yusuke1997/dblp-id-title huggingface dataset:
A probabilistic population code based on neural samples
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
Sequential Decision Probelms and Neural Networks
Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture
Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation
Double Auctions with Two-sided Bandit Feedback
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Simplified Graph Convolution with Heterophily
Neural Network Model Selection Using Asymptotic Jackknife Estimator and Cross-Validation Method
Tree-Structured Reinforcement Learning for Sequential Object Localization
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment
Continuous Heatmap Regression for Pose Estimation via Implicit Neural Representation
Manifold-based Similarity Adaptation for Label Propagation
Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task
Learning Diffusion Priors from Observations by Expectation Maximization
Continuous Submodular Maximization: Beyond DR-Submodularity
Black-Box Differential Privacy for Interactive ML
SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset
Q-VLM: Post-training Quantization for Large Vision-Language Models
Prodding the ROC Curve: Constrained Optimization of Classifier Performance
FaceComposer: A Unified Model for Versatile Facial Content Creation
Improved Regret of Linear Ensemble Sampling
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage
Policy Optimization for Markov Games: Unified Framework and Faster Convergence
Recovering from Out-of-sample States via Inverse Dynamics in Offline Reinforcement Learning
Real-Time Bidding with Side Information
Collapsed Variational Bounds for Bayesian Neural Networks
A Composable Specification Language for Reinforcement Learning Tasks
On the Frequency-bias of Coordinate-MLPs
Semi-Parametric Dynamic Contextual Pricing
Multiresolution Kernel Approximation for Gaussian Process Regression
Learning Guidance Rewards with Trajectory-space Smoothing
Benefits of Additive Noise in Composing Classes with Bounded Capacity
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks
Multi-modal Dependency Tree for Video Captioning
Spike-based Learning Rules and Stabilization of Persistent Neural Activity
A Novel Net that Learns Sequential Decision Process
Genetic Algorithms and Explicit Search Statistics
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
Near-Optimal Entrywise Sampling for Data Matrices
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?
S3GC: Scalable Self-Supervised Graph Clustering
Error-correcting Codes on a Bethe-like Lattice
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
Phase Transitions and the Perceptual Organization of Video Sequences
Submodular Maximization Through Barrier Functions
A Bayesian Analysis of Dynamics in Free Recall
Reparameterizing Mirror Descent as Gradient Descent
A Model for Real-Time Computation in Generic Neural Microcircuits
Learning a Distance Metric from a Network
How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods
Replicated Softmax: an Undirected Topic Model
Multi-Fidelity Bayesian Optimization via Deep Neural Networks
Parametrized Quantum Policies for Reinforcement Learning
Sparse Representation and Its Applications in Blind Source Separation
GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech
A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
Predicting utilization of healthcare services from individual disease trajectories using RNNs with multi-headed attention
Wormhole Loss for Partial Shape Matching
Random Walk Graph Neural Networks
On the Stability and Scalability of Node Perturbation Learning
Pointwise Bounds for Distribution Estimation under Communication Constraints
Understanding the Role of Training Regimes in Continual Learning
Relative Flatness and Generalization
DP-HyPO: An Adaptive Private Framework for Hyperparameter Optimization
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC
A Competitive Algorithm for Agnostic Active Learning
Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules
Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming
Sparsity of SVMs that use the epsilon-insensitive loss
Handwritten Digit Recognition with a Back-Propagation Network
Differentially Private Change-Point Detection
Building Memory with Concept Learning Capabilities from Large-Scale Knowledge Bases
Faster Local Solvers for Graph Diffusion Equations
Learning to Price Homogeneous Data
You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Early Detection of Combustion Instability by Neural-Symbolic Analysis on Hi-Speed Video
AC-GC: Lossy Activation Compression with Guaranteed Convergence
Algorithms for Hyper-Parameter Optimization
On the Generalization Error of Stochastic Mirror Descent for Quadratically-Bounded Losses: an Improved Analysis
Bayesian entropy estimation for binary spike train data using parametric prior knowledge
Optimization on a Budget: A Reinforcement Learning Approach
Biclustering Usinig Message Passing
Combining Different Modalities in Classifying Phonological Categories
Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective
Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples
Regularized Co-Clustering with Dual Supervision
Learning To Dive In Branch And Bound
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers
Hard-Attention for Scalable Image Classification
Reinforcement Learning Guided Semi-Supervised Learning
Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects
Convolutional State Space Models for Long-Range Spatiotemporal Modeling
Local Explanation of Dialogue Response Generation
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Efficient Non-Parametric Optimizer Search for Diverse Tasks
Fast Prediction for Large-Scale Kernel Machines
Optimizing affinity-based binary hashing using auxiliary coordinates
Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence
Contrastively Disentangled Sequential Variational Autoencoder
SyncVIS: Synchronized Video Instance Segmentation
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model
Minimizing a Submodular Function from Samples
Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction
Multiplicative Forests for Continuous-Time Processes
Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning
PLLay: Efficient Topological Layer based on Persistent Landscapes
Parameter-free Regret in High Probability with Heavy Tails
TPC: Test-time Procrustes Calibration for Diffusion-based Human Image Animation
Logical characterizations of recurrent graph neural networks with reals and floats
Sketching for Distributed Deep Learning: A Sharper Analysis
Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis
Fast Algorithms for Robust PCA via Gradient Descent
Acoustic Volume Rendering for Neural Impulse Response Fields
large scale canonical correlation analysis with iterative least squares
On the cohesion and separability of average-link for hierarchical agglomerative clustering
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts
IM-Loss: Information Maximization Loss for Spiking Neural Networks
Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models
Tractable Function-Space Variational Inference in Bayesian Neural Networks
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Single-phase deep learning in cortico-cortical networks
Online Independent Component Analysis with Local Learning Rate Adaptation
Bayesian Strategic Classification
On the Expected Complexity of Maxout Networks
Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction
One Explanation is Not Enough: Structured Attention Graphs for Image Classification
k-NN Regression Adapts to Local Intrinsic Dimension
Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation
H-Consistency Bounds: Characterization and Extensions
Mixability in Statistical Learning
One-shot learning by inverting a compositional causal process
PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling
Supervised Word Mover's Distance
A Theory of Retinal Population Coding
ANN Board Classification for Heart Defibrillators
Learning Causal Semantic Representation for Out-of-Distribution Prediction
NTopo: Mesh-free Topology Optimization using Implicit Neural Representations
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning
From ViT Features to Training-free Video Object Segmentation via Streaming-data Mixture Models
Demystifying and Generalizing BinaryConnect
InsActor: Instruction-driven Physics-based Characters
Diffusion of Credit in Markovian Models
Application of Blind Separation of Sources to Optical Recording of Brain Activity
Intrusion Detection with Neural Networks
CMA-ES with Optimal Covariance Update and Storage Complexity
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization
OneRef: Unified One-tower Expression Grounding and Segmentation with Mask Referring Modeling
Early-Learning Regularization Prevents Memorization of Noisy Labels
Transferable Boltzmann Generators
Pushing the limits of fairness impossibility: Who's the fairest of them all?
Regularization Techniques to Improve Generalization: Preface
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
Bayesian Persuasion for Algorithmic Recourse
Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs
The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
Local Dimensionality Reduction
Heavy Ball Neural Ordinary Differential Equations
Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data
Hierarchical and Density-based Causal Clustering
Joint Cascade Optimization Using A Product Of Boosted Classifiers
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions
Hierarchical Decision Making by Generating and Following Natural Language Instructions
Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints
Posterior Re-calibration for Imbalanced Datasets
Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder
Neural Decoding of Cursor Motion Using a Kalman Filter
Augment-and-Conquer Negative Binomial Processes
Strategic Behavior in Two-sided Matching Markets with Prediction-enhanced Preference-formation
Batch Multi-Fidelity Active Learning with Budget Constraints
Collaborative Filtering with Graph Information: Consistency and Scalable Methods
Breadcrumbs to the Goal: Supervised Goal Selection from Human-in-the-Loop Feedback
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function
TradeMaster: A Holistic Quantitative Trading Platform Empowered by Reinforcement Learning
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
FreeSplat: Generalizable 3D Gaussian Splatting Towards Free View Synthesis of Indoor Scenes
Bicriteria Approximation Algorithms for the Submodular Cover Problem
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
Spike-Timing-Dependent Learning for Oscillatory Networks
Marginals-to-Models Reducibility
Can LLMs Implicitly Learn Numeric Parameter Constraints in Data Science APIs?
Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics
Entropy testing and its application to testing Bayesian networks
Structured Reordering for Modeling Latent Alignments in Sequence Transduction
From Dictionary to Tensor: A Scalable Multi-View Subspace Clustering Framework with Triple Information Enhancement
Fairness in Federated Learning via Core-Stability
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
Benefits of Permutation-Equivariance in Auction Mechanisms
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks
Differentiable Synthesis of Program Architectures
Variance Reduction for Stochastic Gradient Optimization
Higher-Order Correlation Clustering for Image Segmentation
Thrust: Adaptively Propels Large Language Models with External Knowledge
Domain-Invariant Projection Learning for Zero-Shot Recognition
A Consistent Regularization Approach for Structured Prediction
Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
The Effect of Correlated Input Data on the Dynamics of Learning
Clustering Redemption-Beyond the Impossibility of Kleinberg's Axioms
DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection
BadTrack: A Poison-Only Backdoor Attack on Visual Object Tracking
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs
Boosting Graph Pooling with Persistent Homology
Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
Optimal Best-Arm Identification Methods for Tail-Risk Measures
Deep Insights into Noisy Pseudo Labeling on Graph Data
Estimating the Long-Term Effects of Novel Treatments
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity
Disentangling Voice and Content with Self-Supervision for Speaker Recognition
The Infinite Hidden Markov Model
Jigsaw: Learning to Assemble Multiple Fractured Objects
Going Beyond Heuristics by Imposing Policy Improvement as a Constraint
Verifiable Reinforcement Learning via Policy Extraction
Support Vector Machine Classification with Indefinite Kernels
Link Prediction in Graphs with Autoregressive Features
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Variational Inference via \chi Upper Bound Minimization
Fairness without Harm: An Influence-Guided Active Sampling Approach
Task-Adaptive Neural Network Search with Meta-Contrastive Learning
Quasi-Bayes meets Vines
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
Approximation Algorithms for l 0 -Low Rank Approximation
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity
The route to chaos in routing games: When is price of anarchy too optimistic?
Mitigating Source Bias for Fairer Weak Supervision
Language Induction by Phase Transition in Dynamical Recognizers
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Getting More Juice Out of the SFT Data: Reward Learning from Human Demonstration Improves SFT for LLM Alignment
Thinking Fast and Slow with Deep Learning and Tree Search
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
Incorporating Pragmatic Reasoning Communication into Emergent Language
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization
A SNoW-Based Face Detector
Inferring Network Structure from Co-Occurrences
Spatial Representations in the Parietal Cortex May Use Basis Functions
A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
Language-driven Scene Synthesis using Multi-conditional Diffusion Model
Modeling Complex Cells in an Awake Macaque during Natural Image Viewing
A framework for bilevel optimization that enables stochastic and global variance reduction algorithms
Faster Margin Maximization Rates for Generic Optimization Methods
Graph Neural Networks and Arithmetic Circuits
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning
Bayesian Inference for Structured Spike and Slab Priors
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks
Responsible AI (RAI) Games and Ensembles
Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors
Position Variance, Recurrence and Perceptual Learning
Bandit Linear Control
Score-based Generative Modeling in Latent Space
On Convergence of Polynomial Approximations to the Gaussian Mixture Entropy
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
Alternating optimization of decision trees, with application to learning sparse oblique trees
Natural gradient enables fast sampling in spiking neural networks
Self-Paced Learning with Diversity
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements
Learning Spherical Convolution for Fast Features from 360° Imagery
RanPAC: Random Projections and Pre-trained Models for Continual Learning
A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice
Discriminative K-means for Clustering
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning
Deep Momentum Multi-Marginal Schrödinger Bridge
Rational Kernels
Replicable Uniformity Testing
Data-Driven Clustering via Parameterized Lloyd's Families
Learning Dictionary for Visual Attention
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems
Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Efficient Equivariant Network
Amortized Projection Optimization for Sliced Wasserstein Generative Models
Learning Nearest Neighbor Graphs from Noisy Distance Samples
Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics
Improved Regularization and Robustness for Fine-tuning in Neural Networks
The Fairness-Quality Tradeoff in Clustering
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
Quantum Rational Preferences and Desirability
Variational Memory Encoder-Decoder
FUGAL: Feature-fortified Unrestricted Graph Alignment
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy
Low-shot Object Learning with Mutual Exclusivity Bias
Noise-Aware Differentially Private Regression via Meta-Learning
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
Optoelectronic Implementation of a FitzHugh-Nagumo Neural Model
Heuristic-Guided Reinforcement Learning
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability
Projection-Free Online Convex Optimization via Efficient Newton Iterations
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
The Usefulness of Past Knowledge when Learning a New Task in Deep Neural Networks
Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence
Neural Lighting Simulation for Urban Scenes
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning
Cross Attention Network for Few-shot Classification
Amortized Bayesian Experimental Design for Decision-Making
Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA
DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM
3DCoMPaT200: Language Grounded Large-Scale 3D Vision Dataset for Compositional Recognition
Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction
Storchastic: A Framework for General Stochastic Automatic Differentiation
Regression with reject option and application to kNN
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Experimental Results on Learning Stochastic Memoryless Policies for Partially Observable Markov Decision Processes
Persistent Test-time Adaptation in Recurring Testing Scenarios
Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift
Effective Parallelisation for Machine Learning
Posterior Concentration for Sparse Deep Learning
Targeted Neural Dynamical Modeling
Even Sparser Graph Transformers
Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis
Local Smoothness in Variance Reduced Optimization
Kronecker Determinantal Point Processes
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition
HiCoM: Hierarchical Coherent Motion for Dynamic Streamable Scenes with 3D Gaussian Splatting
A Measure-Theoretic Axiomatisation of Causality
Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance
MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation
GRAND-SLAMIN' Interpretable Additive Modeling with Structural Constraints
Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
AirDelhi: Fine-Grained Spatio-Temporal Particulate Matter Dataset From Delhi For ML based Modeling
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
Finite-Dimensional Approximation of Gaussian Processes
Learning Tractable Probabilistic Models from Inconsistent Local Estimates
Energy-Based Modelling for Discrete and Mixed Data via Heat Equations on Structured Spaces
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence
Contemporary Symbolic Regression Methods and their Relative Performance
Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
Local Curvature Smoothing with Stein's Identity for Efficient Score Matching
Task Discovery: Finding the Tasks that Neural Networks Generalize on
Implicit Bias of Gradient Descent on Linear Convolutional Networks
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning
End-to-End Differentiable Physics for Learning and Control
User-Creator Feature Polarization in Recommender Systems with Dual Influence
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space
A Comprehensive Study on Text-attributed Graphs: Benchmarking and Rethinking
A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding
Sample Complexity of Testing the Manifold Hypothesis
Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts