yusuke1997/dblp-id-title huggingface dataset:
Logical Message Passing Networks with One-hop Inference on Atomic Formulas
Theory, Analysis, and Best Practices for Sigmoid Self-Attention
Meta Knowledge Condensation for Federated Learning
Approximate Bayesian Inference with Stein Functional Variational Gradient Descent
On Universal Equivariant Set Networks
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Mini-Monkey: Alleviating the Semantic Sawtooth Effect for Lightweight MLLMs via Complementary Image Pyramid
MaskFusion: Feature Augmentation for Click-Through Rate Prediction via Input-adaptive Mask Fusion
Editable Neural Networks
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
Logical Languages Accepted by Transformer Encoders with Hard Attention
MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs
MP-Mat: A 3D-and-Instance-Aware Human Matting and Editing Framework with Multiplane Representation
Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness
Semantic feature verification in FLAN-T5
Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning
Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems
Causality Compensated Attention for Contextual Biased Visual Recognition
Learnable Embedding Space for Efficient Neural Architecture Compression
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer
Dynamics-Aware Comparison of Learned Reward Functions
Rethinking Visual Counterfactual Explanations Through Region Constraint
Towards Faster Global Convergence of Robust Policy Gradient Methods
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
Periodic Materials Generation using Text-Guided Joint Diffusion Model
InstructPix2NeRF: Instructed 3D Portrait Editing from a Single Image
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
Convergence of Gradient Methods on Bilinear Zero-Sum Games
Learning to Explore and Exploit with GNNs for Unsupervised Combinatorial Optimization
PCMC-Net: Feature-based Pairwise Choice Markov Chains
Graph Coarsening with Neural Networks
On the Modeling Capabilities of Large Language Models for Sequential Decision Making
CoInD: Enabling Logical Compositions in Diffusion Models
Uncertainty Estimation in Autoregressive Structured Prediction
CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models
Finding the Global Semantic Representation in GAN through Fréchet Mean
LongVILA: Scaling Long-Context Visual Language Models for Long Videos
L2-Nonexpansive Neural Networks
Pre-Tokenization of Numbers for Large Language Models
P2Seg: Pointly-supervised Segmentation via Mutual Distillation
Circuit Transformer: A Transformer That Preserves Logical Equivalence
On the Role of Neural Collapse in Transfer Learning
Optimal Binary Autoencoding with Pairwise Correlations
MEDICAL IMAGE UNDERSTANDING WITH PRETRAINED VISION LANGUAGE MODELS: A COMPREHENSIVE STUDY
Lightweight Predictive 3D Gaussian Splats
What's the Move? Hybrid Imitation Learning via Salient Points
Zero Pixel Directional Boundary by Vector Transform
The Directionality of Optimization Trajectories in Neural Networks
Image Translation as Diffusion Visual Programmers
Accurate Retraining-free Pruning for Pretrained Encoder-based Language Models
Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency
Generative Pseudo-Inverse Memory
FROSTER: Frozen CLIP is A Strong Teacher for Open-Vocabulary Action Recognition
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent
Properties from mechanisms: an equivariance perspective on identifiable representation learning
Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers
A Solvable Attention for Neural Scaling Laws
BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking
Faster Sampling from Log-Concave Densities over Polytopes via Efficient Linear Solvers
Deep probabilistic subsampling for task-adaptive compressed sensing
Scaling Optimal LR Across Token Horizons
Representative Guidance: Diffusion Model Sampling with Coherence
A Message Passing Perspective on Learning Dynamics of Contrastive Learning
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Fast Regression for Structured Inputs
Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization
DeFINE: Deep Factorized Input Token Embeddings for Neural Sequence Modeling
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy
Aligning Human Motion Generation with Human Perceptions
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision
JetFormer: An autoregressive generative model of raw images and text
Online Ad Hoc Teamwork under Partial Observability
De Novo Molecular Generation via Connection-aware Motif Mining
Learning Hyperbolic Representations of Topological Features
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models
GAIA: Zero-shot Talking Avatar Generation
Implicit Search via Discrete Diffusion: A Study on Chess
QPLEX: Duplex Dueling Multi-Agent Q-Learning
Accelerating Neural Architecture Search using Performance Prediction
Any-scale Balanced Samplers for Discrete Space
Neural Structured Prediction for Inductive Node Classification
Training Bayesian Neural Networks with Sparse Subspace Variational Inference
Provably Efficient UCB-type Algorithms For Learning Predictive State Representations
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
Learning the Complexity of Weakly Noisy Quantum States
Residual Energy-Based Models for Text Generation
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Imputation for prediction: beware of diminishing returns
On Diffusion Modeling for Anomaly Detection
Bispectral Neural Networks
Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM
Knowledge Graph Unlearning to Defend Language Model Against Jailbreak Attack
Curriculum reinforcement learning for quantum architecture search under hardware errors
OPTAMI: Global Superlinear Convergence of High-order Methods
Discovering Informative and Robust Positives for Video Domain Adaptation
Volumetric Optimal Transportation by Fast Fourier Transform
Listen, Think, and Understand
Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations
Make-A-Video: Text-to-Video Generation without Text-Video Data
Recurrent Mixture Density Network for Spatiotemporal Visual Attention
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
Stiffness-aware neural network for learning Hamiltonian systems
Convex Regularization behind Neural Reconstruction
G2N2 : Weisfeiler and Lehman go grammatical
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training
DCN+: Mixed Objective And Deep Residual Coattention for Question Answering
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
Autoregressive Quantile Flows for Predictive Uncertainty Estimation
AANG : Automating Auxiliary Learning
Vid2Game: Controllable Characters Extracted from Real-World Videos
Sign Bits Are All You Need for Black-Box Attacks
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion
Graph Information Bottleneck for Subgraph Recognition
Modeling Complex System Dynamics with Flow Matching Across Time and Conditions
BC-IRL: Learning Generalizable Reward Functions from Demonstrations
Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Sobolev GAN
Inner Information Analysis Algorithm for Deep Neural Network based on Community
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
Explanations of Black-Box Models based on Directional Feature Interactions
Cycle Consistency Driven Object Discovery
Learning to Recombine and Resample Data For Compositional Generalization
CO-MOT: Boosting End-to-end Transformer-based Multi-Object Tracking via Coopetition Label Assignment and Shadow Sets
Neural Spacetimes for DAG Representation Learning
Provably Accurate Shapley Value Estimation via Leverage Score Sampling
Learning Versatile Neural Architectures by Propagating Network Codes
DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing
A robust differential Neural ODE Optimizer
U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models
MindSimulator: Exploring Brain Concept Localization via Synthetic fMRI
Learning to Navigate in Complex Environments
Balancing Act: Constraining Disparate Impact in Sparse Models
Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation
Residual Non-local Attention Networks for Image Restoration
Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception
StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces
Generative Sliced MMD Flows with Riesz Kernels
The Close Relationship Between Contrastive Learning and Meta-Learning
VL-Cache: Sparsity and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration
Accelerating Diffusion Transformers with Token-wise Feature Caching
The Reasonableness Behind Unreasonable Translation Capability of Large Language Model
Tensor Programs VI: Feature Learning in Infinite Depth Neural Networks
A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals
Image Watermarks are Removable using Controllable Regeneration from Clean Noise
Affine Steerable Equivariant Layer for Canonicalization of Neural Networks
Deep Learning From Crowdsourced Labels: Coupled Cross-Entropy Minimization, Identifiability, and Regularization
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice
Shh, don't say that! Domain Certification in LLMs
Weak to Strong Generalization for Large Language Models with Multi-capabilities
Depth-Adaptive Transformer
On the Global Convergence of Training Deep Linear ResNets
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains
Conditional Testing based on Localized Conformal p-values
Fast Nonlinear Vector Quantile Regression
Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching: With Insights into Other Permutation Search Methods
Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech
Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning
Extremely Simple Activation Shaping for Out-of-Distribution Detection
GraphSAINT: Graph Sampling Based Inductive Learning Method
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning
Skill-based Meta-Reinforcement Learning
Weighted Branch Aggregation Based Deep Learning Model for Track Detection in Autonomous Racing
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation
Language-biased image classification: evaluation based on semantic representations
On Disentangled Training for Nonlinear Transform in Learned Image Compression
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
Learning sparse relational transition models
Backdoor Defense via Decoupling the Training Process
Constructive TT-representation of the tensors given as index interaction functions with applications
Contrastive Audio-Visual Masked Autoencoder
Forward Learning of Graph Neural Networks
Semi-supervised Learning under Self-training via $f$-Divergence
Behavioral Cloning from Noisy Demonstrations
DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding
On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning
Decoupled Adaptation for Cross-Domain Object Detection
A Deep Generative Learning Approach for Two-stage Adaptive Robust Optimization
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction Following
Improving Policy Gradient by Exploring Under-appreciated Rewards
Policy Gradient with Tree Search (PGTS) in Reinforcement Learning Evades Local Maxima
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play
On the Performance of Temporal Difference Learning With Neural Networks
Don't Decay the Learning Rate, Increase the Batch Size
Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration
Causal Covariate Shift Correction using Fisher information penalty
DFlow: Learning to Synthesize Better Optical Flow Datasets via a Differentiable Pipeline
Finite Scalar Quantization: VQ-VAE Made Simple
Non-negative Contrastive Learning
Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
DeeperForward: Enhanced Forward-Forward Training for Deeper and Better Performance
Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage
MaskViT: Masked Visual Pre-Training for Video Prediction
Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias
TempCLR: Temporal Alignment Representation with Contrastive Learning
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
Learning what you can do before doing anything
Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning
Alchemy: Amplifying Theorem-Proving Capability Through Symbolic Mutation
A VAE for Transformers with Nonparametric Variational Information Bottleneck
Generalized Multimodal ELBO
Revisiting the Entropy Semiring for Neural Speech Recognition
Coverage-centric Coreset Selection for High Pruning Rates
Seeing in Words: Learning to Classify through Language Bottlenecks
Arbitrary Virtual Try-on Network: Characteristics Representation and Trade-off between Body and Clothing
Optimal Sample Complexity for Average Reward Markov Decision Processes
Disentangling Learning Representations with Density Estimation
Long-Sequence Recommendation Models Need Decoupled Embeddings
Exploring Effective Stimulus Encoding via Vision System Modeling for Visual Prostheses
NBDT: Neural-Backed Decision Tree
Video Decomposition Prior: Editing Videos Layer by Layer
Multi-objective Optimization by Learning Space Partition
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
Lagging Inference Networks and Posterior Collapse in Variational Autoencoders
Self-Supervised Heterogeneous Graph Learning: a Homophily and Heterogeneity View
Treeformer: Dense Gradient Trees for Efficient Attention Computation
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
On feature learning in neural networks with global convergence guarantees
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
Semi-Parametric Retrieval via Binary Bag-of-Tokens Index
BiBERT: Accurate Fully Binarized BERT
GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification
DySR: Adaptive Super-Resolution via Algorithm and System Co-design
Geometry-Aware Gradient Algorithms for Neural Architecture Search
Expected Sliced Transport Plans
Cut out the annotator, keep the cutout: better segmentation with weak supervision
REFINE: Inversion-Free Backdoor Defense via Model Reprogramming
BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL
GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training
T-Rep: Representation Learning for Time Series using Time-Embeddings
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control
Leveraging Optimization for Adaptive Attacks on Image Watermarks
Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models
CipherPrune: Efficient and Scalable Private Transformer Inference
MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos
Adaptive Federated Optimization
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Calibrating Expressions of Certainty
BANDIT SAMPLING FOR FASTER NEURAL NETWORK TRAINING WITH SGD
Efficient Cross-Episode Meta-RL
Traveling Waves Encode The Recent Past and Enhance Sequence Learning
Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models
The Neural Noisy Channel
Programming With a Differentiable Forth Interpreter
Evaluating The Search Phase of Neural Architecture Search
Latent State Marginalization as a Low-cost Approach for Improving Exploration
TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation
Improved Convergence Rate for Diffusion Probabilistic Models
Black-box Attacks on Deep Neural Networks via Gradient Estimation
An Additive Instance-Wise Approach to Multi-class Model Interpretation
Towards neural networks that provably know when they don't know
When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks
Diffusion Posterior Sampling for Linear Inverse Problem Solving: A Filtering Perspective
Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
InstructDET: Diversifying Referring Object Detection with Generalized Instructions
Towards Establishing Guaranteed Error for Learned Database Operations
Learning Actionable Representations with Goal Conditioned Policies
Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin
Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks
Loss-aware Binarization of Deep Networks
LoLCATs: On Low-Rank Linearizing of Large Language Models
PMI-Masking: Principled masking of correlated spans
Learning differentiable solvers for systems with hard constraints
API Pack: A Massive Multi-Programming Language Dataset for API Call Generation
LOQA: Learning with Opponent Q-Learning Awareness
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression
CBMA: Improving Conformal Prediction through Bayesian Model Averaging
TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning
Group Equivariant Conditional Neural Processes
A Variational Perspective on Solving Inverse Problems with Diffusion Models
Prediction Error-based Classification for Class-Incremental Learning
Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks
Constraining Representations Yields Models That Know What They Don't Know
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels
On the self-verification limitations of large language models on reasoning and planning tasks
GANSynth: Adversarial Neural Audio Synthesis
Short-Term Memory Convolutions
ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation
Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models
The Human-AI Substitution game: active learning from a strategic labeler
CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
A Simple Approach for Visual Room Rearrangement: 3D Mapping and Semantic Search
On the Learning and Learnability of Quasimetrics
Sequential Gradient Coding For Straggler Mitigation
Elastic Feature Consolidation For Cold Start Exemplar-Free Incremental Learning
Gap-Aware Mitigation of Gradient Staleness
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Random Laplacian Features for Learning with Hyperbolic Space
Exploring channel distinguishability in local neighborhoods of the model space in quantum neural networks
Not All Tasks Are Born Equal: Understanding Zero-Shot Generalization
OptionZero: Planning with Learned Options
AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
A Mutual Information Maximization Perspective of Language Representation Learning
Mm-Embed: Universal Multimodal Retrieval with Multimodal LLMS
Contemplating Real-World Object Classification
Prompt Learning with Quaternion Networks
Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation
Representational Continuity for Unsupervised Continual Learning
Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
Ensuring DNN Solution Feasibility for Optimization Problems with Linear Constraints
Learning How Hard to Think: Input-Adaptive Allocation of LM Computation
Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Model
Towards Understanding the Robustness Against Evasion Attack on Categorical Data
Teacher Guided Training: An Efficient Framework for Knowledge Transfer
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model
Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning
Credit-based self organizing maps: training deep topographic networks with minimal performance degradation
Key Patch Proposer: Key Patches Contain Rich Information
Self-Normalized Resets for Plasticity in Continual Learning
Pareto Policy Adaptation
AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text
Control-oriented Clustering of Visual Latent Representation
Tighter bounds lead to improved classifiers
Out-of-distribution Generalization for Total Variation based Invariant Risk Minimization
A Neural Tangent Kernel Perspective of Infinite Tree Ensembles
NextBestPath: Efficient 3D Mapping of Unseen Environments
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission
Understanding Trainable Sparse Coding with Matrix Factorization
A Large-scale Training Paradigm for Graph Generative Models
Fast training and sampling of Restricted Boltzmann Machines