yusuke1997/dblp-id-title huggingface dataset:
Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning
Fast Federated Learning in the Presence of Arbitrary Device Unavailability
Bayesian Optimization of Functions over Node Subsets in Graphs
Smoothed Analysis of Sequential Probability Assignment
Interventional Causal Discovery in a Mixture of DAGs
Convergence of Stochastic Iterative Dynamic Programming Algorithms
Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
Locomotion in a Lower Vertebrate: Studies of the Cellular Basis of Rhythmogenesis and Oscillator Coupling
Deep content-based music recommendation
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Pretrained Optimization Model for Zero-Shot Black Box Optimization
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Fast Information Value for Graphical Models
Learning to Traverse Image Manifolds
The Canonical Distortion Measure in Feature Space and 1-NN Classification
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation
Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks
Dimension-free empirical entropy estimation
When does perceptual alignment benefit vision representations?
The Option Keyboard: Combining Skills in Reinforcement Learning
Budgeted Reinforcement Learning in Continuous State Space
OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters
Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation
Accessing Higher Dimensions for Unsupervised Word Translation
Can large language models explore in-context?
Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres
CoDet: Co-occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data
Abstract Reasoning with Distracting Features
Streamlining Variational Inference for Constraint Satisfaction Problems
Threshold Learning for Optimal Decision Making
Graph Reordering for Cache-Efficient Near Neighbor Search
Infinite Mixtures of Gaussian Process Experts
Sparse Gaussian Processes using Pseudo-inputs
On Differentially Private U Statistics
ARA*: Anytime A* with Provable Bounds on Sub-Optimality
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development
Diffusion Twigs with Loop Guidance for Conditional Graph Generation
Machine Learning and Interpretation in Neuroimaging - International Workshop, MLINI 2011, Held at NIPS 2011, Sierra Nevada, Spain, December 16-17, 2011, Revised Selected and Invited Contributions
Radial Basis Function Network for Multi-task Learning
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning
Mixture of Tokens: Continuous MoE through Cross-Example Aggregation
MIMONets: Multiple-Input-Multiple-Output Neural Networks Exploiting Computation in Superposition
Adaptive Stimulus Representations: A Computational Theory of Hippocampal-Region Functions
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks
Metamers of neural networks reveal divergence from human perceptual systems
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
Graph Self-supervised Learning with Accurate Discrepancy Learning
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense
Discrete Flows: Invertible Generative Models of Discrete Data
Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent
Generalized Leverage Score Sampling for Neural Networks
Gauging Variational Inference
A Powerful Generative Model Using Random Weights for the Deep Image Representation
Expected and Unexpected Uncertainty: ACh and NE in the Neocortex
Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
Learning by State Recurrence Detection
Fisher Efficient Inference of Intractable Models
MM-WLAuslan: Multi-View Multi-Modal Word-Level Australian Sign Language Recognition Dataset
A theoretical design of concept sets: improving the predictability of concept bottleneck models
Top-Ambiguity Samples Matter: Understanding Why Deep Ensemble Works in Selective Classification
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Self-supervised video pretraining yields robust and more human-aligned visual representations
Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios
Large-scale probabilistic predictors with and without guarantees of validity
Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection
Wavelet Models for Video Time-Series
Co-regularized Alignment for Unsupervised Domain Adaptation
Attentive Transfer Entropy to Exploit Transient Emergence of Coupling Effect
Self-Aware Personalized Federated Learning
Cooled and Relaxed Survey Propagation for MRFs
VideoCapsuleNet: A Simplified Network for Action Detection
Query Complexity of Bayesian Private Learning
TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models
Generalization Properties of NAS under Activation and Skip Connection Search
Markovian Score Climbing: Variational Inference with KL(p||q)
Deep Automodulators
Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions
Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem
DiffComplete: Diffusion-based Generative 3D Shape Completion
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
Estimation of Entropy in Constant Space with Improved Sample Complexity
Deep Transformation-Invariant Clustering
XCiT: Cross-Covariance Image Transformers
Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries
Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
PertEval: Unveiling Real Knowledge Capacity of LLMs with Knowledge-Invariant Perturbations
Clustering via Concave Minimization
Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness
Symbolic Distillation for Learned TCP Congestion Control
At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks
Unsupervised Transformation Learning via Convex Relaxations
QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning
Sample-Efficient Private Learning of Mixtures of Gaussians
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
Higher-Order Statistical Properties Arising from the Non-Stationarity of Natural Signals
Robust Allocations with Diversity Constraints
Minibatch Stochastic Approximate Proximal Point Methods
Subgoal Search For Complex Reasoning Tasks
BiMatting: Efficient Video Matting via Binarization
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
A Boosting Approach to Reinforcement Learning
Coherent 3D Scene Diffusion From a Single RGB Image
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
MonoForest framework for tree ensemble analysis
One-Shot Object Detection with Co-Attention and Co-Excitation
UltraPixel: Advancing Ultra High-Resolution Image Synthesis to New Peaks
InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
Counterfactual Evaluation of Peer-Review Assignment Policies
Learning Transferrable Representations for Unsupervised Domain Adaptation
Are Emergent Abilities of Large Language Models a Mirage?
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
Reinforcement Learning Methods for Continuous-Time Markov Decision Problems
DiffUTE: Universal Text Editing Diffusion Model
Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones
Quality-Improved and Property-Preserved Polarimetric Imaging via Complementarily Fusing
MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution
Independent mechanism analysis, a new concept?
CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations
Exploiting Connections between Lipschitz Structures for Certifiably Robust Deep Equilibrium Models
Online Improper Learning with an Approximation Oracle
Estimating Dependency Structure as a Hidden Variable
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior
Recovery of Coherent Data via Low-Rank Dictionary Pursuit
Recurrent Quantum Neural Networks
Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD
PROTES: Probabilistic Optimization with Tensor Sampling
Memory-Based Reinforcement Learning: Efficient Computation with Prioritized Sweeping
Gradient Dynamics of Shallow Univariate ReLU Networks
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data
Coarse sample complexity bounds for active learning
Continual learning with the neural tangent ensemble
Sample Efficient Active Learning of Causal Trees
A Pseudo-Bayesian Algorithm for Robust PCA
Conditional Generation Using Polynomial Expansions
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Graphical Models for Recognizing Human Interactions
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Gradient Methods Provably Converge to Non-Robust Networks
Instruction Tuning Large Language Models to Understand Electronic Health Records
Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives
Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering for HDR View Synthesis
Adaptive Auxiliary Task Weighting for Reinforcement Learning
Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
The Skellam Mechanism for Differentially Private Federated Learning
Kernel Descriptors for Visual Recognition
Entropy and mutual information in models of deep neural networks
e-SNLI: Natural Language Inference with Natural Language Explanations
Collapsed variational Bayes for Markov jump processes
An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
MsSVT: Mixed-scale Sparse Voxel Transformer for 3D Object Detection on Point Clouds
Particle-based Variational Inference for Continuous Systems
Subspace Embeddings for the Polynomial Kernel
Stability Guarantees for Feature Attributions with Multiplicative Smoothing
PeRFception: Perception using Radiance Fields
Optimized Covariance Design for AB Test on Social Network under Interference
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
Qualitative Structure From Motion
Online Composite Optimization Between Stochastic and Adversarial Environments
Wasserstein Dependency Measure for Representation Learning
Multi-View Reinforcement Learning
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
TrojLLM: A Black-box Trojan Prompt Attack on Large Language Models
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
Towards Efficient Post-training Quantization of Pre-trained Language Models
Global Structure-Aware Diffusion Process for Low-light Image Enhancement
The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation
Ordering-Based Causal Discovery for Linear and Nonlinear Relations
Efficient sparse coding algorithms
Learning Differential Equations that are Easy to Solve
Entropy-based Training Methods for Scalable Neural Implicit Samplers
Online-Within-Online Meta-Learning
A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences
Dual Progressive Prototype Network for Generalized Zero-Shot Learning
Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination
On Fenchel Mini-Max Learning
Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation
Revive Re-weighting in Imbalanced Learning by Density Ratio Estimation
Amortized Variational Inference for Simple Hierarchical Models
SELF-DISCOVER: Large Language Models Self-Compose Reasoning Structures
Fighting Bandits with a New Kind of Smoothness
Coupled Dynamics of Fast Neurons and Slow Interactions
Adversarially Robust Generalization Requires More Data
Transfer Learning by Distribution Matching for Targeted Advertising
Recognition-Based Segmentation of On-Line Hand-Printed Words
Color Equivariant Convolutional Networks
Scalable Fair Influence Maximization
Cognitive Model Discovery via Disentangled RNNs
Sensory Integration and Density Estimation
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems
General Articulated Objects Manipulation in Real Images via Part-Aware Diffusion Process
Compressive Sensing MRI with Wavelet Tree Sparsity
Non-rigid Point Cloud Registration with Neural Deformation Pyramid
A Boosting Framework on Grounds of Online Learning
Nonparametric Online Regression while Learning the Metric
Analysis of Information in Speech Based on MANOVA
Step Size Matters in Deep Learning
Visual Grammars and Their Neural Nets
HonestLLM: Toward an Honest and Helpful Large Language Model
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation
R2D2: Reliable and Repeatable Detector and Descriptor
Stage-wise Conservative Linear Bandits
Gaussian sampling by local perturbations
Fault-Aware Neural Code Rankers
Public-data Assisted Private Stochastic Optimization: Power and Limitations
Least Informative Dimensions
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
MOMA: Multi-Object Multi-Actor Activity Parsing
Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
DropBlock: A regularization method for convolutional networks
Hindsight Bias Impedes Learning
Treeffuser: probabilistic prediction via conditional diffusions with gradient-boosted trees
Markovian Sliced Wasserstein Distances: Beyond Independent Projections
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
Communication Efficient Distributed Machine Learning with the Parameter Server
Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models
Energy-based Hopfield Boosting for Out-of-Distribution Detection
Deep Graph Mating
Efficient Prompt Optimization Through the Lens of Best Arm Identification
Could Giant Pre-trained Image Models Extract Universal Representations?
Block Broyden's Methods for Solving Nonlinear Equations
AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
Credal Learning Theory
Robust Bi-Tempered Logistic Loss Based on Bregman Divergences
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data
Learning Neural Representations of Human Cognition across Many fMRI Studies
Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
Hierarchical Topic Models and the Nested Chinese Restaurant Process
From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Semi-supervised Vision Transformers at Scale
Stochastic Learning Networks and their Electronic Implementation
Interaction-Force Transport Gradient Flows
First-Order Methods for Large-Scale Market Equilibrium Computation
Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes
Natural Graph Networks
Online Clustering of Bandits with Misspecified User Models
Data driven semi-supervised learning
Preferential Normalizing Flows
Speech Production Using A Neural Network with a Cooperative Learning Mechanism
Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation
Cross-modal Representation Flattening for Multi-modal Domain Generalization
Online Ad Allocation with Predictions
The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic
Reinforced Genetic Algorithm for Structure-based Drug Design
Zero-Shot Transfer of Neural ODEs
Fine-grained Generalization Analysis of Inductive Matrix Completion
Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model
Deep Residual Learning in Spiking Neural Networks
On the non-universality of deep learning: quantifying the cost of symmetry
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
MLPerf Tiny Benchmark
Improved Switching among Temporally Abstract Actions
Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling
Exact learning curves for Gaussian process regression on large random graphs
Learning Mixture Hierarchies
Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
Artificial Olfactory Brain for Mixture Identification
PIXIU: A Comprehensive Benchmark, Instruction Dataset and Large Language Model for Finance
Pricing and Competition for Generative AI
Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
Lipschitz-Certifiable Training with a Tight Outer Bound
ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understanding
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
Learning models of object structure
Deep Reinforcement Learning of Marked Temporal Point Processes
Cognitive Steering in Deep Neural Networks via Long-Range Modulatory Feedback Connections
Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs
Infinite latent feature models and the Indian buffet process
A Dynamical Central Limit Theorem for Shallow Neural Networks
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents
Quasi-Monte Carlo Graph Random Features
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Policy Optimization for Continuous Reinforcement Learning
Doubly Convolutional Neural Networks
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
Bandit Convex Optimization: Towards Tight Bounds
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev
Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling
Multivariate Probabilistic Time Series Forecasting with Correlated Errors
Finding Safe Zones of Markov Decision Processes Policies
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
Object-Category Aware Reinforcement Learning
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
Scalable Adaptive Stochastic Optimization Using Random Projections
Identifiable Generative models for Missing Not at Random Data Imputation
OpenAssistant Conversations - Democratizing Large Language Model Alignment
A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets
PLUGIn: A simple algorithm for inverting generative models with recovery guarantees
Unlocking neural population non-stationarities using hierarchical dynamics models
On the design of convolutional neural networks for automatic detection of Alzheimer's disease
Switched Latent Force Models for Movement Segmentation
Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain
Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
Uncertainty-Aware Contour Proposal Networks for Cell Segmentation in Multi-Modality High-Resolution Microscopy Images
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
Generalized Lasso based Approximation of Sparse Coding for Visual Recognition
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss
Understanding Sparse JL for Feature Hashing
Making Latin Manuscripts Searchable using gHMMs
Multi-View Active Learning in the Non-Realizable Case
Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks
Stochastic Stein Discrepancies
Trading-off price for data quality to achieve fair online allocation
Practical Confidence and Prediction Intervals
Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces
VAIN: Attentional Multi-agent Predictive Modeling
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks
TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs
Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes
AutoGuide: Automated Generation and Selection of Context-Aware Guidelines for Large Language Model Agents
On the Information Theoretic Limits of Learning Ising Models
Binary Classification from Positive-Confidence Data
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
Online Linear Regression and Its Application to Model-Based Reinforcement Learning
Multiway clustering via tensor block models
Robust Near-Isometric Matching via Structured Learning of Graphical Models
Competitive On-line Linear Regression