yusuke1997/dblp-id-title huggingface dataset:
SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers
Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks
Domain Generalization via Entropy Regularization
Feature-Proxy Transformer for Few-Shot Segmentation
Fair Classification with Adversarial Perturbations
Self-similarity Properties of Natural Images
Convex Polytope Trees and its Application to VAE
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation
TrackIME: Enhanced Video Point Tracking via Instance Motion Estimation
FeLMi : Few shot Learning with hard Mixup
Do Not Marginalize Mechanisms, Rather Consolidate!
Building a stable classifier with the inflated argmax
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits
Learning-Augmented Dynamic Submodular Maximization
Single-Image Depth Perception in the Wild
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions
Connectionist Modeling and Parallel Architectures
Maximum-Entropy Fine Grained Classification
Reducing Network Agnostophobia
Learning to be Bayesian without Supervision
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
Multi-relational Poincaré Graph Embeddings
DISCO Nets : DISsimilarity COefficients Networks
Bayesian Transduction
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users
Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
Optimal sub-graphical models
2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
Learning to Screen
A Simple Weight Decay Can Improve Generalization
SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection
Online Convex Optimization with Hard Constraints: Towards the Best of Two Worlds and Beyond
Information Capacity and Robustness of Stochastic Neuron Models
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning
Causal discovery from observational and interventional data across multiple environments
Window-Based Distribution Shift Detection for Deep Neural Networks
Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments
Sparse Winning Tickets are Data-Efficient Image Recognizers
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method
When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
Okapi: Generalising Better by Making Statistical Matches Match
Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods
Interpreting Unsupervised Anomaly Detection in Security via Rule Extraction
Low-Rank Constraints for Fast Inference in Structured Models
Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark
Neural Abstractions
Evaluating the design space of diffusion-based generative models
Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
Fair Clustering Under a Bounded Cost
Decentralized Noncooperative Games with Coupled Decision-Dependent Distributions
Bayesian Query Construction for Neural Network Models
Watch Your Step: Learning Node Embeddings via Graph Attention
NeRF-IBVS: Visual Servo Based on NeRF for Visual Localization and Navigation
Gradient Methods for Online DR-Submodular Maximization with Stochastic Long-Term Constraints
Deep Learning Through the Lens of Example Difficulty
Unsupervised Kernel Dimension Reduction
Kernel Methods Through the Roof: Handling Billions of Points Efficiently
Improved Feature Distillation via Projector Ensemble
SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models
CODA: A Correlation-Oriented Disentanglement and Augmentation Modeling Scheme for Better Resisting Subpopulation Shifts
Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
The Crucial Role of Normalization in Sharpness-Aware Minimization
Consistent Minimization of Clustering Objective Functions
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
Basins of Attraction for Electronic Neural Networks
Learning Safe Policies with Expert Guidance
Policy Optimization with Linear Temporal Logic Constraints
LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning
Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm
Boosted Sparse and Low-Rank Tensor Regression
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
TaSIL: Taylor Series Imitation Learning
Online Selective Classification with Limited Feedback
Estimating the wrong Markov random field: Benefits in the computation-limited setting
RUDDER: Return Decomposition for Delayed Rewards
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization
ODRL: A Benchmark for Off-Dynamics Reinforcement Learning
Attention is All you Need
Approximate Learning of Dynamic Models
Neural Model Checking
Active Ranking without Strong Stochastic Transitivity
Efficient online learning with kernels for adversarial large scale problems
Arbitrary Conditional Distributions with Energy
Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees
Sufficient dimension reduction for classification using principal optimal transport direction
Modeling Tabular data using Conditional GAN
Searching for objects driven by context
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Bayesian Averaging is Well-Temperated
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
Mixtape: Breaking the Softmax Bottleneck Efficiently
Temporal Effective Batch Normalization in Spiking Neural Networks
MAVEN: Multi-Agent Variational Exploration
shapiq: Shapley Interactions for Machine Learning
Regularized Greedy Importance Sampling
Analog Memories in a Balanced Rate-Based Network of E-I Neurons
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)
A New Perspective on Pool-Based Active Classification and False-Discovery Control
Deep Metric Learning with Spherical Embedding
In search of robust measures of generalization
Diffusion Imitation from Observation
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Adaptive Nearest Neighbor Classification Using Support Vector Machines
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
The Exact Sample Complexity Gain from Invariances for Kernel Regression
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes
Temporally Disentangled Representation Learning under Unknown Nonstationarity
Domain Adaptation meets Individual Fairness. And they get along
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
Generating Long-term Trajectories Using Deep Hierarchical Networks
Analytic Solutions to the Formation of Feature-Analysing Cells of a Three-Layer Feedforward Visual Information Processing Neural Net
Neuronal Group Selection Theory: A Grounding in Robotics
MOPO: Model-based Offline Policy Optimization
Summed Weight Neuron Perturbation: An O(N) Improvement Over Weight Perturbation
Boosting Structured Prediction for Imitation Learning
Compete to Compute
Generating Images with Perceptual Similarity Metrics based on Deep Networks
Learning Additive Exponential Family Graphical Models via \ell_{2, 1}-norm Regularized M-Estimation
Contextual semibandits via supervised learning oracles
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
A scalable generative model for dynamical system reconstruction from neuroimaging data
Samba: Severity-aware Recurrent Modeling for Cross-domain Medical Image Grading
Style Transfer from Non-Parallel Text by Cross-Alignment
Theoretical guarantees in KL for Diffusion Flow Matching
IF-Font: Ideographic Description Sequence-Following Font Generation
MAC Advice for facility location mechanism design
Towards Safe Reinforcement Learning with a Safety Editor Policy
Slight Corruption in Pre-training Data Makes Better Diffusion Models
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
Improved Clinical Abbreviation Expansion via Non-Sense-Based Approaches
A New Feature Selection Method Based on Stability Theory - Exploring Parameters Space to Evaluate Classification Accuracy in Neuroimaging Data
On Markov Chain Gradient Descent
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching
E 3 : Exploring Embodied Emotion Through A Large-Scale Egocentric Video Dataset
Putting Bayes to sleep
MMLONGBENCH-DOC: Benchmarking Long-context Document Understanding with Visualizations
High Dimensional Linear Regression using Lattice Basis Reduction
DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks
Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models
Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning
Generalizing to Unseen Domains via Adversarial Data Augmentation
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy
Understanding Multi-phase Optimization Dynamics and Rich Nonlinear Behaviors of ReLU Networks
A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model
Inferring Hybrid Neural Fluid Fields from Videos
Look Ma, No Hands: Analyzing the Monotonic Feature Abstraction for Text Classification
Orientation-Selective aVLSI Spiking Neurons
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs
Learning Structured Sparsity in Deep Neural Networks
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
ResT: An Efficient Transformer for Visual Recognition
Geometry Awakening: Cross-Geometry Learning Exhibits Superiority over Individual Structures
Combined discriminative and generative articulated pose and non-rigid shape estimation
Sparse Modular Activation for Efficient Sequence Modeling
Goal Conditioned Reinforcement Learning for Photo Finishing Tuning
On Affine Homotopy between Language Encoders
Double Randomized Underdamped Langevin with Dimension-Independent Convergence Guarantee
Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning
Learning Multiple Tasks in Parallel with a Shared Annotator
Autoinverse: Uncertainty Aware Inversion of Neural Networks
Decoding with Value Networks for Neural Machine Translation
Incentivizing Quality Text Generation via Statistical Contracts
Relative gradient optimization of the Jacobian term in unsupervised deep learning
An Adaptive WTA using Floating Gate Technology
Conformalized matrix completion
Logarithmic Pruning is All You Need
Cluster Randomized Designs for One-Sided Bipartite Experiments
Bayesian Model Comparison by Monte Carlo Chaining
Optimizing Classifers for Imbalanced Training Sets
ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
On Poisson Graphical Models
Attentive State-Space Modeling of Disease Progression
Learning Shuffle Ideals Under Restricted Distributions
Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks
Multiple Instance Boosting for Object Detection
Neural Sparse Voxel Fields
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
Im2Text: Describing Images Using 1 Million Captioned Photographs
Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
A Marginalized Particle Gaussian Process Regression
Topographic Transformation as a Discrete Latent Variable
Multiplicative Weights Updates with Constant Step-Size in Graphical Constant-Sum Games
Explaining Hyperparameter Optimization via Partial Dependence Plots
Modeling the effects of memory on human online sentence processing with particle filters
FLSL: Feature-level Self-supervised Learning
Mixed Membership Stochastic Blockmodels
Optimal Number of Choices in Rating Contexts
Addressing the Real-world Class Imbalance Problem in Dermatology
Semantic Image Synthesis with Unconditional Generator
SpelsNet: Surface Primitive Elements Segmentation by B-Rep Graph Structure Supervision
Localized Adaptive Risk Control
Collective Inference on Markov Models for Modeling Bird Migration
Supervised Learning with Growing Cell Structures
D-LLM: A Token Adaptive Computing Resource Allocation Strategy for Large Language Models
Improved Generation of Adversarial Examples Against Safety-aligned LLMs
Are all Frames Equal? Active Sparse Labeling for Video Action Detection
Learning (Very) Simple Generative Models Is Hard
Topic-Conversation Relevance (TCR) Dataset and Benchmarks
All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
AiluRus: A Scalable ViT Framework for Dense Prediction
Supervised Graph Inference
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer
Mind the Graph When Balancing Data for Fairness or Robustness
RUBi: Reducing Unimodal Biases for Visual Question Answering
Worst-Case Analysis for Randomly Collected Data
Equivariant Graph Hierarchy-Based Neural Networks
Applying Metric-Trees to Belief-Point POMDPs
Globally Convergent Policy Search for Output Estimation
Uncalibrated Models Can Improve Human-AI Collaboration
Debiased Self-Training for Semi-Supervised Learning
Exponential Concentration for Mutual Information Estimation with Application to Forests
Dynamic Trace Estimation
Clustering with the Connectivity Kernel
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits
Human and Ideal Observers for Detecting Image Curves
Spectral Learning of General Weighted Automata via Constrained Matrix Completion
PiCoDes: Learning a Compact Code for Novel-Category Recognition
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings
The Noisy Euclidean Traveling Salesman Problem and Learning
Minimizing Statistical Bias with Queries
New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
AugMax: Adversarial Composition of Random Augmentations for Robust Training
Composable Coresets for Determinant Maximization: Greedy is Almost Optimal
Support Vector Machines for Multiple-Instance Learning
Mapping State Space using Landmarks for Universal Goal Reaching
Unsupervised Learning of Group Invariant and Equivariant Representations
HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies
Neural Basis Models for Interpretability
Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts
Automatic Learning Rate Maximization in Large Adaptive Machines
Single-Stage Visual Query Localization in Egocentric Videos
MLLM-CompBench: A Comparative Reasoning Benchmark for Multimodal LLMs
Regularized Nonlinear Acceleration
Spectral-Risk Safe Reinforcement Learning with Convergence Guarantees
Bit Efficient Quantization for Deep Neural Networks
Are Sixteen Heads Really Better than One?
Differentially Private Sampling from Distributions
Meta-Reinforcement Learning of Structured Exploration Strategies
Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter
Do Different Tracking Tasks Require Different Appearance Models?
Object-Centric Slot Diffusion
Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views
Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems
Learning Local Search Heuristics for Boolean Satisfiability
Learning Sparse Prototypes for Text Generation
Neural Star Domain as Primitive Representation
Policy Gradient With Serial Markov Chain Reasoning
Learning about Canonical Views from Internet Image Collections
Katakomba: Tools and Benchmarks for Data-Driven NetHack
Toward Provably Correct Feature Selection in Arbitrary Domains
Causal Contrastive Learning for Counterfactual Regression Over Time
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity
The Multidimensional Wisdom of Crowds
Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases
Neural Multisensory Scene Inference
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks
Differentially Private Uniformly Most Powerful Tests for Binomial Data
Variational Inference for Diffusion Processes
Provable Acceleration of Nesterov's Accelerated Gradient for Asymmetric Matrix Factorization and Linear Neural Networks
Bidirectional Learning for Offline Infinite-width Model-based Optimization
Max-value Entropy Search for Multi-Objective Bayesian Optimization
Test-Time Distribution Normalization for Contrastively Learned Visual-language Models
Estimating Fluctuations in Neural Representations of Uncertain Environments
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns
Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks
AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
Resource-Adaptive Federated Learning with All-In-One Neural Composition
Neurally Plausible Reinforcement Learning of Working Memory Tasks
MADE: Exploration via Maximizing Deviation from Explored Regions
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Boltzmann Chains and Hidden Markov Models
Scalable Training of Mixture Models via Coresets
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition
Fast Channel Simulation via Error-Correcting Codes
Bias Amplification in Language Model Evolution: An Iterated Learning Perspective
The Power of Approximation: A Comparison of Activation Functions
AdjointDEIS: Efficient Gradients for Diffusion Models
FastDrag: Manipulate Anything in One Step
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks
Attentional Modulation of Human Pattern Discrimination Psychophysics Reproduced by a Quantitative Model
Unsupervised Sound Separation Using Mixture Invariant Training
Joint Modeling of a Matrix with Associated Text via Latent Binary Features
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Interactive Visual Reasoning under Uncertainty
Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference
Quantifying the Gain in Weak-to-Strong Generalization
Brain Dissection: fMRI-trained Networks Reveal Spatial Selectivity in the Processing of Natural Images
On the (In)fidelity and Sensitivity of Explanations
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
Generating Origin-Destination Matrices in Neural Spatial Interaction Models
PAC-Bayes Learning Bounds for Sample-Dependent Priors
Margin Maximizing Loss Functions
Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies
Hierarchical Graph Representation Learning with Differentiable Pooling
applying Divide and Conquer to Large Scale Pattern Recognition Tasks
Environment Diversification with Multi-head Neural Network for Invariant Learning
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
Distribution-Informed Neural Networks for Domain Adaptation Regression
Efficient and Private Marginal Reconstruction with Local Non-Negativity
Provable Non-linear Inductive Matrix Completion
A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices
Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation
Enhancing Minority Classes by Mixing: An Adaptative Optimal Transport Approach for Long-tailed Classification
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
Discriminative Batch Mode Active Learning
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Feature Selection in the Contrastive Analysis Setting
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
FairLISA: Fair User Modeling with Limited Sensitive Attributes Information
Persistent Homology for High-dimensional Data Based on Spectral Methods
Data-Driven Network Neuroscience: On Data Collection and Benchmark
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization
Just-In-Time Learning for Fast and Flexible Inference
Finding Structure in Reinforcement Learning
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
Online Adaptation of Language Models with a Memory of Amortized Contexts
Differentially Private Set Representations
Filter Selection Model for Generating Visual Motion Signals
Contrastive Learning with Adversarial Examples
Adaptive Regularization of Weight Vectors
Bringing regularized optimal transport to lightspeed: a splitting method adapted for GPUs
Train-by-Reconnect: Decoupling Locations of Weights from Their Values
Robust Learning with Progressive Data Expansion Against Spurious Correlation
Constrained Binary Decision Making
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Eliciting Categorical Data for Optimal Aggregation
Better than least squares: comparison of objective functions for estimating linear-nonlinear models
Autoregressive Search Engines: Generating Substrings as Document Identifiers
Equal Opportunity of Coverage in Fair Regression
HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning
ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection
Measuring and Reducing Model Update Regression in Structured Prediction for NLP
Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems
Influence-Augmented Online Planning for Complex Environments
Audio-Driven Co-Speech Gesture Video Generation
Model Interpretability through the lens of Computational Complexity
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
Reduced Policy Optimization for Continuous Control with Hard Constraints
Quadratic Video Interpolation
How many samples is a good initial point worth in Low-rank Matrix Recovery?
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
A Closer Look at Weakly-Supervised Audio-Visual Source Localization