yusuke1997/dblp-id-title huggingface dataset:
Non-Linear Operator Approximations for Initial Value Problems
When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers
Learning High-Degree Parities: The Crucial Role of the Initialization
Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention
CircuitNet 2.0: An Advanced Dataset for Promoting Machine Learning Innovations in Realistic Chip Design Environment
Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging
On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding
Improving the Accuracy of Learning Example Weights for Imbalance Classification
Improving Large Language Model Planning with Action Sequence Similarity
Is a Caption Worth a Thousand Images? A Study on Representation Learning
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning
ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering
NetInfoF Framework: Measuring and Exploiting Network Usable Information
Searching for Optimal Solutions with LLMs via Bayesian Optimization
On the Existence of Universal Lottery Tickets
Stick-Breaking Variational Autoencoders
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
Open-Set Graph Anomaly Detection via Normal Structure Regularisation
Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
Bayesian Image Regression with Soft-thresholded Conditional Autoregressive Prior
The Surprising Computational Power of Nondeterministic Stack RNNs
Mind the Gap: Offline Policy Optimization for Imperfect Rewards
Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations
Can Conformal Prediction Obtain Meaningful Safety Guarantees for ML Models?
Backdoor Contrastive Learning via Bi-level Trigger Optimization
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
On Predicting Generalization using GANs
Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models
BREEDS: Benchmarks for Subpopulation Shift
LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics
Perceptual Scales Predicted by Fisher Information Metrics
Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data
O(T -1 Convergence of Optimistic-Follow-the-Regularized-Leader in Two-Player Zero-Sum Markov Games
Stay Moral and Explore: Learn to Behave Morally in Text-based Games
Small Models are LLM Knowledge Triggers for Medical Tabular Prediction
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models
Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory
Adaptive Estimators Show Information Compression in Deep Neural Networks
Behaviour Distillation
Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
Generating High fidelity Images with subscale pixel Networks and Multidimensional Upscaling
Interpreting Emergent Planning in Model-Free Reinforcement Learning
Multi-level Residual Networks from Dynamical Systems View
Point2SSM: Learning Morphological Variations of Anatomies from Point Clouds
OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes
SpectralWords: Spectral Embeddings Approach to Word Similarity Task for Large Vocabularies
Training individually fair ML models with sensitive subspace robustness
Feature Kernel Distillation
SEMDICE: Off-policy State Entropy Maximization via Stationary Distribution Correction Estimation
Control-Aware Representations for Model-based Reinforcement Learning
CL-DiffPhyCon: Closed-loop Diffusion Control of Complex Physical Systems
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Are adversarial examples inevitable?
Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
Negative eigenvalues of the Hessian in deep neural networks
SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal
Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition
An Unforgeable Publicly Verifiable Watermark for Large Language Models
Towards Domain Adaptive Neural Contextual Bandits
Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View
Provable Memory Efficient Self-Play Algorithm for Model-free Reinforcement Learning
Linear Partial Gromov-Wasserstein Embedding
Sample Efficient Actor-Critic with Experience Replay
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment
Fidelity-based Deep Adiabatic Scheduling
MetaOOD: Automatic Selection of OOD Detection Models
REBIND: Enhancing Ground-state Molecular Conformation Prediction via Force-Based Graph Rewiring
Topologically Regularized Data Embeddings
DeLLMa: Decision Making Under Uncertainty with Large Language Models
Robust Conformal Prediction with a Single Binary Certificate
Improving Graph Neural Networks by Learning Continuous Edge Directions
Deep Confident Steps to New Pockets: Strategies for Docking Generalization
TiC-CLIP: Continual Training of CLIP Models
Experimenting with Multimodal AutoML: Detection and Evaluation of Alzheimer's Disease
Searching for Activation Functions
Graph Neural Networks Gone Hogwild
TOSS: High-quality Text-guided Novel View Synthesis from a Single Image
MALI: A memory efficient and reverse accurate integrator for Neural ODEs
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks
DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning
Learning What To Do by Simulating the Past
Provably Robust Conformal Prediction with Improved Efficiency
Guess & Sketch: Language Model Guided Transpilation
Trajectory Prediction using Equivariant Continuous Convolution
METRA: Scalable Unsupervised RL with Metric-Aware Abstraction
Time-to-Event Pretraining for 3D Medical Imaging
Deceptive Fairness Attacks on Graphs via Meta Learning
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs
Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models
Exploring Semantic Variations in GAN Latent Spaces via Matrix Factorization
Training-Free Dataset Pruning for Instance Segmentation
Does Progress On Object Recognition Benchmarks Improve Generalization on Crowdsourced, Global Data?
HELM: Hierarchical Encoding for mRNA Language Modeling
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds
DebGCD: Debiased Learning with Distribution Guidance for Generalized Category Discovery
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists
Relating transformers to models and neural representations of the hippocampal formation
Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration
Diff-Prompt: Diffusion-Driven Prompt Generator with Mask Supervision
On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning
SphereFace2: Binary Classification is All You Need for Deep Face Recognition
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic
Enhancing Meta Learning via Multi-Objective Soft Improvement Functions
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example
Variational Message Passing with Structured Inference Networks
Learning local equivariant representations for quantum operators
Learning to engineer protein flexibility
Differentially Private Steering for Large Language Model Alignment
Decoupled Subgraph Federated Learning
Beyond Circuit Connections: A Non-Message Passing Graph Transformer Approach for Quantum Error Mitigation
Rethinking Graph Neural Networks From A Geometric Perspective Of Node Features
Language Models are Advanced Anonymizers
AIM: Adapting Image Models for Efficient Video Action Recognition
SFESS: Score Function Estimators for k-Subset Sampling
Transformer-Squared: Self-adaptive LLMs
Triples as the Key: Structuring Makes Decomposition and Verification Easier in LLM-based TableQA
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
Improving Complex Reasoning with Dynamic Prompt Corruption: A Soft Prompt Optimization Approach
ConcreTizer: Model Inversion Attack via Occupancy Classification and Dispersion Control for 3D Point Cloud Restoration
Measuring Compositionality in Representation Learning
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach
Exploring Memorization in Adversarial Training
Why is SAM Robust to Label Noise?
A Linear Algebraic Framework for Counterfactual Generation
Bootstrapping Parallel Anchors for Relative Representations
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection
Robust Representation Consistency Model via Contrastive Denoising
Order-aware Interactive Segmentation
LongMamba: Enhancing Mamba's Long-Context Capabilities via Training-Free Receptive Field Enlargement
R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning
Backstepping Temporal Difference Learning
When SMILES have Language: Drug Classification using Text Classification Methods on Drug SMILES Strings
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
Towards Stable Test-time Adaptation in Dynamic Wild World
Paleo: A Performance Model for Deep Neural Networks
Analytic DAG Constraints for Differentiable DAG Learning
Gaussian-Based Instance-Adaptive Intensity Modeling for Point-Supervised Facial Expression Spotting
Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning
Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning
Function Space Particle Optimization for Bayesian Neural Networks
Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks
gradSim: Differentiable simulation for system identification and visuomotor control
PBADet: A One-Stage Anchor-Free Approach for Part-Body Association
Procedural Synthesis of Synthesizable Molecules
Learning Shape-Independent Transformation via Spherical Representations for Category-Level Object Pose Estimation
Graph Neural Ricci Flow: Evolving Feature from a Curvature Perspective
Real or Not Real, that is the Question
An Extensible Multi-modal Multi-task Object Dataset with Materials
NeuralPlane: Structured 3D Reconstruction in Planar Primitives with Neural Fields
Reliable Adversarial Distillation with Unreliable Teachers
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
GaussianAnything: Interactive Point Cloud Flow Matching for 3D Generation
MACHINE TRANSLATION BASELINES FOR ARABIC - SWAHILI
DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption
Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives
Rethinking CNN's Generalization to Backdoor Attack from Frequency Domain
Novel View Synthesis with Diffusion Models
Deep Kernel Posterior Learning under Infinite Variance Prior Weights
Learning Recurrent Representations for Hierarchical Behavior Modeling
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Attacking Binarized Neural Networks
Visual Agents as Fast and Slow Thinkers
Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Learn hybrid prototypes for multivariate time series anomaly detection
ST-GCond: Self-supervised and Transferable Graph Dataset Condensation
How Much is a Noisy Image Worth? Data Scaling Laws for Ambient Diffusion
MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses
Resolving Training Biases via Influence-based Data Relabeling
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data
Optimizing (L0, L1)-Smooth Functions by Gradient Methods
Probability Calibration for Knowledge Graph Embedding Models
Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport
An efficient encoder-decoder architecture with top-down attention for speech separation
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs
Lifelong Learning with Dynamically Expandable Networks
NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval
ContextRef: Evaluating Referenceless Metrics for Image Description Generation
Variational Intrinsic Control
A Gradient Flow Framework For Analyzing Network Pruning
Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions
Dynamic Sparse Training with Structured Sparsity
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation
ManyDG: Many-domain Generalization for Healthcare Applications
Transformers are Universal In-context Learners
simple_rl: Reproducible Reinforcement Learning in Python
Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers
Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?
On Achieving Optimal Adversarial Test Error
qNBO: quasi-Newton Meets Bilevel Optimization
NORM: Knowledge Distillation via N-to-One Representation Matching
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
Fine-tuning with Reserved Majority for Noise Reduction
Learning Self-Imitating Diverse Policies
Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses
Effective passive membership inference attacks in federated learning against overparameterized models
Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist
A Tight Convergence Analysis of Inexact Stochastic Proximal Point Algorithm for Stochastic Composite Optimization Problems
Training Large Language Models for Retrieval-Augmented Question Answering through Backtracking Correction
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Hierarchically Encapsulated Representation for Protocol Design in Self-Driving Labs
LayoutNUWA: Revealing the Hidden Layout Expertise of Large Language Models
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data
Regularization by Texts for Latent Diffusion Inverse Solvers
Continuous pseudo-labeling from the start
Efficient Continual Finite-Sum Minimization
Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies
RetroInText: A Multimodal Large Language Model Enhanced Framework for Retrosynthetic Planning via In-Context Representation Learning
Active Test-Time Adaptation: Theoretical Analyses and An Algorithm
Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images
Fat-to-Thin Policy Optimization: Offline Reinforcement Learning with Sparse Policies
Deberta: decoding-Enhanced Bert with Disentangled Attention
Depth Any Video with Scalable Synthetic Data
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Masked Frequency Modeling for Self-Supervised Visual Pre-Training
AutoBencher: Towards Declarative Benchmark Construction
Bag of Instances Aggregation Boosts Self-supervised Distillation
Compositional Task Representations for Large Language Models
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation
Automatically Composing Representation Transformations as a Means for Generalization
Learning Transferable Reward for Query Object Localization with Policy Adaptation
Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms
Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration
Matrix capsules with EM routing
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search
3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation
MUSE: Machine Unlearning Six-Way Evaluation for Language Models
Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHR Data
Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows
Guaranteed Generation from Large Language Models
Deep Biaffine Attention for Neural Dependency Parsing
Towards Generalization Bounds of GCNs for Adversarially Robust Node Classification
FedDAR: Federated Domain-Aware Representation Learning
A New Perspective on Shampoo's Preconditioner
Solving New Tasks by Adapting Internet Video Knowledge
Minimax Optimal Reinforcement Learning with Quasi-Optimism
Learning to Reject Meets Long-tail Learning
Course Correcting Koopman Representations
MLPs Learn In-Context on Regression and Classification Tasks
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View
Multi-Resolution Diffusion Models for Time Series Forecasting
COLLABORATIVE CONCEPT DRIFT DETECTION
DAVA: Disentangling Adversarial Variational Autoencoder
REEF: Representation Encoding Fingerprints for Large Language Models
Headless Language Models: Learning without Predicting with Contrastive Weight Tying
Streamlining Prediction in Bayesian Deep Learning
Tree Search-Based Policy Optimization under Stochastic Execution Delay
The adaptive complexity of parallelized log-concave sampling
Mechanistic Permutability: Match Features Across Layers
LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation models
DrNAS: Dirichlet Neural Architecture Search
ViLMA: A Zero-Shot Benchmark for Linguistic and Temporal Grounding in Video-Language Models
A Unified Framework for Soft Threshold Pruning
Transformers are Sample-Efficient World Models
Rapid Selection and Ordering of In-Context Demonstrations via Prompt Embedding Clustering
An Efficient Tester-Learner for Halfspaces
Sample Efficient Adaptive Text-to-Speech
Toward Understanding In-context vs. In-weight Learning
Real-time variational method for learning neural trajectory and its dynamics
Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis
SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation
LipsFormer: Introducing Lipschitz Continuity to Vision Transformers
Finite-State Autoregressive Entropy Coding for Efficient Learned Lossless Compression
Hybrid Regularization Improves Diffusion-based Inverse Problem Solving
AutoUAD: Hyper-parameter Optimization for Unsupervised Anomaly Detection
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models
Sparse Dictionary Learning by Dynamical Neural Networks
Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
Cycle to Clique (Cy2C) Graph Neural Network: A Sight to See beyond Neighborhood Aggregation
MoPro: Webly Supervised Learning with Momentum Prototypes
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving
Grounding Multimodal Large Language Model in GUI World
A Study of State Aliasing in Structured Prediction with RNNs
LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation
Duoduo CLIP: Efficient 3D Understanding with Multi-View Images
Revolutionizing EMCCD Denoising through a Novel Physics-Based Learning Framework for Noise Modeling
Sharpness-aware Minimization for Efficiently Improving Generalization
Learn to Explain Efficiently via Neural Logic Inductive Learning
TopoDiffusionNet: A Topology-aware Diffusion Model
Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step
ControlVideo: Training-free Controllable Text-to-video Generation
Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models
L2P-MIP: Learning to Presolve for Mixed Integer Programming
Semantic Flow: Learning Semantic Fields of Dynamic Scenes from Monocular Videos
Error-quantified Conformal Inference for Time Series
Learning Equivariant Non-Local Electron Density Functionals
Graph Edit Networks
Riemannian Adaptive Optimization Methods
Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment
Decoupled Training for Long-Tailed Classification With Stochastic Representations
6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Workshop Track Proceedings
Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning
Learning A Minimax Optimizer: A Pilot Study
Limitless Stability for Graph Convolutional Networks
Neural Deep Equilibrium Solvers
Multi-Scale Fusion for Object Representation
Discriminative Similarity for Data Clustering
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
Adaptive Retention & Correction: Test-Time Training for Continual Learning
R-GAP: Recursive Gradient Attack on Privacy
Actor-critic is implicitly biased towards high entropy optimal policies
Empirical Study on Updating Key-Value Memories in Transformer Feed-forward Layers
I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength
Inverse decision-making using neural amortized Bayesian actors
INVASE: Instance-wise Variable Selection using Neural Networks
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration
Mining your own secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models
Domain constraints improve risk prediction when outcome data is missing
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
Gumbel Counterfactual Generation From Language Models
Sleep Deprivation in the Forward-Forward Algorithm
FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning
AN ENSEMBLE LEARNING FRAMEWORK FOR VISIBILITY PREDICTION IN INDO-GANGETIC REGION
Rethinking the Role of Gradient-based Attribution Methods for Model Interpretability
Robust training with ensemble consensus
On Robust Prefix-Tuning for Text Classification
LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
Accelerated training through iterative gradient propagation along the residual path
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-Based Decision-Making Systems
Efficiently Controlling Multiple Risks with Pareto Testing
Logic-Logit: A Logic-Based Approach to Choice Modeling
Agent S: An Open Agentic Framework that Uses Computers Like a Human
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity
Learning About Progress From Experts
SPA: 3D Spatial-Awareness Enables Effective Embodied Representation
GeoLoRA: Geometric integration for parameter efficient fine-tuning
cryoSPHERE: Single-Particle HEterogeneous REconstruction from cryo EM
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
VibeCheck: Discover and Quantify Qualitative Differences in Large Language Models
Counterfactual Generative Modeling with Variational Causal Inference
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm