yusuke1997/dblp-id-title huggingface dataset:
Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine
MoGU: A Framework for Enhancing Safety of LLMs While Preserving Their Usability
Hierarchical VAEs provide a normative account of motion processing in the primate brain
Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes
Enhancing LLM Reasoning via Vision-Augmented Prompting
Population Codes Representing Musical Timbre for High-Level fMRI Categorization of Music Genres
HEAR: Holistic Evaluation of Audio Representations
Linear tree shap
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models
Voxel Proposal Network via Multi-Frame Knowledge Distillation for Semantic Scene Completion
Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems
Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness
Optimization, Learning, and Games with Predictable Sequences
Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework
A Unified View of Label Shift Estimation
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
BEDD: The MineRL BASALT Evaluation and Demonstrations Dataset for Training and Benchmarking Agents that Solve Fuzzy Tasks
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images
What Can a Single Neuron Compute?
Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees
Exact inference and learning for cumulative distribution functions on loopy graphs
NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics
Grounding Multimodal Large Language Models in Actions
Wasserstein Iterative Networks for Barycenter Estimation
Direct Training of SNN using Local Zeroth Order Method
Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains
Global Filter Networks for Image Classification
Learning To Learn Around A Common Mean
Counterfactual Fairness
Recovery of Jointly Sparse Signals from Few Random Projections
Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling
Effects of Noise on Convergence and Generalization in Recurrent Networks
Adversarial Model for Offline Reinforcement Learning
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Phase Retrieval using Alternating Minimization
A quasi-Newton proximal splitting method
Understanding Deep Contrastive Learning via Coordinate-wise Optimization
Risk-sensitive control as inference with Rényi divergence
Limits of Spectral Clustering
Directional diffusion models for graph representation learning
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Nonlinear Pattern Separation in Single Hippocampal Neurons with Active Dendritic Membrane
How to Data in Datathons
Movement extraction by detecting dynamics switches and repetitions
Dependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1
RedPajama: an Open Dataset for Training Large Language Models
State Sequences Prediction via Fourier Transform for Representation Learning
Indexed Minimum Empirical Divergence for Unimodal Bandits
EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models
CAM Storage of Analog Patterns and Continuous Sequences with 3N 2 Weights
Online learning of long-range dependencies
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Can neural operators always be continuously discretized?
Annihilation of Spurious Minima in Two-Layer ReLU Networks
Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset
An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism
Covariance shrinkage for autocorrelated data
Generalization in Adaptive Data Analysis and Holdout Reuse
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations
Error Correction Output Codes for Robust Neural Networks against Weight-errors: A Neural Tangent Kernel Point of View
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
Identifying graph-structured activation patterns in networks
MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
Probabilistic Methods for Support Vector Machines
UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition
Bayesian models for Large-scale Hierarchical Classification
Towards Understanding the Importance of Shortcut Connections in Residual Networks
Gaussian Processes in Reinforcement Learning
Selective Explanations
Large-Scale Methods for Distributionally Robust Optimization
Visual Instruction Inversion: Image Editing via Image Prompting
ProteinShake: Building datasets and benchmarks for deep learning on protein structures
Non-Crossing Quantile Regression for Distributional Reinforcement Learning
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method
Self-training Avoids Using Spurious Features Under Domain Shift
Kermut: Composite kernel regression for protein variant effects
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning
Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach
Certified Machine Unlearning via Noisy Stochastic Gradient Descent
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
Adversarial Distributional Training for Robust Deep Learning
SDformer: Similarity-driven Discrete Transformer For Time Series Generation
Connectivity Versus Entropy
Function Classes for Identifiable Nonlinear Independent Component Analysis
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild
Incremental Natural Actor-Critic Algorithms
Adaptable Logical Control for Large Language Models
Improved Distribution Matching Distillation for Fast Image Synthesis
Probabilistic Semantic Video Indexing
Orbit Regularization
Neural Net and Traditional Classifiers
Denoising Normalizing Flow
Intra-agent speech permits zero-shot task acquisition
Optimal Reinforcement Learning for Gaussian Systems
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Multi-Group Proportional Representation in Retrieval
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability
How Do Humans Teach: On Curriculum Learning and Teaching Dimension
On Sample Optimality in Personalized Collaborative and Federated Learning
Dynamics of Learning in Recurrent Feature-Discovery Networks
Smooth and Strong: MAP Inference with Linear Convergence
Lung250M-4B: A Combined 3D Dataset for CT- and Point Cloud-Based Intra-Patient Lung Registration
Predicting Embedded Syntactic Structures from Natural Language Sentences with Neural Network Approaches
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
Variational Weighting for Kernel Density Ratios
Glance and Focus: Memory Prompting for Multi-Event Video Question Answering
Dynamic Rescaling for Training GNNs
Variational Model Inversion Attacks
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
Revenue Optimization against Strategic Buyers
Fast Bayesian Estimation of Point Process Intensity as Function of Covariates
Generalization Abilities of Cascade Network Architecture
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings
Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics
High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes
Backpropagation without Multiplication
Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Unsupervised Learning in Neurodynamics Using the Phase Velocity Field Approach
A probabilistic model for generating realistic lip movements from speech
Understanding Multi-Granularity for Open-Vocabulary Part Segmentation
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Integral Probability Metrics PAC-Bayes Bounds
VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution
Contextual Bandits with Cross-Learning
Natural Dolphin Echo Recognition Using an Integrator Gateway Network
A Polygonal Line Algorithm for Constructing Principal Curves
From Algorithmic to Subjective Randomness
Neural Circuit Synthesis from Specification Patterns
Learning Sequential Tasks by Incrementally Adding Higher Orders
Inversion-based Latent Bayesian Optimization
Analyzing the Harmonic Structure in Graph-Based Learning
The Elastic Lottery Ticket Hypothesis
Active Data Clustering
Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness
Invariant Tokenization of Crystalline Materials for Language Model Enabled Generation
Robust Principal Component Analysis with Adaptive Neighbors
Predicting Label Distribution from Multi-label Ranking
MILP-StuDio: MILP Instance Generation via Block Structure Decomposition
Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation
Winning the Lottery with Continuous Sparsification
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
Transformer Doctor: Diagnosing and Treating Vision Transformers
Unsupervised Variational Bayesian Learning of Nonlinear Models
KLUE: Korean Language Understanding Evaluation
Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection
Fast Approximation of Similarity Graphs with Kernel Density Estimation
Non-Linear Dimensionality Reduction
DeltaDEQ: Exploiting Heterogeneous Convergence for Accelerating Deep Equilibrium Iterations
Coordinate-wise Power Method
CRF-CNN: Modeling Structured Information in Human Pose Estimation
Spiking Graph Neural Network on Riemannian Manifolds
Diffusion Actor-Critic with Entropy Regulator
Boosting Adversarial Transferability by Achieving Flat Local Maxima
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Feature Likelihood Score: Evaluating the Generalization of Generative Models Using Samples
Automated Classification of Model Errors on ImageNet
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
S2HPruner: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
Provably efficient, succinct, and precise explanations
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
LieGG: Studying Learned Lie Group Generators
If Influence Functions are the Answer, Then What is the Question?
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
IaC-Eval: A Code Generation Benchmark for Cloud Infrastructure-as-Code Programs
Stochastic contextual bandits with graph feedback: from independence number to MAS number
Bayesian Structure Learning by Recursive Bootstrap
Cardinality-Aware Set Prediction and Top-$k$ Classification
Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks
Teachable Reinforcement Learning via Advice Distillation
Efficient Testable Learning of Halfspaces with Adversarial Label Noise
Towards a Unified Information-Theoretic Framework for Generalization
Robust Generalization despite Distribution Shift via Minimum Discriminating Information
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations
Unveiling the Tapestry of Consistency in Large Vision-Language Models
Neural Network Diagnosis of Avascular Necrosis from Magnetic Resonance Images
Optimal ROC Curve for a Combination of Classifiers
Calibration and Consistency of Adversarial Surrogate Losses
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
A model of the hippocampus combibing self-organization and associative memory function
Large Scale Structure of Neural Network Loss Landscapes
A Framework for Non-rigid Matching and Correspondence
NeurIPS’22 Cross-Domain MetaDL Challenge: Results and lessons learned
Your head is there to move you around: Goal-driven models of the primate dorsal pathway
Exact inference in structured prediction
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
A Model for Learning Variance Components of Natural Images
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
Training Neural Networks is ER-complete
Reducing Reparameterization Gradient Variance
Denoising and Untangling Graphs Using Degree Priors
Energy-based learning algorithms for analog computing: a comparative study
Learning Versatile Filters for Efficient Convolutional Neural Networks
Smooth Normalizing Flows
Conformal Inverse Optimization
Σ-Optimality for Active Learning on Gaussian Random Fields
Neural Non-Rigid Tracking
Loss Functions for Multiset Prediction
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models
AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation
Temporally-Consistent Survival Analysis
Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks
Improving Deep Learning Interpretability by Saliency Guided Training
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Adaptive Caching by Refetching
Does Preprocessing Help Training Over-parameterized Neural Networks?
Identification and Overidentification of Linear Structural Equation Models
Rethinking Image Restoration for Object Detection
Lifelong Inverse Reinforcement Learning
Learning with Product Units
Neural Architecture
Statistical Dynamics of Batch Learning
Piecewise Strong Convexity of Neural Networks
Prompt-augmented Temporal Point Process for Streaming Event Sequence
Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron
The Implicit Bias of Minima Stability: A View from Function Space
Collapsed Variational Inference for HDP
Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training
Fast and Effective Robustness Certification
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
A Local Learning Approach for Clustering
Sparse High-Dimensional Isotonic Regression
Structural Knowledge Distillation for Object Detection
Learning to Parameterize Visual Attributes for Open-set Fine-grained Retrieval
Ordered Memory
Large Margin DAGs for Multiclass Classification
Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
Federated Principal Component Analysis
A Bandit Learning Algorithm and Applications to Auction Design
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
How do Minimum-Norm Shallow Denoisers Look in Function Space?
Analysis of Bit Error Probability of Direct-Sequence CDMA Multiuser Demodulators
Convolutional Generation of Textured 3D Meshes
Efficient Methods for Dealing with Missing Data in Supervised Learning
3D Concept Grounding on Neural Fields
WikiDO: A New Benchmark Evaluating Cross-Modal Retrieval for Vision-Language Models
LEACE: Perfect linear concept erasure in closed form
Multi-Class Deep Boosting
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
SocraticLM: Exploring Socratic Personalized Teaching with Large Language Models
A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation
Needle In A Multimodal Haystack
Multiagent Evaluation under Incomplete Information
Linear Submodular Bandits and their Application to Diversified Retrieval
Label Distribution Learning Forests
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
Accelerating Nash Equilibrium Convergence in Monte Carlo Settings Through Counterfactual Value Based Fictitious Play
Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy
Learning visual biases from human imagination
Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching
Online Convex Optimization with Stochastic Constraints
A Second Order Cone programming Formulation for Classifying Missing Data
Learning to Compose Domain-Specific Transformations for Data Augmentation
Improving Variational Autoencoders with Density Gap-based Regularization
Differentiating Functions of the Jacobian with Respect to the Weights
GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
A Drifting-Games Analysis for Online Learning and Applications to Boosting
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach
Power and limitations of single-qubit native quantum neural networks
Uncertainty Calibration for Ensemble-Based Debiasing Methods
Combinatorial Energy Learning for Image Segmentation
Tree-Structured Stick Breaking for Hierarchical Data
Semi-Supervised Domain Generalization with Known and Unknown Classes
Some results on convergent unlearning algorithm
The Utility of Explainable AI in Ad Hoc Human-Machine Teaming
Continuous Temporal Domain Generalization
Multi-Bandit Best Arm Identification
CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms
Constraints on Adaptive Networks for Modeling Human Generalization
BourGAN: Generative Networks with Metric Embeddings
Binary Classification with Confidence Difference
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs
Progressive Augmentation of GANs
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models
WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks
RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-Identification
A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression
PCA Initialization for Approximate Message Passing in Rotationally Invariant Models
Diffusion Visual Counterfactual Explanations
PuLID: Pure and Lightning ID Customization via Contrastive Alignment
When are Local Queries Useful for Robust Learning?
How2comm: Communication-Efficient and Collaboration-Pragmatic Multi-Agent Perception
Large-Scale Multiclass Transduction
Orientational and Geometric Determinants of Place and Head-direction
Dataset Inference for Self-Supervised Models
Learning with Partially Absorbing Random Walks
SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models
EEVR: A Dataset of Paired Physiological Signals and Textual Descriptions for Joint Emotion Representation Learning
Cell Segmentation in Multi-modality High-Resolution Microscopy Images with Cellpose
Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
An Improved Scheme for Detection and Labelling in Johansson Displays
Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion
Offline Multi-Agent Reinforcement Learning with Knowledge Distillation
TransHP: Image Classification with Hierarchical Prompting
Improving Explorability in Variational Inference with Annealed Variational Objectives
Learning in Feedforward Networks with Nonsmooth Functions
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards
Flow Snapshot Neurons in Action: Deep Neural Networks Generalize to Biological Motion Perception
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
Truncated proposals for scalable and hassle-free simulation-based inference
A Convergence Analysis of Gradient Descent on Graph Neural Networks
Classification Done Right for Vision-Language Pre-Training
Constructing Heterogeneous Committees Using Input Feature Grouping: Application to Economic Forecasting
No-regret Learning in Harmonic Games: Extrapolation in the Face of Conflicting Interests
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
Bootstrap Your Object Detector via Mixed Training
Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models
DevBench: A multimodal developmental benchmark for language learning
Nested Mini-Batch K-Means
Derandomized novelty detection with FDR control via conformal e-values
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning
Large Scale Distributed Sparse Precision Estimation
Benchmarking Robustness to Adversarial Image Obfuscations
A Reconfigurable Analog VLSI Neural Network Chip
Dual-Space Analysis of the Sparse Linear Model
A case for reframing automated medical image classification as segmentation
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer
Classification via Minimum Incremental Coding Length (MICL)
Analyzing Compositionality in Visual Question Answering
On Learning Rotations
Active Preference Learning with Discrete Choice Data
3D Object Recognition Using Unsupervised Feature Extraction
DRIVE: One-bit Distributed Mean Estimation
Bayesian Optimization with Cost-varying Variable Subsets
Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models
Predictive Indexing for Fast Search
Inverting Deep Generative models, One layer at a time
Learning word embeddings efficiently with noise-contrastive estimation
Boosting Vision-Language Models with Transduction
An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits
From Isolation to Cooperation: An Alternative View of a System of Experts
Agnostic Learning of a Single Neuron with Gradient Descent
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime
Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables
Switching state space model for simultaneously estimating state transitions and nonstationary firing rates
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians