yusuke1997/dblp-id-title huggingface dataset:
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective
Conditional Object-Centric Learning from Video
Boosting Multiple Views for pretrained-based Continual Learning
Improved Autoregressive Modeling with Distribution Smoothing
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
Learning Rotation-Agnostic Representations via Group Equivariant VAEs
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection
Optimistic Exploration even with a Pessimistic Initialisation
Pointer Sentinel Mixture Models
Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL
Isometric Propagation Network for Generalized Zero-shot Learning
Neural Context Flows for Meta-Learning of Dynamical Systems
Communication in Multi-Agent Reinforcement Learning: Intention Sharing
MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts
To Grok or not to Grok: Disentangling Generalization and Memorization on Corrupted Algorithmic Datasets
GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation
A Restoration Network as an Implicit Prior
Complementary Label Learning with Positive Label Guessing and Negative Label Enhancement
Efficient Federated Domain Translation
Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models
AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?
Meta-Learning with Neural Tangent Kernels
Efficient variational Bayesian neural network ensembles for outlier detection
Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time
Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks
Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models
Large Language Models to Enhance Bayesian Optimization
Prioritized Generative Replay
Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
Universal Backdoor Attacks
Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"
Improving Transformation Invariance in Contrastive Representation Learning
The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024
TEDDY: Trimming Edges with Degree-based Discrimination Strategy
Snapshot Ensembles: Train 1, Get M for Free
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Do Egocentric Video-Language Models Truly Understand Hand-Object Interactions?
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
Learning Efficient Tensor Representations with Ring Structure Networks
CAMBranch: Contrastive Learning with Augmented MILPs for Branching
Estimating Conditional Mutual Information for Dynamic Feature Selection
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Incremental False Negative Detection for Contrastive Learning
A Theoretical Analysis of the Number of Shots in Few-Shot Learning
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation
Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embeddings
Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers
AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents
Mixture of Weak and Strong Experts on Graphs
General Graph Random Features
HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
Development of JavaScript-based deep learning platform and application to distributed training
Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise
Deterministic Variational Inference for Robust Bayesian Neural Networks
Temporal Generalization Estimation in Evolving Graphs
LAVA: Data Valuation without Pre-Specified Learning Algorithms
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
Progressively Compressed Auto-Encoder for Self-supervised Representation Learning
DRoP: Distributionally Robust Data Pruning
OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learning
Machine Unlearning via Simulated Oracle Matching
Robustness of Classifiers to Universal Perturbations: A Geometric Perspective
Rethinking the Uniformity Metric in Self-Supervised Learning
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?
Effective and Efficient Federated Tree Learning on Hybrid Data
Local Composite Saddle Point Optimization
PEARL: Parallel Speculative Decoding with Adaptive Draft Length
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching
What Does a Visual Formal Analysis of the World's 500 Most Famous Paintings Tell Us About Multimodal LLMs?
Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking
LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation
MAI: A Multi-turn Aggregation-Iteration Model for Composed Image Retrieval
LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100x Faster Inference
LiFT: Learning to Fine-Tune via Bayesian Parameter Efficient Meta Fine-Tuning
Toward Understanding the Impact of Staleness in Distributed Machine Learning
Efficient Offline Policy Optimization with a Learned Model
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy Optimization
Generative Adversarial Equilibrium Solvers
CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features
LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
Divergence of Neural Tangent Kernel in Classification Problems
Mitigating Metastable Failures in Distributed Systems with Offline Reinforcement Learning
At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks?
Self-supervised Learning from a Multi-view Perspective
Improving Object-centric Learning with Query Optimization
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization
SNAS: stochastic neural architecture search
Online Reinforcement Learning in Non-Stationary Context-Driven Environments
Are Large Vision Language Models Good Game Players?
TIS-DPO: Token-level Importance Sampling for Direct Preference Optimization With Estimated Weights
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP
CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes
RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations
Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness
CFlowNets: Continuous Control with Generative Flow Networks
Adversarial AutoMixup
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models
Recurrent Batch Normalization
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining
Designing Neural Network Architectures using Reinforcement Learning
Tighter Sparse Approximation Bounds for ReLU Neural Networks
HIFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance
GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost
Re-calibrating Feature Attributions for Model Interpretation
Generalised Lookahead Optimiser
Improving Language Model Distillation through Hidden State Matching
Semi-supervised Community Detection via Structural Similarity Metrics
Revisiting Few-sample BERT Fine-tuning
Inducing Document Representations from Graphs: A Blueprint
Natural Language Inference Improves Compositionality in Vision-Language Models
Towards Provable Control for Unknown Linear Dynamical Systems
Analytical solutions for a family of single layer neural network regression problems
MT3: Multi-Task Multitrack Music Transcription
No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images
Mitigating Memorization of Noisy Labels via Regularization between Representations
Bidirectional Language Models Are Also Few-shot Learners
Learning to Control PDEs with Differentiable Physics
Adapting to Distribution Shift by Visual Domain Prompt Generation
Encoding Unitig-level Assembly Graphs with Heterophilous Constraints for Metagenomic Contigs Binning
Discriminating image representations with principal distortions
Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models
Problem-Parameter-Free Federated Learning
Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement
Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgetting
Learning-Augmented Frequent Directions
A General Framework for User-Guided Bayesian Optimization
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
The In-Sample Softmax for Offline Reinforcement Learning
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs
Bayesian Context Aggregation for Neural Processes
CASR: Generating Complex Sequences with Autoregressive Self-Boost Refinement
Towards hyperparameter-free optimization with differential privacy
Learning to Steer Markovian Agents under Model Uncertainty
Certified Training: Small Boxes are All You Need
The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022
Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training
Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models
Towards Optimal Multi-draft Speculative Decoding
Progressive Fourier Neural Representation for Sequential Video Compilation
RelitLRM: Generative Relightable Radiance for Large Reconstruction Models
RA-TTA: Retrieval-Augmented Test-Time Adaptation for Vision-Language Models
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
Learning meta-features for AutoML
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks
A Generalized Training Approach for Multiagent Learning
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
Wizard of Wikipedia: Knowledge-Powered Conversational Agents
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
Latent Space Oddity: on the Curvature of Deep Generative Models
Sensitivity Verification for Additive Decision Tree Ensembles
An Experimental Design Perspective on Model-Based Reinforcement Learning
Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting
FIRING-Net: A filtered feature recycling network for speech enhancement
Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time
ADAM Optimization with Adaptive Batch Selection
Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length
Presto! Distilling Steps and Layers for Accelerating Music Generation
Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions
Efficient Planning in a Compact Latent Action Space
Controlling generative models with continuous factors of variations
CoMotion: Concurrent Multi-person 3D Motion
Zero-Shot Natural Language Explanations
Dropout Q-Functions for Doubly Efficient Reinforcement Learning
DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
Lost in Translation: GANs' Inability to Generate Simple Probability Distributions
Towards Robustness Against Natural Language Word Substitutions
Robust Function-Calling for On-Device Language Model via Function Masking
RecDreamer: Consistent Text-to-3D Generation via Uniform Score Distillation
Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention
Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning
One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt
Graph Neural Networks for Link Prediction with Subgraph Sketching
Simplifying, Stabilizing and Scaling Continuous-time Consistency Models
Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency
Learning Fine-Grained Representations through Textual Token Disentanglement in Composed Video Retrieval
Intermediate Layer Classifiers for OOD generalization
Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity
9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021
Preference Elicitation for Offline Reinforcement Learning
Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
Enhancing Spiking Transformers with Binary Attention Mechanisms
Towards Offline Opponent Modeling with In-context Learning
Over-Training with Mixup May Hurt Generalization
Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning
Impossibly Good Experts and How to Follow Them
Prediction without Preclusion: Recourse Verification with Reachable Sets
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
Stochastic Conditional Generative Networks with Basis Decomposition
W-CTC: a Connectionist Temporal Classification Loss with Wild Cards
Toward Optimal Policy Population Growth in Two-Player Zero-Sum Games
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100
A Simple Approach to Unifying Diffusion-based Conditional Generation
How Sharpness-Aware Minimization Minimizes Sharpness?
Detecting Statistical Interactions from Neural Network Weights
Compressed Context Memory for Online Language Model Interaction
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach
Simple ReFlow: Improved Techniques for Fast Flow Models
A Quadratic Synchronization Rule for Distributed Deep Learning
Overparameterisation and worst-case generalisation: friend or foe?
Learning to Jointly Understand Visual and Tactile Signals
Interpretable Debiasing of Vectorized Language Representations with Iterative Orthogonalization
More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization
Gradient descent aligns the layers of deep linear networks
Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
The Local Elasticity of Neural Networks
Backdooring Vision-Language Models with Out-Of-Distribution Data
Solar Panel Segmentation: Self-Supervised Learning Solutions for Imperfect Datasets
Neural representation and generation for RNA secondary structures
Adversarially Robust Conformal Prediction
Predicting Medications from Diagnostic Codes with Recurrent Neural Networks
Interpolation-Prediction Networks for Irregularly Sampled Time Series
Frustratingly Simple Prompting-based Text Denoising
Attention Based Variational Graph Auto-Encoder (AVGAE)
Editing models with task arithmetic
Pareto Set Learning for Neural Multi-Objective Combinatorial Optimization
Breach By A Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses
A Foundation Model for Error Correction Codes
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments
SLiMe: Segment Like Me
Concept Pinpoint Eraser for Text-to-image Diffusion Models via Residual Attention Gate
Beyond Single Concept Vector: Modeling Concept Subspace in LLMs with Gaussian Distribution
Reinforced active learning for image segmentation
Learning Locality and Isotropy in Dialogue Modeling
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation
Collapsed Language Models Promote Fairness
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning
LIFe-GoM: Generalizable Human Rendering with Learned Iterative Feedback Over Multi-Resolution Gaussians-on-Mesh
The Role of Pretrained Representations for the OOD Generalization of RL Agents
Generative Modeling with Phase Stochastic Bridge
FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking
Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Adversarial Attacks on Graph Neural Networks via Meta Learning
Diffusion Models for Multi-Task Generative Modeling
Score-based Continuous-time Discrete Diffusion Models
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
Towards a Unified View of Parameter-Efficient Transfer Learning
A Probabilistic Formulation of Unsupervised Text Style Transfer
Scalable Modular Network: A Framework for Adaptive Learning via Agreement Routing
In-Context Pretraining: Language Modeling Beyond Document Boundaries
Scalable Universal T-Cell Receptor Embeddings from Adaptive Immune Repertoires
Towards Understanding Factual Knowledge of Large Language Models
Incremental Causal Effect for Time to Treatment Initialization
Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts
Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes
A Differential Geometric View and Explainability of GNN on Evolving Graphs
Diffusion Bridge Implicit Models
Permute-and-Flip: An optimally stable and watermarkable decoder for LLMs
Escaping Saddle Points Faster with Stochastic Momentum
Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation
Rethinking the Hyperparameters for Fine-tuning
Connecting Federated ADMM to Bayes
Varying Shades of Wrong: Aligning LLMs with Wrong Answers Only
Exploring the Camera Bias of Person Re-identification
Near-Exact Privacy Amplification for Matrix Mechanisms
Approximately Piecewise E(3) Equivariant Point Networks
SurFhead: Affine Rig Blending for Geometrically Accurate 2D Gaussian Surfel Head Avatars
Rapidly Adapting Policies to the Real-World via Simulation-Guided Fine-Tuning
Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models
Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detection
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation
Compositional Preference Models for Aligning LMs
RB-Modulation: Training-Free Stylization using Reference-Based Modulation
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic
Ask, and it shall be given: On the Turing completeness of prompting
On the Role of General Function Approximation in Offline Reinforcement Learning
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning
Effects of Graph Convolutions in Multi-layer Networks
Identifiability Results for Multimodal Contrastive Learning
Massively Scalable Inverse Reinforcement Learning in Google Maps
Noisy Test-Time Adaptation in Vision-Language Models
Learning Hyper Label Model for Programmatic Weak Supervision
A Good Image Generator Is What You Need for High-Resolution Video Synthesis
Training Generative Adversarial Networks via Primal-Dual subgradient Methods: a Lagrangian Perspective on GaN
Strong Preferences Affect the Robustness of Preference Models and Value Alignment
Energy-Based Diffusion Language Models for Text Generation
UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate
Extending the Framework of Equilibrium Propagation to General Dynamics
Learning Planning Abstractions from Language
Learning Randomized Algorithms with Transformers
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
Exploring the Promise and Limits of Real-Time Recurrent Learning
Language GANs Falling Short
On the loss landscape of a class of deep neural networks with no bad local valleys
DiffWave: A Versatile Diffusion Model for Audio Synthesis
Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Language-Image Models with 3D Understanding
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression
LLaVA-MoD: Making LLaVA Tiny via MoE-Knowledge Distillation
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Consistency Checks for Language Model Forecasters
Semi-Supervised CLIP Adaptation by Enforcing Semantic and Trapezoidal Consistency
A Characterization Theorem for Equivariant Networks with Point-wise Activations
Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion
Federated Learning with Matched Averaging
Reflective Gaussian Splatting
Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation
On the Hardness of Constrained Cooperative Multi-Agent Reinforcement Learning
From Sparse to Soft Mixtures of Experts
SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models
Enabling Arbitrary Translation Objectives with Adaptive Tree Search
Automatic Rule Extraction from Long Short Term Memory Networks
3D Vision-Language Gaussian Splatting
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
Local Patterns Generalize Better for Novel Anomalies
NOLA: Compressing LoRA using Linear Combination of Random Basis
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Hyperbolic Neural Networks++
IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning
Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model
Following the Human Thread in Social Navigation
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
Schema Inference for Interpretable Image Classification
Matching receptor to odorant with protein language and graph neural networks
ELFS: Label-Free Coreset Selection with Proxy Training Dynamics
Small nonlinearities in activation functions create bad local minima in neural networks