yusuke1997/dblp-id-title huggingface dataset:
On the Information Bottleneck Theory of Deep Learning
Fair Mixup: Fairness via Interpolation
Model merging with SVD to tie the Knots
Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity
Subtask-Aware Visual Reward Learning from Segmented Demonstrations
Adaptive teachers for amortized samplers
VLAS: Vision-Language-Action Model with Speech Instructions for Customized Robot Manipulation
X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing
Contextual bandits with concave rewards, and an application to fair ranking
Learning Distributionally Robust Models at Scale via Composite Optimization
Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses
Breaking Mental Set to Improve Reasoning through Diverse Multi-Agent Debate
Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games
Sparse Communication via Mixed Distributions
Exponentially vanishing sub-optimal local minima in multilayer neural networks
Do We Really Need Complicated Model Architectures For Temporal Networks?
An Efficient Framework for Crediting Data Contributors of Diffusion Models
One Step Diffusion via Shortcut Models
A Learned Representation For Artistic Style
Knowledge Distillation Based on Transformed Teacher Matching
Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance
Progressive Compositionality in Text-to-Image Generative Models
DEPfold: RNA Secondary Structure Prediction as Dependency Parsing
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
Point Cluster: A Compact Message Unit for Communication-Efficient Collaborative Perception
Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL
Differentiable Optimization of Similarity Scores Between Models and Brains
Debiasing Algorithm through Model Adaptation
New Algorithms for the Learning-Augmented k-means Problem
Learning Implicit Representation for Reconstructing Articulated Objects
Variational Diffusion Posterior Sampling with Midpoint Guidance
On the Convergence of Adam and Beyond
Deep learning generalizes because the parameter-function map is biased towards simple functions
Rethinking Symbolic Regression: Morphology and Adaptability in the Context of Evolutionary Algorithms
OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision
A Deep Variational Approach to Clustering Survival Data
Energy-Weighted Flow Matching for Offline Reinforcement Learning
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
FITS: Modeling Time Series with 10k Parameters
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
Learning Clustering-based Prototypes for Compositional Zero-Shot Learning
GenSE: Generative Speech Enhancement via Language Models using Hierarchical Modeling
Beyond Random Augmentations: Pretraining with Hard Views
Evidential Learning-based Certainty Estimation for Robust Dense Feature Matching
Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning
Personality Alignment of Large Language Models
YaRN: Efficient Context Window Extension of Large Language Models
Program Guided Agent
Multi-lingual Evaluation of Code Generation Models
Graph Convolutional Reinforcement Learning
Disentangling Factors of Variations Using Few Labels
Uncertainty and Influence aware Reward Model Refinement for Reinforcement Learning from Human Feedback
Influence Estimation for Generative Adversarial Networks
Learning Controllable Adaptive Simulation for Multi-resolution Physics
Discovering Clone Negatives via Adaptive Contrastive Learning for Image-Text Matching
SufiSent - Universal Sentence Representations Using Suffix Encodings
Online Bayesian Transfer Learning for Sequential Data Modeling
Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
Universal Successor Representations for Transfer Reinforcement Learning
Nonlinear multiregion neural dynamics with parametric impulse response communication channels
T2V-Turbo-v2: Enhancing Video Model Post-Training through Data, Reward, and Conditional Guidance Design
Why In-Context Learning Models are Good Few-Shot Learners?
WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling
Practical Conditional Neural Process Via Tractable Dependent Predictions
Fair Submodular Cover
VOILA: Evaluation of MLLMs For Perceptual Understanding and Analogical Reasoning
Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled Data
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
DLVM: A modern compiler infrastructure for deep learning systems
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
Scalable Bayesian Inverse Reinforcement Learning
Invariant Causal Representation Learning for Out-of-Distribution Generalization
Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling
Rényi Fair Inference
GS-CPR: Efficient Camera Pose Refinement via 3D Gaussian Splatting
Fake It Until You Make It : Towards Accurate Near-Distribution Novelty Detection
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions
Don't fear the unlabelled: safe semi-supervised learning via debiasing
MeshDiffusion: Score-based Generative 3D Mesh Modeling
Perturbation-Restrained Sequential Model Editing
Implicit Bias of Mirror Flow for Shallow Neural Networks in Univariate Regression
Gnothi Seauton: Empowering Faithful Self-Interpretability in Black-Box Transformers
Mode Normalization
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
AtomSurf: Surface Representation for Learning on Protein Structures
Visualizing and Understanding GANs
OccProphet: Pushing the Efficiency Frontier of Camera-Only 4D Occupancy Forecasting with an Observer-Forecaster-Refiner Framework
On Self Modulation for Generative Adversarial Networks
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance
Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning
Looking Back on Learned Experiences For Class/task Incremental Learning
Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration
Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning
Learning General-purpose Biomedical Volume Representations using Randomized Synthesis
Ins-DetCLIP: Aligning Detection Model to Follow Human-Language Instruction
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Memorization in Self-Supervised Learning Improves Downstream Generalization
Decoupling the Layers in Residual Networks
A Generative Model For Electron Paths
Digi-Q: Learning VLM Q-Value Functions for Training Device-Control Agents
Sustainable Resource Management
Intriguing Properties of Adversarial Examples
Continual evaluation for lifelong learning: Identifying the stability gap
Discriminator Rejection Sampling
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Boosting the Adversarial Robustness of Graph Neural Networks: An OOD Perspective
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
Most discriminative stimuli for functional cell type clustering
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
MatExpert: Decomposing Materials Discovery By Mimicking Human Experts
Towards Open Temporal Graph Neural Networks
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
Adversarial Training for Defense Against Label Poisoning Attacks
Self-Supervised Contrastive Learning for Long-term Forecasting
Code Translation with Compiler Representations
Increasing the Cost of Model Extraction with Calibrated Proof of Work
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency
KLCE: Regularized Imbalance Node-classification Via KL-divergence and Cross-Entropy
DeepOBS: A Deep Learning Optimizer Benchmark Suite
What Matters in Learning from Large-Scale Datasets for Robot Manipulation
Tracking the risk of a deployed model and detecting harmful distribution shifts
Object-Centric Pretraining via Target Encoder Bootstrapping
EA-HAS-Bench: Energy-aware Hyperparameter and Architecture Search Benchmark
The Pitfalls of Memorization: When Memorization Hurts Generalization
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles
CodeT: Code Generation with Generated Tests
Logically Consistent Language Models via Neuro-Symbolic Integration
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
Deep Ranking Ensembles for Hyperparameter Optimization
Ai Gen ASSISTment: Analyzing the Effectiveness of Generative AI Data Amplification for Dkt
Learning to Learn by Zeroth-Order Oracle
Distributional Sliced-Wasserstein and Applications to Generative Modeling
Competing Large Language Models in Multi-Agent Gaming Environments
Representation Learning for Sequence Data with Deep Autoencoding Predictive Components
The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks
A Systematic Study of the Role of Data Quality and Alignment for Fine-tuning LLMs for Enhanced Autoformalization
RMSprop converges with proper hyper-parameter
Deep Geodesic Canonical Correlation Analysis for Covariance-Based Neuroimaging Data
A Unified Framework for Bayesian Optimization under Contextual Uncertainty
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
Unsupervised Machine Translation Using Monolingual Corpora Only
DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale
Bypassing the Safety Training of Open-Source LLMs with Priming Attacks
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
Deep Ensembles for Graphs with Higher-order Dependencies
Towards Hierarchical Rectified Flow
Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning
Cascading Reinforcement Learning
Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning
On the Benefits of Attribute-Driven Graph Domain Adaptation
Surprising Effectiveness of pretraining Ternary Language Model at Scale
Noise-Robust De-Duplication at Scale
Evidential Turing Processes
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
Does CLIP's generalization performance mainly stem from high train-test similarity?
CrossMPT: Cross-attention Message-passing Transformer for Error Correcting Codes
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
Data augmentation alone can improve adversarial training
Non-Equilibrium Dynamics of Hybrid Continuous-Discrete Ground-State Sampling
Fully-inductive Node Classification on Arbitrary Graphs
EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
Towards the Generalization of Contrastive Self-Supervised Learning
Improved Diffusion-based Generative Model with Better Adversarial Robustness
HoME: a Household Multimodal Environment
MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?
Scalable Extraction of Training Data from Aligned, Production Language Models
SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs
Underdamped Diffusion Bridges with Applications to Sampling
L3Ms - Lagrange Large Language Models
Examining Alignment of Large Language Models through Representative Heuristics: the case of political stereotypes
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization
The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning
Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification
Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning
Projection Head is Secretly an Information Bottleneck
Structured Attention Networks
The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation
Spatio-temporal point processes with deep non-stationary kernels
Aligning Language Models with Demonstrated Feedback
Learning with Logical Constraints but without Shortcut Satisfaction
Adaptive Extra-Gradient Methods for Min-Max Optimization and Games
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
Compositional Language Continual Learning
Improved Active Learning via Dependent Leverage Score Sampling
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation
BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics
Contrastive Learning with 3D Shapes
Wasserstein Auto-Encoders
Decomposing Motion and Content for Natural Video Sequence Prediction
Simulating Human-like Daily Activities with Desire-driven Autonomy
End-to-end Optimized Image Compression
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos
GraphBridge: Towards Arbitrary Transfer Learning in GNNs
NANSY++: Unified Voice Synthesis with Neural Analysis and Synthesis
Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images
Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency
Knowledge Localization: Mission Not Accomplished? Enter Query Localization!
Combining policy gradient and Q-learning
Highway and Residual Networks learn Unrolled Iterative Estimation
Towards a Complete Logical Framework for GNN Expressiveness
Long Range Language Modeling via Gated State Spaces
A Direct Approach to Robust Deep Learning Using Adversarial Networks
Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication
Accurate Forgetting for Heterogeneous Federated Continual Learning
Implicit Bias of Adversarial Training for Deep Neural Networks
Language Models Inversely Scale on Piecewise Function Evaluation with Biased Examples
Bespoke Solvers for Generative Flow Models
AutoVP: An Automated Visual Prompting Framework and Benchmark
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders
Learning Graphon Mean Field Games and Approximate Nash Equilibria
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
An Extensible Framework for Open Heterogeneous Collaborative Perception
ToolACE: Winning the Points of LLM Function Calling
Deep reinforcement learning with relational inductive biases
Playing SNES in the Retro Learning Environment
Human-level Protein Localization with Convolutional Neural Networks
An analytic theory of generalization dynamics and transfer learning in deep linear networks
What does the Knowledge Neuron Thesis Have to do with Knowledge?
Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models
Learning representations for binary-classification without backpropagation
Learning more skills through optimistic exploration
Needle Threading: Can LLMs Follow Threads Through Near-Million-Scale Haystacks?
Wasserstein Barycenter Model Ensembling
Residual Loss Prediction: Reinforcement Learning With No Incremental Feedback
Continual Pre-training of Language Models
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning
LEARNING LIGHTWEIGHT STRUCTURE-AWARE EMBEDDINGS FOR PROTEIN SEQUENCES
Fourier Sliced-Wasserstein Embedding for Multisets and Measures
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
Learning Polynomial Problems with SL(2, R)-Equivariance
Language Model Decoding as Direct Metrics Optimization
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Concept-level Debugging of Part-Prototype Networks
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Towards Model Agnostic Federated Learning Using Knowledge Distillation
Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules
Fast Adversarial CNN-based Perturbation Attack on No-Reference Image- and Video-Quality Metrics
Scaling Forward Gradient With Local Losses
Native machine learning for noisy microscopic data processing
Gaussian-Det: Learning Closed-Surface Gaussians for 3D Object Detection
Efficient approximation of neural population structure and correlations with probabilistic circuits
Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States
A statistical theory of cold posteriors in deep neural networks
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models
Edge Guided GANs with Contrastive Learning for Semantic Image Synthesis
Direct Distributional Optimization for Provable Alignment of Diffusion Models
Individually Fair Rankings
Adversarial Generative Flow Network for Solving Vehicle Routing Problems
On Bits and Bandits: Quantifying the Regret-Information Trade-off
USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields
OmniKV: Dynamic Context Selection for Efficient Long-Context LLMs
Investigating Multi-task Pretraining and Generalization in Reinforcement Learning
Learning Color Equivariant Representations
The IMO Small Challenge: Not-Too-Hard Olympiad Math Datasets for LLMs
Adversarial Dropout Regularization
I-Con: A Unifying Framework for Representation Learning
Sequence Mixup for Zero-Shot Cross-Lingual Part-of-speech Tagging
Learning Diagrams: A Graphical Language for Compositional Training Regimes
Classification with Conceptual Safeguards
Unicom: Universal and Compact Representation Learning for Image Retrieval
Robustness Auditing for Linear Regression: To Singularity and Beyond
An Intelligent Agentic System for Complex Image Restoration Problems
In-Context Learning through the Bayesian Prism
Faster Discovery of Neural Architectures by Searching for Paths in a Large Model
Selective Frequency Network for Image Restoration
Rotation Has Two Sides: Evaluating Data Augmentation for Deep One-class Classification
PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences
PaLI: A Jointly-Scaled Multilingual Language-Image Model
Meta-learning Symmetries by Reparameterization
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Boosting Methods for Interval-censored Data with Regression and Classification
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations
Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting
Multivariate Time-series Imputation with Disentangled Temporal Representations
Transformer Fusion with Optimal Transport
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis
Scaling the Convex Barrier with Active Sets
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning
Evidential Uncertainty and Diversity Guided Active Learning for Scene Graph Generation
Preventing Posterior Collapse with delta-VAEs
Optimal Sample Complexity of Contrastive Learning
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
Neural networks with late-phase weights
Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks
Sampling-Free Learning of Bayesian Quantized Neural Networks
Approximation and non-parametric estimation of functions over high-dimensional spheres via deep ReLU networks
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator
FreeLB: Enhanced Adversarial Training for Natural Language Understanding
G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
Gradual Domain Adaptation via Gradient Flow
ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference
Analyzing Inverse Problems with Invertible Neural Networks
BackPACK: Packing more into Backprop
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
A two-parameter learnable Logmoid Activation Unit
Fusing 3D-CNN and lightweight Swin Transformer networks for HSI
Group Distributionally Robust Dataset Distillation with Risk Minimization
Mutual Information State Intrinsic Control
SGD on Random Mixtures: Private Machine Learning under Data Breach Threats
Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation
A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies
Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization
On Evaluation Metrics for Graph Generative Models
Image-guided Neural Object Rendering
Neural Fine-Tuning Search for Few-Shot Learning
UC-NERF: Neural Radiance Field for Under-Calibrated Multi-View Cameras in Autonomous Driving
Minimally Redundant Laplacian Eigenmaps
Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations
BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Learning the Arrow of Time for Problems in Reinforcement Learning
Meta-Learning without Memorization
Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
Out-of-distribution Representation Learning for Time Series Classification
Solving Homogeneous and Heterogeneous Cooperative Tasks with Greedy Sequential Execution
Learning Large DAGs is Harder than you Think: Many Losses are Minimal for the Wrong DAG
PEAR: Primitive Enabled Adaptive Relabeling for Boosting Hierarchical Reinforcement Learning
Bias Mitigation in Graph Diffusion Models
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
Denoising Diffusion Error Correction Codes
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning
Range, not Independence, Drives Modularity in Biologically Inspired Representations
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics
EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Fix your classifier: the marginal value of training the last weight layer
GenDICE: Generalized Offline Estimation of Stationary Values
Variational Reference Priors
Understanding and Enhancing the Transferability of Jailbreaking Attacks
Pitfalls of Gaussians as a noise distribution in NCE
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
Do We Need Anisotropic Graph Neural Networks?
Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
GDrag: Towards General-Purpose Interactive Editing with Anti-ambiguity Point Diffusion
TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval