yusuke1997/dblp-id-title huggingface dataset:
CheapNet: Cross-attention on Hierarchical representations for Efficient protein-ligand binding Affinity Prediction
Feature-Based Metrics for Exploring the Latent Space of Generative Models
Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning
Beyond Worst-Case Dimensionality Reduction for Sparse Vectors
Bridging State and History Representations: Understanding Self-Predictive RL
Learning a Latent Simplex in Input Sparsity Time
Variance Networks: When Expectation Does Not Meet Your Expectations
Neuron-Enhanced AutoEncoder Matrix Completion and Collaborative Filtering: Theory and Practice
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks
Iterative Empirical Game Solving via Single Policy Best Response
The "Law" of the Unconscious Contrastive Learner: Probabilistic Alignment of Unpaired Modalities
NRGBoost: Energy-Based Generative Boosted Trees
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs
SoftCVI: Contrastive variational inference with self-generated soft labels
On amortizing convex conjugates for optimal transport
Adversarial Training Methods for Semi-Supervised Text Classification
Learning Differentially Private Recurrent Language Models
Automatic Loss Function Search for Predict-Then-Optimize Problems with Strong Ranking Property
AdaWM: Adaptive World Model based Planning for Autonomous Driving
Gated Delta Networks: Improving Mamba2 with Delta Rule
Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps
Cost-Sensitive Robustness against Adversarial Examples
Curriculum learning as a tool to uncover learning principles in the brain
Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization
Multimodal Federated Learning via Contrastive Representation Ensemble
Enhancing Adversarial Defense by k-Winners-Take-All
Efficient Biological Data Acquisition through Inference Set Design
P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Efficient Inference of Flexible Interaction in Spiking-neuron Networks
Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?
Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models
SecureGS: Boosting the Security and Fidelity of 3D Gaussian Splatting Steganography
Multilingual Alignment of Contextual Word Representations
MEMO: A Deep Network for Flexible Combination of Episodic Memories
Deep Frank-Wolfe For Neural Network Optimization
Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization
Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent
Efficient Multi-agent Offline Coordination via Diffusion-based Trajectory Stitching
SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Model
Shifting Mean Activation Towards Zero with Bipolar Activation Functions
Language Modelling with Pixels
Gaussian Mixture Counterfactual Generator
SegLLM: Multi-round Reasoning Segmentation with Large Language Models
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms
Vision and Language Synergy for Rehearsal Free Continual Learning
GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS
Efficient Inverse Multiagent Learning
Partial Label Unsupervised Domain Adaptation with Class-Prototype Alignment
Adversarial Imitation Learning with Preferences
Scaling Autonomous Agents via Automatic Reward Modeling And Planning
On Evaluating the Durability of Safeguards for Open-Weight LLMs
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
Adaptive Rank Allocation: Speeding Up Modern Transformers with RaNA Adapters
Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting
Towards Faster Decentralized Stochastic Optimization with Communication Compression
Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts
Deep Learning with Sets and Point Clouds
On the Importance of Firth Bias Reduction in Few-Shot Classification
Improving Neural Optimal Transport via Displacement Interpolation
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement
Influence Functions for Scalable Data Attribution in Diffusion Models
I-PHYRE: Interactive Physical Reasoning
Learning 3D Perception from Others' Predictions
PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
Pay Less Attention with Lightweight and Dynamic Convolutions
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences
TLDR: Token-Level Detective Reward Model for Large Vision Language Models
Scaling Laws for Downstream Task Performance in Machine Translation
Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis
Advancing Prompt-Based Methods for Replay-Independent General Continual Learning
Differentiable Causal Discovery for Latent Hierarchical Causal Models
Unsupervised Learning for Anomaly Detection: A Comparison of Deep Generative Models
Enhancing Drug-Drug Interaction Prediction with Context-Aware Architecture
Handling unstructured data for operator learning using implicit neural representations
Progressive Memory Banks for Incremental Domain Adaptation
BEiT: BERT Pre-Training of Image Transformers
Provably convergent quasistatic dynamics for mean-field two-player zero-sum games
Quasi-Monte Carlo for 3D Sliced Wasserstein
σ-zero: Gradient-based Optimization of ℓ0-norm Adversarial Examples
Self-supervised Adversarial Robustness for the Low-label, High-data Regime
Zoology: Measuring and Improving Recall in Efficient Language Models
Meta-Learning For Stochastic Gradient MCMC
Neuron based Personality Trait Induction in Large Language Models
TabFact: A Large-scale Dataset for Table-based Fact Verification
Online Adversarial Attacks
Learning Energy Decompositions for Partial Inference in GFlowNets
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning
MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models
A Critique of Self-Expressive Deep Subspace Clustering
Structured Prediction using cGANs with Fusion Discriminator
Fully Hyperbolic Convolutional Neural Networks for Computer Vision
At Which Training Stage Does Code Data Help LLMs Reasoning?
A Statistical Framework for Ranking LLM-based Chatbots
How to Train Your MAML to Excel in Few-Shot Classification
VeRA: Vector-based Random Matrix Adaptation
Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
Universal Approximation Under Constraints is Possible with Transformers
Learning Accurate Entropy Model with Global Reference for Image Compression
Reinterpreting Importance-Weighted Autoencoders
Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies
When and Why Vision-Language Models Behave like Bags-Of-Words, and What to Do About It?
PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training
Never Train from Scratch: Fair Comparison of Long-Sequence Models Requires Data-Driven Priors
Fourier Head: Helping Large Language Models Learn Complex Probability Distributions
Topograph: An Efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation
Finding Flatter Minima with SGD
Iterative weakly supervised learning for novel class object detection
Towards Inferential Reproducibility of Machine Learning Research
Generalizing Few-Shot NAS with Gradient Matching
Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection
Learning Interactive Real-World Simulators
Data Unlearning in Diffusion Models
Scaling up Masked Diffusion Models on Text
GROOT-2: Weakly Supervised Multimodal Instruction Following Agents
CoPhy: Counterfactual Learning of Physical Dynamics
GeneDAE: A Sparse Denoising Autoencoder for Deriving Interpretable Gene Embeddings
Salvage: Shapley-distribution Approximation Learning Via Attribution Guided Exploration for Explainable Image Classification
Memory-Consistent Neural Networks for Imitation Learning
Learning to Represent Programs with Graphs
AugKD: Ingenious Augmentations Empower Knowledge Distillation for Image Super-Resolution
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
IntSGD: Adaptive Floatless Compression of Stochastic Gradients
FastSHAP: Real-Time Shapley Value Estimation
Union-over-Intersections: Object Detection beyond Winner-Takes-All
Deep Graph Matching Consensus
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios
On the Hölder Stability of Multiset and Graph Neural Networks
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models
GMValuator: Similarity-based Data Valuation for Generative Models
Learning Causal Alignment for Reliable Disease Diagnosis
DreamFlow: High-quality text-to-3D generation by Approximating Probability Flow
Powderworld: A Platform for Understanding Generalization via Rich Task Distributions
Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models
Mixture of Attentions For Speculative Decoding
Image Clustering Conditioned on Text Criteria
Learning Robust Representations via Multi-View Information Bottleneck
Scalable Diffusion for Materials Generation
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
Mean Field Theory in Deep Metric Learning
Neural Synthesis of Binaural Speech From Mono Audio
Exposing Text-Image Inconsistency Using Diffusion Models
Dynamic Tensor Rematerialization
General Neural Gauge Fields
BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations
Online Hyperparameter Meta-Learning with Hypergradient Distillation
Consolidator: Mergable Adapter with Group Connections for Visual Adaptation
Auto-Encoding Goodness of Fit
Shadow Cones: A Generalized Framework for Partial Order Embeddings
Flow Annealed Importance Sampling Bootstrap
Quantifying and Mitigating the Impact of Label Errors on Model Disparity Metrics
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models
Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search
Distribution-Specific Agnostic Conditional Classification With Halfspaces
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
Exploiting Hidden Symmetry to Improve Objective Perturbation for DP Linear Learners with a Nonsmooth L1-Norm
Spa-Bench: a comprehensive Benchmark for Smartphone Agent Evaluation
Relative Behavioral Attributes: Filling the Gap between Symbolic Goal Specification and Reward Learning from Human Preferences
CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets
Efficient Augmentation via Data Subsampling
Going Beyond Static: Understanding Shifts with Time-Series Attribution
Rethinking Invariance in In-context Learning
Partitioning Message Passing for Graph Fraud Detection
Towards Resolving the Implicit Bias of Gradient Descent for Matrix Factorization: Greedy Low-Rank Learning
Efficient Certified Training and Robustness Verification of Neural ODEs
Reasoning of Large Language Models over Knowledge Graphs with Super-Relations
Mitigating Dataset Bias by Using Per-Sample Gradient
Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning
Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study
Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Robust Graph Dictionary Learning
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
ImProver: Agent-Based Automated Proof Optimization
An interpretable error correction method for enhancing code-to-code translation
GSBAK: top-K Geometric Score-based Black-box Attack
Delta: Degradation-Free Fully Test-Time Adaptation
Semi-Supervised Learning With GANs: Revisiting Manifold Regularization
New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions
Generative Flows on Synthetic Pathway for Drug Design
POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding
Language-Assisted Feature Transformation for Anomaly Detection
A Discriminative Gaussian Mixture Model with Sparsity
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
Deep Attentive Variational Inference
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck
Learning Altruistic Behaviours in Reinforcement Learning without External Rewards
Object centric architectures enable efficient causal representation learning
Enhancing Transferable Adversarial Attacks on Vision Transformers through Gradient Normalization Scaling and High-Frequency Adaptation
Multimodal Molecular Pretraining via Modality Blending
Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective
Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game
Can We Talk Models Into Seeing the World Differently?
The Value of Sensory Information to a Robot
Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning
Strong Model Collapse
SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning
RLIF: Interactive Imitation Learning as Reinforcement Learning
When Spiking Neural Networks Meet Temporal Attention Image Decoding and Adaptive Spiking Neuron
FVD: A new Metric for Video Generation
SMITE: Segment Me In TimE
Edge Prompt Tuning for Graph Neural Networks
SMASH: One-Shot Model Architecture Search through HyperNetworks
Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents
Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
Deep Convolutional Malware Classifiers Can Learn from Raw Executables and Labels Only
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning
Tool-Planner: Task Planning with Clusters across Multiple Tools
Share or Not? Learning to Schedule Language-Specific Capacity for Multilingual Translation
SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding
Revisiting flow generative models for Out-of-distribution detection
Task Relatedness-Based Generalization Bounds for Meta Learning
Recon: Reducing Conflicting Gradients From the Root For Multi-Task Learning
A Simple, Fast Algorithm for Continual Learning from High-Dimensional Data
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
Invariance-based Learning of Latent Dynamics
Hierarchical Few-Shot Imitation with Skill Transition Models
Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks
DataGen: Unified Synthetic Dataset Generation via Large Language Models
Entropy-aided prompt Federated learning
Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escape, and Network Embedding
Trellis Networks for Sequence Modeling
Vision Language Models are In-Context Value Learners
Sampling with Mirrored Stein Operators
Learning Robust Representations with Long-Term Information for Generalization in Visual Reinforcement Learning
Adversarially Robust Anomaly Detection through Spurious Negative Pair Mitigation
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics
A theoretical study of inductive biases in contrastive learning
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
Learning Partial Graph Matching via Optimal Partial Transport
RandLoRA: Full rank parameter-efficient fine-tuning of large models
How to prepare your task head for finetuning
BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models
Neural Approximate Sufficient Statistics for Implicit Models
TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice
Enhancing Fairness in In-Context Learning: Prioritizing Minority Samples in Demonstrations
GeValDi: Generative Validation of Discriminative Models
Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Power
Meta Learning Shared Hierarchies
MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models
An Engorgio Prompt Makes Large Language Model Babble on
Pruning Filters for Efficient ConvNets
gDDIM: Generalized denoising diffusion implicit models
ODAM: Gradient-based Instance-Specific Visual Explanations for Object Detection
Online Bias Correction for Task-Free Continual Learning
Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information
A Control-Centric Benchmark for Video Prediction
Explaining Kernel Clustering via Decision Trees
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks
Centrality-guided Pre-training for Graph
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking
VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning
LevAttention: Time, Space and Streaming Efficient Algorithm for Heavy Attentions
Dropout Enhanced Bilevel Training
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative
Label-free Node Classification on Graphs with Large Language Models (LLMs)
Has the Deep Neural Network learned the Stochastic Process? An Evaluation Viewpoint
Focus on the Common Good: Group Distributional Robustness Follows
Hierarchical Autoregressive Modeling for Neural Video Compression
Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs
Progress measures for grokking via mechanistic interpretability
Natural Language Descriptions of Deep Visual Features
Learning to Decompose Visual Features with Latent Textual Prompts
Generalizing Reasoning Problems to Longer Lengths
Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation
Systematic Generalization: What Is Required and Can It Be Learned?
Know Your Action Set: Learning Action Relations for Reinforcement Learning
Energy-Inspired Molecular Conformation Optimization
Rethinking skip connection model as a learnable Markov chain
Fast and Accurate Text Classification: Skimming, Rereading and Early Stopping
αTC-VAE: On the relationship between Disentanglement and Diversity
Smooth Loss Functions for Deep Top-k Classification
Self-ensembling for visual domain adaptation
Autonomous Reinforcement Learning: Formalism and Benchmarking
Career Path Modeling and Recommendations with Linkedin Career Data and Predicted Salary Estimations
Better autoregressive regression with LLMs via regression-aware fine-tuning
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction
Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation
Physics-Informed Diffusion Models
Competitive experience replay
PhoWhisper: Automatic Speech Recognition for Vietnamese
Hadamrnn: Binary and Sparse Ternary orthogonal RNNs
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
Isotropy in the Contextual Embedding Space: Clusters and Manifolds
Possibility Before Utility: Learning And Using Hierarchical Affordances
Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping
Object-Centric Learning with Slot Mixture Module
Towards Deep Interpretability (MUS-ROVER II): Learning Hierarchical Representations of Tonal Music
Expected Return Symmetries
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation
ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Self-Supervised Category-Level Articulated Object Pose Estimation with Part-Level SE(3) Equivariance
Information Prioritization through Empowerment in Visual Model-based RL
Learning to Make Decisions via Submodular Regularization
Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling
Personalized Federated Learning for Medical Segmentation using Hypernetworks
Hybrid Local SGD for Federated Learning with Heterogeneous Communications
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
Exploratory Analysis of Scholarly Publications on Artificial Intelligence (AI) in Colonoscopy using Litstudy
VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis
Neural Field Classifiers via Target Encoding and Classification Loss
MotionClone: Training-Free Motion Cloning for Controllable Video Generation
No Need to Talk: Asynchronous Mixture of Language Models
Lagrangian Flow Networks for Conservation Laws
Transformers can optimally learn regression mixture models
OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data
From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs
Dynamic Layer Tying for Parameter-Efficient Transformers
Rethinking Reward Modeling in Preference-based Large Language Model Alignment
SSOLE: Rethinking Orthogonal Low-rank Embedding for Self-Supervised Learning
Training on the Test Task Confounds Evaluation and Emergence
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL
Multi-Reward as Condition for Instruction-based Image Editing
Budgeted Training for Vision Transformer
TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks
A Transfer Attack to Image Watermarks
Accelerating SGD for Distributed Deep-Learning Using an Approximted Hessian Matrix
Efficient Imitation under Misspecification
Extending Mercer's expansion to indefinite and asymmetric kernels
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection
miniF2F: a cross-system benchmark for formal Olympiad-level mathematics
Learning from Demonstration with Weakly Supervised Disentanglement
Unsupervised Neural Machine Translation
Online Preference Alignment for Language Models via Count-based Exploration
Differentially Private Learning Needs Better Features (or Much More Data)
DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator