yusuke1997/dblp-id-title huggingface dataset:
Learning Frequency-Adapted Vision Foundation Model for Domain Generalized Semantic Segmentation
Macro Placement by Wire-Mask-Guided Black-Box Optimization
A Decomposition of Forecast Error in Prediction Markets
Penalty-based Methods for Simple Bilevel Optimization under Hölderian Error Bounds
AER Building Blocks for Multi-Layer Multi-Chip Neuromorphic Vision Systems
Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30 - December 5, 1998]
Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion
Terra: A Multimodal Spatio-Temporal Dataset Spanning the Earth
An Information Maximization Model of Eye Movements
Generative Adversarial Imitation Learning
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models
How degenerate is the parametrization of neural networks with the ReLU activation function?
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
Decomposable Transformer Point Processes
OPEL: Optimal Transport Guided ProcedurE Learning
Learning to Perceive Transparency from the Statistics of Natural Scenes
Online Robust Policy Learning in the Presence of Unknown Adversaries
On the Global Linear Convergence of Frank-Wolfe Optimization Variants
Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis
Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Translation Model
Co-exposure Maximization in Online Social Networks
Proportional Fairness in Clustering: A Social Choice Perspective
Structured Semidefinite Programming for Recovering Structured Preconditioners
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
Kernel functions based on triplet comparisons
Constructing Proofs in Symmetric Networks
TransBoost: Improving the Best ImageNet Performance using Deep Transduction
Variational Bayesian Learning of Sparse Representations and Its Application in Functional Neuroimaging
Adaptive Object Representation with Hierarchically-Distributed Memory Sites
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes
Learning Nonsymmetric Determinantal Point Processes
Visual Instruction Tuning
Order-Independence Without Fine Tuning
Differentially Private Clustering: Tight Approximation Ratios
Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
PTADisc: A Cross-Course Dataset Supporting Personalized Learning in Cold-Start Scenarios
Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis
Refined Lower Bounds for Adversarial Bandits
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
Faster Non-asymptotic Convergence for Double Q-learning
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
StateMask: Explaining Deep Reinforcement Learning through State Mask
Methods Towards Invasive Human Brain Computer Interfaces
DrugCLIP: Contrasive Protein-Molecule Representation Learning for Virtual Screening
Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization
FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
4-bit Shampoo for Memory-Efficient Network Training
VoiceBlock: Privacy through Real-Time Adversarial Attacks with Audio-to-Audio Models
Truthfulness of Calibration Measures
Learning Invariant Graph Representations for Out-of-Distribution Generalization
How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad
A Locally Adaptive Normal Distribution
Goal-directed decision making in prefrontal cortex: a computational framework
One Microphone Source Separation
Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut
OpenAUC: Towards AUC-Oriented Open-Set Recognition
Data-Driven Conditional Robust Optimization
On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias
Projective dictionary pair learning for pattern classification
Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability
Risk-Aversion in Multi-armed Bandits
Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation
Fast Active Set Methods for Online Spike Inference from Calcium Imaging
Diffusion Curvature for Estimating Local Curvature in High Dimensional Data
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics
Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations
Debiased Machine Learning without Sample-Splitting for Stable Estimators
Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets
Minimax and Hamiltonian Dynamics of Excitatory-Inhibitory Networks
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models
Learning Rankings via Convex Hull Separation
Two-step lookahead Bayesian optimization with inequality constraints
SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning
What's a good imputation to predict with missing values?
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
The Prevalence of Neural Collapse in Neural Multivariate Regression
Extracting computational mechanisms from neural data using low-rank RNNs
Optimal Subsampling with Influence Functions
Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Random Projection Filter Bank for Time Series Data
Spin-Weighted Spherical CNNs
Inverse Learning of Symmetries
Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness
4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization
Ex ante coordination and collusion in zero-sum multi-player extensive-form games
Training Transformers with 4-bit Integers
Pointer Graph Networks
Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases
Functional Neural Networks for Parametric Image Restoration Problems
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
The Use of Dynamic Writing Information in a Connectionist On-Line Cursive Handwriting Recognition System
Color Opponency Constitutes a Sparse Representation for the Chromatic Structure of Natural Scenes
Analytic Insights into Structure and Rank of Neural Network Hessian Maps
Unsupervised Text Style Transfer using Language Models as Discriminators
Learning better with Dale's Law: A Spectral Perspective
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Perceptron Learning of SAT
On the Second-order Convergence Properties of Random Search Methods
LiT: Unifying LiDAR "Languages" with LiDAR Translator
VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person Detection
Does progress on ImageNet transfer to real-world datasets?
Manifold Topology Divergence: a Framework for Comparing Data Manifolds
Causal Navigation by Continuous-time Neural Networks
TransTab: Learning Transferable Tabular Transformers Across Tables
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization
Measuring Data Reconstruction Defenses in Collaborative Inference Systems
Recognize Any Regions
AIMS: All-Inclusive Multi-Level Segmentation for Anything
OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
CaMP: Causal Multi-policy Planning for Interactive Navigation in Multi-room Scenes
3D Object Proposals for Accurate Object Class Detection
Learning with Multiple Labels
When does label smoothing help?
Neural Circuit Architectural Priors for Embodied Control
Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment
Towards In-context Scene Understanding
Few-Shot Adversarial Domain Adaptation
The Power of Predictions in Online Control
Mitigating Covariate Shift in Behavioral Cloning via Robust Stationary Distribution Correction
UrbanKGent: A Unified Large Language Model Agent Framework for Urban Knowledge Graph Construction
Competence Acquisition in an Autonomous Mobile Robot using Hardware Neural Techniques
Learning Optimal Representations with the Decodable Information Bottleneck
Gaussian Partial Information Decomposition: Bias Correction and Application to High-dimensional Data
SLTrain: a sparse plus low rank approach for parameter and memory efficient pretraining
A Lagrangian Duality Approach to Active Learning
Making Scalable Meta Learning Practical
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features
Probabilistic Fair Clustering
Training for Stable Explanation for Free
Max-Margin Invariant Features from Transformed Unlabelled Data
NN4SysBench: Characterizing Neural Network Verification for Computer Systems
Proximal Graphical Event Models
Can SGD Learn Recurrent Neural Networks with Provable Generalization?
Iterative Teaching by Label Synthesis
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
STREAMER: Streaming Representation Learning and Event Segmentation in a Hierarchical Manner
Combining Neural Networks and Context-Driven Search for On-Line, Printed Handwriting Recognition in the Newton
Recurrent Networks and NARMA Modeling
Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models
Conditional mean field
Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models
On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory
Group Anomaly Detection using Flexible Genre Models
DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously
Mixture-Rank Matrix Approximation for Collaborative Filtering
Selective Sampling for Online Best-arm Identification
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
CoSW: Conditional Sample Weighting for Smoke Segmentation with Label Noise
Even Faster SVD Decomposition Yet Without Agonizing Pain
Bridging Model-Based Optimization and Generative Modeling via Conservative Fine-Tuning of Diffusion Models
Reverse-Complement Equivariant Networks for DNA Sequences
Hiding Images in Deep Probabilistic Models
Towards Higher Ranks via Adversarial Weight Pruning
Self-Supervised Intrinsic Image Decomposition
Coupled Markov Random Fields and Mean Field Theory
Sign Cauchy Projections and Chi-Square Kernel
Residual Pathway Priors for Soft Equivariance Constraints
ShuffleMixer: An Efficient ConvNet for Image Super-Resolution
Quality Aware Generative Adversarial Networks
Learning a Rare Event Detection Cascade by Direct Feature Selection
CS-Isolate: Extracting Hard Confident Examples by Content and Style Isolation
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Learn To be Efficient: Build Structured Sparsity in Large Language Models
Fairness-Aware Meta-Learning via Nash Bargaining
Multi-Stage Dantzig Selector
A mechanistic multi-area recurrent network model of decision-making
Generating compositional scenes via Text-to-image RGBA Instance Generation
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems
Scalable Log Determinants for Gaussian Process Kernel Learning
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow Shrink Trees
ResMem: Learn what you can and memorize the rest
Inverse Filtering for Hidden Markov Models
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models
Scalable Inference for Neuronal Connectivity from Calcium Imaging
Online Decision-Making in General Combinatorial Spaces
Asynchronous Anytime Sequential Monte Carlo
Observational-Interventional Priors for Dose-Response Learning
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding
Policy Learning for Fairness in Ranking
RETVec: Resilient and Efficient Text Vectorizer
Decentralized Cooperative Stochastic Bandits
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training
Fair Wrapping for Black-box Predictions
Universal Invariant and Equivariant Graph Neural Networks
Decomposable Submodular Function Minimization: Discrete and Continuous
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis
ColdGANs: Taming Language GANs with Cautious Sampling Strategies
On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond
Optimal Stopping and Effective Machine Complexity in Learning
Optimal Sparse Decision Trees
Bayesian Policy Gradient Algorithms
AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos
On Adaptive Attacks to Adversarial Example Defenses
Improving a Page Classifier with Anchor Extraction and Link Analysis
Adversarially Robust Decision Transformer
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
Probabilistic Invariant Learning with Randomized Linear Classifiers
Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
Neural Network Routing for Random Multistage Interconnection Networks
On GANs and GMMs
An analytic theory of shallow networks dynamics for hinge loss classification
Dynamic Rank Factor Model for Text Streams
Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning
Zero-Shot Semantic Segmentation
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis
Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels
Matrix reconstruction with the local max norm
Stabilizing Dynamical Systems via Policy Gradient Methods
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems
Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence
MoEUT: Mixture-of-Experts Universal Transformers
Random Reshuffling: Simple Analysis with Vast Improvements
FACT: Learning Governing Abstractions Behind Integer Sequences
Multistep Distillation of Diffusion Models via Moment Matching
BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression
Ratio Trace Formulation of Wasserstein Discriminant Analysis
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Localization with Limited Annotation for Chest X-rays
Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning
Dual Estimation and the Unscented Transformation
Inference and Sampling for Archimax Copulas
Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models
Implicit Graph Neural Networks
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
Can Learned Optimization Make Reinforcement Learning Less Difficult?
Theory and Algorithms for the Localized Setting of Learning Kernels
PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change
Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images
Cholinergic Modulation Preserves Spike Timing Under Physiologically Realistic Fluctuating Input
Local Differential Privacy for Evolving Data
The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries
Weighted Distillation with Unlabeled Examples
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient
Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models
Debiasing Scores and Prompts of 2D Diffusion for View-consistent Text-to-3D Generation
ActionSense: A Multimodal Dataset and Recording Framework for Human Activities Using Wearable Sensors in a Kitchen Environment
Speedy Performance Estimation for Neural Architecture Search
AViD Dataset: Anonymized Videos from Diverse Countries
Fair Multiple Decision Making Through Soft Interventions
Towards Robust Multimodal Sentiment Analysis with Incomplete Data
Batch size-invariance for policy optimization
Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference
Multi-Scale Representation Learning for Protein Fitness Prediction
Refractoriness and Neural Precision
Boosting the Area under the ROC Curve
Data-Efficient Augmentation for Training Neural Networks
Deconditional Downscaling with Gaussian Processes
Amortized Mixing Coupling Processes for Clustering
SHINE: SubHypergraph Inductive Neural nEtwork
But How Does It Work in Theory? Linear SVM with Random Features
Learning Infinite RBMs with Frank-Wolfe
Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees
Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems
Memory Efficient Optimizers with 4-bit States
Linear Time Sinkhorn Divergences using Positive Features
Adaptive Averaging in Accelerated Descent Dynamics
Relating Leverage Scores and Density using Regularized Christoffel Functions
Image Denoising and Inpainting with Deep Neural Networks
Adaptive Access Control Applied to Ethernet Data
Question Asking as Program Generation
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing
Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark
Generating velocity tuning by asymmetric recurrent connections
Generalized Protein Pocket Generation with Prior-Informed Flow Matching
Efficient Learning Equilibrium
Nearest Neighbour with Bandit Feedback
An Adaptive Metric Machine for Pattern Classification
Systematic improvement of neural network quantum states using Lanczos
Modulated Self-attention Convolutional Network for VQA
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
Emergent Communication of Generalizations
Lattice Regression
Searching Parameterized AP Loss for Object Detection
Spatial Mixture-of-Experts
A Dynamical Approach to Temporal Pattern Processing
Representation Learning of Compositional Data
What training reveals about neural network complexity
Towards Distribution-Agnostic Generalized Category Discovery
Efficient Second-Order Online Kernel Learning with Adaptive Embedding
Temporal Adaptation in a Silicon Auditory Nerve
Probabilistic Orientation Estimation with Matrix Fisher Distributions
Learning Stochastic Perceptrons Under k-Blocking Distributions
On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs
Gradient Descent: Second Order Momentum and Saturating Error
The Tradeoffs of Large Scale Learning
Nonparametric learning from Bayesian models with randomized objective functions
FineCLIP: Self-distilled Region-based CLIP for Better Fine-grained Understanding
Embedding Symbolic Knowledge into Deep Networks
Multi-LexSum: Real-world Summaries of Civil Rights Lawsuits at Multiple Granularities
General Tensor Spectral Co-clustering for Higher-Order Data
Exploring Bit-Slice Sparsity in Deep Neural Networks for Efficient ReRAM-Based Deployment
EgoChoir: Capturing 3D Human-Object Interaction Regions from Egocentric Views
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
Heatmap Distribution Matching for Human Pose Estimation
Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning
Identification and Estimation of Causal Effects from Dependent Data
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
Intelligent Knee Sleeves: A Real-time Multimodal Dataset for 3D Lower Body Motion Estimation Using Smart Textile
Development and Spatial Structure of Cortical Feature Maps: A Model Study
Neural Relightable Participating Media Rendering
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016 co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, December 9, 2016
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler
The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
IQA-EVAL: Automatic Evaluation of Human-Model Interactive Question Answering
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream
Self-supervised surround-view depth estimation with volumetric feature fusion
Learning Optimal Tax Design in Nonatomic Congestion Games
Inferring Sparse, Overcomplete Image Codes Using an Efficient Coding Framework
Bayes Networks on Ice: Robotic Search for Antarctic Meteorites
Fractal Grammars which Recover from Perturbations
Efficient Risk-Averse Reinforcement Learning
B-test: A Non-parametric, Low Variance Kernel Two-sample Test
Learning Adaptive Tensorial Density Fields for Clean Cryo-ET Reconstruction
Alignment with human representations supports robust few-shot learning
AdaptiveISP: Learning an Adaptive Image Signal Processor for Object Detection
Processing of Visual and Auditory Space and Its Modification by Experience
Learning to Optimize via Information-Directed Sampling
Bridging Geometric States via Geometric Diffusion Bridge
Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games
Omnigrasp: Grasping Diverse Objects with Simulated Humanoids
Reinforcement Learning Benchmarks for Traffic Signal Control
Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
Convolution Monge Mapping Normalization for learning on sleep data
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information
Comparing Unsupervised Word Translation Methods Step by Step
Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems
Optimal Rates for Regularized Conditional Mean Embedding Learning
Efficient Generalization with Distributionally Robust Learning
Co-Training for Domain Adaptation
A Study on Encodings for Neural Architecture Search
Using a Saliency Map for Active Spatial Selective Attention: Implementation & Initial Results
Learning a Gaussian Process Prior for Automatically Generating Music Playlists