"Environmental Science",
"Climate Change",
"Remote Sensing",
"Conservation Science"
] | |
ICLR | 2,024 | 17,635 | LRM: Large Reconstruction Model for Single Image to 3D | We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category-specific fashion, LRM adopts a highly scalable transformer-based arc... | [
"Computer Vision",
"3D Reconstruction",
"Neural Networks",
"Deep Learning",
"Image Processing",
"Neural Radiance Fields ",
"Transformer Models"
] | |
ICLR | 2,022 | 6,865 | Shallow and Deep Networks are Near-Optimal Approximators of Korobov Functions | In this paper, we analyze the number of neurons and training parameters that a neural network needs to approximate multivariate functions of bounded second mixed derivatives --- Korobov functions. We prove upper bounds on these quantities for shallow and deep neural networks, drastically lessening the curse of dimensio... | [
"Neural Networks",
"Function Approximation",
"Computational Mathematics"
] | |
NeurIPS | 2,023 | 70,904 | Learning Domain-Aware Detection Head with Prompt Tuning | Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. However, existing methods focus on reducing the domain bias of the detection backbone by inferring a discriminative visual encoder, while ignoring the domain bias in the detection ... | [
"Computer Vision",
"Domain Adaptation",
"Object Detection",
"Vision-Language Models"
] | |
ICLR | 2,024 | 17,527 | Learning Multi-Agent Communication with Contrastive Learning | Communication is a powerful tool for coordination in multi-agent RL. But inducing an effective, common language is a difficult challenge, particularly in the decentralized setting. In this work, we introduce an alternative perspective where communicative messages sent between agents are considered as different incomple... | [
"Multi-Agent Reinforcement Learning",
"Contrastive Learning",
"Machine Learning Communication",
"Decentralized Systems"
] | |
ICLR | 2,023 | 11,078 | SP2 : A Second Order Stochastic Polyak Method | Recently the SP (Stochastic Polyak step size) method has emerged as a competitive adaptive method for setting the step sizes of SGD. SP can be interpreted as a method specialized to interpolated models, since it solves the interpolation equations. SP solves these equation by using local linearizations of the model. W... | [
"Optimization",
"Numerical Analysis",
"Stochastic Methods",
"Second-Order Methods"
] | |
NeurIPS | 2,022 | 53,053 | MoCoDA: Model-based Counterfactual Data Augmentation | The number of states in a dynamic process is exponential in the number of objects, making reinforcement learning (RL) difficult in complex, multi-object domains. For agents to scale to the real world, they will need to react to and reason about unseen combinations of objects. We argue that the ability to recognize and... | [
"Reinforcement Learning",
"Robotics",
"Data Augmentation",
"Model-based Learning"
] | |
ICML | 2,023 | 23,600 | Simplified Temporal Consistency Reinforcement Learning | Reinforcement learning (RL) is able to solve complex sequential decision-making tasks but is currently limited by sample efficiency and required computation. To improve sample efficiency, recent work focuses on model-based RL which interleaves model learning with planning. Recent methods further utilize policy learning... | [
"Reinforcement Learning",
"Model-Based Reinforcement Learning",
"Model-Free Reinforcement Learning",
"Representation Learning"
] | |
ICLR | 2,022 | 6,849 | Understanding over-squashing and bottlenecks on graphs via curvature | Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph. Recent works pointed to the distortion of information flowing from distant nodes as a factor limiting the efficiency of message passing for tasks relying on long-distance interactions. This phen... | [
"Graph Neural Networks",
"Graph Theory",
"Network Analysis",
"Computational Geometry"
] | |
NeurIPS | 2,023 | 71,807 | Practical Differentially Private Hyperparameter Tuning with Subsampling | Tuning the hyperparameters of differentially private (DP) machine learning (ML) algorithms often requires use of sensitive data and this may leak private information via hyperparameter values. Recently, Papernot and Steinke (2022) proposed a certain class of DP hyperparameter tuning algorithms, where the number of rand... | [
"Differential Privacy",
"Hyperparameter Tuning",
"Data Privacy",
"Computational Efficiency"
] | |
NeurIPS | 2,022 | 63,068 | Equivariant Graph Hierarchy-based Neural Networks | Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems. Existing EGNs conduct flat message passing, which, yet, is unable to capture the spatial/dynamical hierarchy for complex systems particularly, limiting substructure discovery and global information fusio... | [
"Graph Neural Networks",
"Computational Physics",
"Multi-body Dynamics",
"Protein Dynamics Modeling"
] | |
NeurIPS | 2,023 | 72,181 | Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete | We consider the algorithmic problem of finding the optimal weights and biases for a two-layer fully connected neural network to fit a given set of data points, also known as empirical risk minimization. We show that the problem is $\exists\mathbb{R}$-complete. This complexity class can be defined as the set of algorith... | [
"Computational Complexity",
"Neural Networks",
"Machine Learning Theory",
"Algorithmic Complexity"
] | |
ICML | 2,024 | 35,017 | Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching | In real-world scenarios, arbitrary interactions with the environment can often be costly, and actions of expert demonstrations are not always available. To reduce the need for both, offline Learning from Observations (LfO) is extensively studied: the agent learns to solve a task given only expert states and task-agnost... | [
"Reinforcement Learning",
"Imitation Learning",
"Offline Learning",
"Learning from Observations ",
"Optimal Transport",
"Wasserstein Distance"
] | |
NeurIPS | 2,023 | 72,564 | Tuning Multi-mode Token-level Prompt Alignment across Modalities | Advancements in prompt tuning of vision-language models have underscored their potential in enhancing open-world visual concept comprehension. However, prior works only primarily focus on single-mode (only one prompt for each modality) and holistic level (image or sentence) semantic alignment, which fails to capture th... | [
"Vision-Language Models",
"Multi-modal Learning",
"Prompt Tuning",
"Semantic Alignment",
"Image Recognition",
"Few-shot Learning"
] | |
NeurIPS | 2,023 | 73,485 | Mind2Web: Towards a Generalist Agent for the Web | We introduce Mind2Web, the first dataset for developing and evaluating generalist agents for the web that can follow language instructions to complete complex tasks on any website. Existing datasets for web agents either use simulated websites or only cover a limited set of websites and tasks, thus not suitable for gen... | [
"Natural Language Processing",
"Human-Computer Interaction",
"Web Technologies"
] | |
ICML | 2,022 | 17,297 | Interactive Inverse Reinforcement Learning for Cooperative Games | We study the problem of designing autonomous agents that can learn to cooperate effectively with a potentially suboptimal partner while having no access to the joint reward function. This problem is modeled as a cooperative episodic two-agent Markov decision process. We assume control over only the first of the two age... | [
"Reinforcement Learning",
"Multi-Agent Systems",
"Game Theory"
] | |
ICLR | 2,024 | 17,638 | Large Language Models Are Not Robust Multiple Choice Selectors | Multiple choice questions (MCQs) serve as a common yet important task format in the evaluation of large language models (LLMs). This work shows that modern LLMs are vulnerable to option position changes in MCQs due to their inherent “selection bias”, namely, they prefer to select specific option IDs as answers (like “O... | [
"Natural Language Processing",
"Model Robustness",
"Bias in AI Models"
] | |
NeurIPS | 2,023 | 70,582 | Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction | Time series anomaly detection is challenging due to the complexity and variety of patterns that can occur. One major difficulty arises from modeling time-dependent relationships to find contextual anomalies while maintaining detection accuracy for point anomalies. In this paper, we propose a framework for unsupervised ... | [
"Time Series Analysis",
"Anomaly Detection",
"Unsupervised Learning",
"Data Science"
] | |
ICML | 2,024 | 35,115 | Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation | Model-Free Reinforcement Learning (MFRL), leveraging the policy gradient theorem, has demonstrated considerable success in continuous control tasks. However, these approaches are plagued by high gradient variance due to zeroth-order gradient estimation, resulting in suboptimal policies. Conversely, First-Order Model-Ba... | [
"Reinforcement Learning",
"Model-Based Reinforcement Learning",
"Continuous Control",
"Differentiable Simulation",
"Robotics and Control Systems"
] | |
ICML | 2,022 | 17,195 | A Functional Information Perspective on Model Interpretation | Contemporary predictive models are hard to interpret as their deep nets exploit numerous complex relations between input elements. This work suggests a theoretical framework for model interpretability by measuring the contribution of relevant features to the functional entropy of the network with respect to the input. ... | [
"Machine Learning Interpretability",
"Information Theory",
"Deep Learning",
"Theoretical Computer Science"
] | |
ICLR | 2,024 | 18,109 | FedWon: Triumphing Multi-domain Federated Learning Without Normalization | Federated learning (FL) enhances data privacy with collaborative in-situ training on decentralized clients. Nevertheless, FL encounters challenges due to non-independent and identically distributed (non-i.i.d) data, leading to potential performance degradation and hindered convergence. While prior studies predominantly... | [
"Federated Learning",
"Data Privacy",
"Multi-domain Learning",
"Distributed Systems"
] | |
ICML | 2,024 | 33,862 | Criterion Collapse and Loss Distribution Control | In this work, we consider the notion of "criterion collapse," in which optimization of one metric implies optimality in another, with a particular focus on conditions for collapse into error probability minimizers under a wide variety of learning criteria, ranging from DRO and OCE risks (CVaR, tilted ERM) to non-monoto... | [
"Optimization",
"Risk Management",
"Statistical Learning Theory"
] | |
NeurIPS | 2,023 | 74,159 | Reproducibility Study of ”Label-Free Explainability for Unsupervised Models” | In this work, we present our reproducibility study of "Label-Free Explainability for Unsupervised Models", a paper that introduces two post‐hoc explanation techniques for neural networks: (1) label‐free feature importance and (2) label‐free example importance. Our study focuses on the reproducibility of the authors’ mo... | [
"Explainable AI",
"Reproducibility Studies",
"Neural Networks",
"Unsupervised Learning"
] | |
NeurIPS | 2,022 | 57,459 | Boosting Multi-modal Contrastive Learning with Modern Hopfield Networks and InfoLOOB | CLIP yielded impressive results on zero-shot transfer learning tasks and is considered as a foundation model like BERT or GPT3. CLIP vision models that have a rich representation are pre-trained using the InfoNCE objective and natural language supervision before they are fine-tuned on particular tasks. Though CLIP exce... | [
"Multi-modal Learning",
"Contrastive Learning",
"Neural Networks",
"Transfer Learning",
"Computer Vision",
"Natural Language Processing"
] | |
NeurIPS | 2,022 | 56,987 | Towards Creating Benchmark Datasets of Universal Neural Network Potential for Material Discovery | Recently, neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for computational material discovery. Especially in recent years, large-scale datasets have begun to emerge for the purpose of ensuring versatility. However, we show that even with a large dataset... | [
"Computational Materials Science",
"Machine Learning in Materials Science",
"Neural Network Potentials",
"Atomistic Simulations",
"Dataset Benchmarking"
] | |
NeurIPS | 2,022 | 54,562 | Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations | Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between... | [
"Contrastive Learning",
"Video-and-Language Representation",
"Multimodal Learning",
"Computer Vision",
"Natural Language Processing"
] | |
ICML | 2,023 | 23,842 | Who Needs to Know? Minimal Knowledge for Optimal Coordination | To optimally coordinate with others in cooperative games, it is often crucial to have information about one’s collaborators: successful driving requires understanding which side of the road to drive on. However, not every feature of collaborators is strategically relevant: the fine-grained acceleration of drivers may b... | [
"Game Theory",
"Cooperative Games",
"Multi-Agent Systems",
"Decision Making",
"Dynamic Games",
"Information Theory"
] | |
ICML | 2,024 | 34,351 | Shifted Interpolation for Differential Privacy | Noisy gradient descent and its variants are the predominant algorithms for differentially private machine learning. It is a fundamental question to quantify their privacy leakage, yet tight characterizations remain open even in the foundational setting of convex losses. This paper improves over previous analyses by est... | [
"Differential Privacy",
"Optimization",
"Privacy Analysis",
"Convex Optimization"
] | |
ICML | 2,023 | 25,802 | Gradient Scaling on Deep Spiking Neural Networks with Spike-Dependent Local Information | Deep spiking neural networks (SNNs) are promising neural networks for their model capacity from deep neural network architecture and energy efficiency from SNNs' operations. To train deep SNNs, recently, spatio-temporal backpropagation (STBP) with surrogate gradient was proposed. Although deep SNNs have been successful... | [
"Deep Learning",
"Spiking Neural Networks",
"Neural Network Training",
"Computational Neuroscience"
] |
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