yusuke1997/dblp-id-title huggingface dataset:
3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining
Dataless Knowledge Fusion by Merging Weights of Language Models
Building Normalizing Flows with Stochastic Interpolants
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
ParFam - (Neural Guided) Symbolic Regression via Continuous Global Optimization
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
Towards Semantic Equivalence of Tokenization in Multimodal LLM
Learning Compositional Koopman Operators for Model-Based Control
Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time
InCoder: A Generative Model for Code Infilling and Synthesis
On the Importance of Difficulty Calibration in Membership Inference Attacks
RevisEval: Improving LLM-as-a-Judge via Response-Adapted References
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods
Submodular Reinforcement Learning
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning
MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection
AtomNAS: Fine-Grained End-to-End Neural Architecture Search
Continual Unsupervised Disentangling of Self-Organizing Representations
Latent Action Pretraining from Videos
MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises
Better Neural PDE Solvers Through Data-Free Mesh Movers
On Learning Universal Representations Across Languages
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
Fast Fourier Convolutions in Self-Supervised Neural Networks for Image Denoising
Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo
Learning Weight Sensitivity from Entropy
Partitioned Learned Bloom Filters
Weighted Training for Cross-Task Learning
Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement Learning
Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
Optimal Protocols for Continual Learning via Statistical Physics and Control Theory
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
Enhancing Neural Training via a Correlated Dynamics Model
Emergent Road Rules In Multi-Agent Driving Environments
An Agnostic Approach to Federated Learning with Class Imbalance
Score Regularized Policy Optimization through Diffusion Behavior
Provable Convergence Bounds for Hybrid Dynamical Sampling and Optimization
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework
Multimeasurement Generative Models
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
Universal Image Restoration Pre-training via Degradation Classification
Disambiguating Symbolic Expressions in Informal Documents
Singular Subspace Perturbation Bounds via Rectangular Random Matrix Diffusions
Accurate Bayesian Meta-Learning by Accurate Task Posterior Inference
ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models
Learn to Pay Attention
The Role of ImageNet Classes in Fréchet Inception Distance
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
Neural Tangents: Fast and Easy Infinite Neural Networks in Python
Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision
InfoCon: Concept Discovery with Generative and Discriminative Informativeness
Automatically Inferring Data Quality for Spatiotemporal Forecasting
Bootstrapping Semantic Segmentation with Regional Contrast
An Empirical study of Binary Neural Networks' Optimisation
Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
Caveats for information bottleneck in deterministic scenarios
Scaling Large Language Model-based Multi-Agent Collaboration
LASeR: Towards Diversified and Generalizable Robot Design with Large Language Models
Robust Feature Learning for Multi-Index Models in High Dimensions
FreeDyG: Frequency Enhanced Continuous-Time Dynamic Graph Model for Link Prediction
The optimality of kernel classifiers in Sobolev space
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
Conformal Language Model Reasoning with Coherent Factuality
Masked Image Modeling with Denoising Contrast
Language Model Alignment in Multilingual Trolley Problems
Confidence-Based Feature Imputation for Graphs with Partially Known Features
DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling
Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates
Small-to-Large Generalization: Training Data Influences Models Consistently Across Scale
GFlowNets and variational inference
Understanding The Robustness of Self-supervised Learning Through Topic Modeling
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Adaptive Batch Size for Privately Finding Second-Order Stationary Points
Addressing Signal Delay in Deep Reinforcement Learning
SF(DA)2: Source-free Domain Adaptation Through the Lens of Data Augmentation
State Advantage Weighting for Offline RL
Max-Margin Works while Large Margin Fails: Generalization without Uniform Convergence
Equivariant Graph Mechanics Networks with Constraints
IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities
Towards Robust Offline Reinforcement Learning under Diverse Data Corruption
Graph Expansion in Pruned Recurrent Neural Network Layers Preserves Performance
Continual Learning with Filter Atom Swapping
Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
DiGress: Discrete Denoising diffusion for graph generation
Boosting Adversarial Transferability using Dynamic Cues
Scale-Equivariant Steerable Networks
Whittle Index with Multiple Actions and State Constraint for Inventory Management
EgoExo-Gen: Ego-centric Video Prediction by Watching Exo-centric Videos
A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs
Aligned Datasets Improve Detection of Latent Diffusion-Generated Images
Learning from weak labelers as constraints
R2Det: Exploring Relaxed Rotation Equivariance in 2D Object Detection
Methods for Convex (L0, L1)-Smooth Optimization: Clipping, Acceleration, and Adaptivity
Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering
Self-ensemble Adversarial Training for Improved Robustness
Can We Evaluate Domain Adaptation Models Without Target-Domain Labels?
Computational Language Acquisition with Theory of Mind
Quaternion Recurrent Neural Networks
ThinkBot: Embodied Instruction Following with Thought Chain Reasoning
How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings
GiraffeDet: A Heavy-Neck Paradigm for Object Detection
Learning Harmonized Representations for Speculative Sampling
Exploiting Time Channel Vulnerability of Learned Bloom Filters
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off
Learning Mask Invariant Mutual Information for Masked Image Modeling
Edge-aware Image Smoothing with Relative Wavelet Domain Representation
Error Analysis of Fitted Q-iteration with ReLU-activated Deep Neural Networks
Spectral Decomposition Representation for Reinforcement Learning
Learning to Discretize Denoising Diffusion ODEs
JudgeBench: A Benchmark for Evaluating LLM-Based Judges
RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression
Temporal Heterogeneous Graph Generation with Privacy, Utility, and Efficiency
Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
Human Motion Diffusion as a Generative Prior
Linear Multistep Solver Distillation for Fast Sampling of Diffusion Models
HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex Optimization
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks
Probabilistic Numeric Convolutional Neural Networks
Learning Super-Features for Image Retrieval
Severing Spurious Correlations with Data Pruning
SAdam: A Variant of Adam for Strongly Convex Functions
Graph Assisted Offline-Online Deep Reinforcement Learning for Dynamic Workflow Scheduling
V4D: 4D Convolutional Neural Networks for Video-level Representation Learning
Actionable Neural Representations: Grid Cells from Minimal Constraints
Understanding Optimization in Deep Learning with Central Flows
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Models
A Generic Framework for Conformal Fairness
Extending Power of Nature from Binary to Real-Valued Graph Learning in Real World
SOLAR: Sparse Orthogonal Learned and Random Embeddings
SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents
Toeplitz Neural Network for Sequence Modeling
Scalable Model Compression by Entropy Penalized Reparameterization
Patched Denoising Diffusion Models For High-Resolution Image Synthesis
How Much Can CLIP Benefit Vision-and-Language Tasks?
Tuning Recurrent Neural Networks with Reinforcement Learning
Simple Guidance Mechanisms for Discrete Diffusion Models
What Makes a Good Prune? Maximal Unstructured Pruning for Maximal Cosine Similarity
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks
Evaluations and Methods for Explanation through Robustness Analysis
Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought
Differentially Private Federated Learning with Time-Adaptive Privacy Spending
Learning Energy-Based Models by Diffusion Recovery Likelihood
Scalable Mechanistic Neural Networks
An Effective Manifold-based Optimization Method for Distributionally Robust Classification
Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks
Molecule Generation For Target Protein Binding with Structural Motifs
From Tokens to Lattices: Emergent Lattice Structures in Language Models
KFC: Knowledge Reconstruction and Feedback Consolidation Enable Efficient and Effective Continual Generative Learning
Blending Imitation and Reinforcement Learning for Robust Policy Improvement
Long-tailed Diffusion Models with Oriented Calibration
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
Learning to Learn Without Labels
Computing Circuits Optimization via Model-Based Circuit Genetic Evolution
Function-space regularized Rényi divergences
HAMSTER: Hierarchical Action Models for Open-World Robot Manipulation
Mind the Gap: Domain Gap Control for Single Shot Domain Adaptation for Generative Adversarial Networks
ϕ-Update: A Class of Policy Update Methods with Policy Convergence Guarantee
Composing Task-Agnostic Policies with Deep Reinforcement Learning
Coordinate-Aware Modulation for Neural Fields
DPLM-2: A Multimodal Diffusion Protein Language Model
HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts
Adversarially Guided Actor-Critic
Parametrized Hierarchical Procedures for Neural Programming
Training-free Multi-objective Diffusion Model for 3D Molecule Generation
Ensemble Robustness and Generalization of Stochastic Deep Learning Algorithms
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
Leveraging Submodule Linearity Enhances Task Arithmetic Performance in LLMs
EmbodiedSAM: Online Segment Any 3D Thing in Real Time
Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization
Policy Optimization by Genetic Distillation
SSD: A Unified Framework for Self-Supervised Outlier Detection
SkipW: Resource Adaptable RNN with Strict Upper Computational Limit
Robust Unlearnable Examples: Protecting Data Privacy Against Adversarial Learning
SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups
Energy-Inspired Self-Supervised Pretraining for Vision Models
Causal Discovery via Bayesian Optimization
Robustness to Adversarial Examples through an Ensemble of Specialists
How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study
Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework
Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning
code2seq: Generating Sequences from Structured Representations of Code
Generating Liquid Simulations with Deformation-aware Neural Networks
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
Adversarial Audio Synthesis
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
Selective Unlearning via Representation Erasure Using Domain Adversarial Training
Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation
Effective Model Sparsification by Scheduled Grow-and-Prune Methods
DynFrs: An Efficient Framework for Machine Unlearning in Random Forest
Xformer: Hybrid X-Shaped Transformer for Image Denoising
On Non-Random Missing Labels in Semi-Supervised Learning
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Embarrassingly Simple Dataset Distillation
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning
Graph Generation with K2-trees
Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models
TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields
Feature selection and low test error in shallow low-rotation ReLU networks
Dual Algorithmic Reasoning
Specialized Foundation Models Struggle to Beat Supervised Baselines
Memory-based Parameter Adaptation
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement
Federated Nearest Neighbor Machine Translation
Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates
VAE-Var: Variational Autoencoder-Enhanced Variational Methods for Data Assimilation in Meteorology
DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL
Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation
PerFedMask: Personalized Federated Learning with Optimized Masking Vectors
VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning
Skip-Attention: Improving Vision Transformers by Paying Less Attention
Forward χ2 Divergence Based Variational Importance Sampling
Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles
Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
Decoding As Dynamic Programming For Recurrent Autoregressive Models
Intraclass clustering: an implicit learning ability that regularizes DNNs
Optimizing 4D Gaussians for Dynamic Scene Video from Single Landscape Images
Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures
Generalization bounds via distillation
Generating Physical Dynamics under Priors
Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
Functional Regularisation for Continual Learning with Gaussian Processes
DamoFD: Digging into Backbone Design on Face Detection
Harnessing Structures for Value-Based Planning and Reinforcement Learning
Fairness Under Partial Observability
SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction
Orchestrated Value Mapping for Reinforcement Learning
Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments
Defending Against Physically Realizable Attacks on Image Classification
Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies
Diffusion-based Image Translation using disentangled style and content representation
MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer
Threaten Spiking Neural Networks through Combining Rate and Temporal Information
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting
Divide-and-Conquer Reinforcement Learning
Lagrangian Fluid Simulation with Continuous Convolutions
Unsupervised Detection of Cell Assemblies with Graph Neural Networks
Can Neural Networks Learn Implicit Logic from Physical Reasoning?
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
LambdaNetworks: Modeling long-range Interactions without Attention
AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models
Forte : Finding Outliers with Representation Typicality Estimation
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning
Atlas Gaussians Diffusion for 3D Generation
Intrinsic Dimension Correlation: uncovering nonlinear connections in multimodal representations
Domain Generalization via Heckman-type Selection Models
On the Trade-Off between Actionable Explanations and the Right to be Forgotten
A Scalable Self-supervised Learner for Hyperspectral Image Classification
Modeling Fine-Grained Hand-Object Dynamics for Egocentric Video Representation Learning
Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data
Structural Inference with Dynamics Encoding and Partial Correlation Coefficients
Imitation Learning from Observation with Automatic Discount Scheduling
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
Separation Power of Equivariant Neural Networks
Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting
Three Mechanisms of Feature Learning in a Linear Network
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models
Law of the Weakest Link: Cross Capabilities of Large Language Models
Logical Consistency of Large Language Models in Fact-Checking
BaB-ND: Long-Horizon Motion Planning with Branch-and-Bound and Neural Dynamics
Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation
Learning vector fields of differential equations on manifolds with geometrically constrained operator-valued kernels
A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification
The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric
Structured Prediction as Translation between Augmented Natural Languages
Handling Distribution Shifts on Graphs: An Invariance Perspective
Planning in Stochastic Environments with a Learned Model
Multiple sequence alignment as a sequence-to-sequence learning problem
Automating Nearest Neighbor Search Configuration with Constrained Optimization
CoTFormer: A Chain of Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization
Few-shot Cross-domain Image Generation via Inference-time Latent-code Learning
Revisiting adapters with adversarial training
First-order ANIL provably learns representations despite overparametrisation
Controllable Unlearning for Image-to-Image Generative Models via ϵ-Constrained Optimization
Frustratingly Short Attention Spans in Neural Language Modeling
Variational methods for simulation-based inference
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler
RNA Secondary Structure Prediction By Learning Unrolled Algorithms
Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks
Binding Language Models in Symbolic Languages
L2MAC: Large Language Model Automatic Computer for Extensive Code Generation
Fast AdvProp
Generalization and Estimation Error Bounds for Model-based Neural Networks
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
Implicit Normalizing Flows
Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery
On-Policy Model Errors in Reinforcement Learning
Model-agnostic meta-learners for estimating heterogeneous treatment effects over time
Explicitly Minimizing the Blur Error of Variational Autoencoders
Simplified State Space Layers for Sequence Modeling
MAP IT to Visualize Representations
Neural-Guided Deductive Search for Real-Time Program Synthesis from Examples
On a Relation Between the Rate-Distortion Function and Optimal Transport
WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling
Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Efficient Model Updates for Approximate Unlearning of Graph-Structured Data
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization
WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
Generation of a random self-similarity curve
Hidden in the Noise: Two-Stage Robust Watermarking for Images
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
AstroCompress: A benchmark dataset for multi-purpose compression of astronomical data
Robust Pruning at Initialization
Modeling Label Space Interactions in Multi-label Classification using Box Embeddings
GameGen-X: Interactive Open-world Game Video Generation
Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It
Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
Multifactor Sequential Disentanglement via Structured Koopman Autoencoders
Beating Price of Anarchy and Gradient Descent without Regret in Potential Games
Gaussian Mixture Convolution Networks
On the Foundations of Shortcut Learning
Unified Generative Modeling of 3D Molecules with Bayesian Flow Networks
On the Pitfalls of Analyzing Individual Neurons in Language Models
Anyprefer: An Agentic Framework for Preference Data Synthesis
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification
Revisit Finetuning strategy for Few-Shot Learning to Transfer the Emdeddings
GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-Time Alignment
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
Generalized Principal-Agent Problem with a Learning Agent
Synthesizing Programmatic Policies that Inductively Generalize
The Spectral Bias of Polynomial Neural Networks
Fundamental Limitations on Subquadratic Alternatives to Transformers
Momentum Benefits Non-iid Federated Learning Simply and Provably
Restricted Strong Convexity of Deep Learning Models with Smooth Activations
WRPN: Wide Reduced-Precision Networks
Physics-aware, probabilistic model order reduction with guaranteed stability