yusuke1997/dblp-id-title huggingface dataset:
Proceedings of The Cell Segmentation Challenge in Multi-modality High-Resolution Microscopy Images, Competitions in Neural Information Processing Systems, December 7, 2022, New Orleans, LA, USA
Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing
Marrying Causal Representation Learning with Dynamical Systems for Science
Diffusion Schrödinger Bridge Matching
Bayesian Online Natural Gradient (BONG)
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
For Valid Generalization the Size of the Weights is More Important than the Size of the Network
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
Reproducibility in Optimization: Theoretical Framework and Limits
Container: Context Aggregation Networks
The Broad Optimality of Profile Maximum Likelihood
Distributed Inference and Fine-tuning of Large Language Models Over The Internet
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization
Near-Linear Time Algorithm for the Chamfer Distance
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction
Rethinking the Pruning Criteria for Convolutional Neural Network
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback
A Silicon Axon
Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations
Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming
Break It Down: Evidence for Structural Compositionality in Neural Networks
Locally private online change point detection
Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation
Pessimistic Backward Policy for GFlowNets
The Label Complexity of Active Learning from Observational Data
Uniform Convergence of Gradients for Non-Convex Learning and Optimization
Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees
Nearly-Tight and Oblivious Algorithms for Explainable Clustering
Learning to Control an Unstable System with Forward Modeling
Sampling Techniques for Kernel Methods
Boosting versus Covering
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
S-SOS: Stochastic Sum-Of-Squares for Parametric Polynomial Optimization
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression
Swapout: Learning an ensemble of deep architectures
Decomposing Isotonic Regression for Efficiently Solving Large Problems
Continual Audio-Visual Sound Separation
The Neural Dynamics of Visual Processing in Monkey Extrastriate Cortex: A Comparison between Univariate and Multivariate Techniques
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Copula Processes
Training Multilayer Perceptrons with the Extende Kalman Algorithm
Reinforcement Learning for Spoken Dialogue Systems
Strategic Data Sharing between Competitors
Occam's razor is insufficient to infer the preferences of irrational agents
Efficient Forward Architecture Search
Robust Low Rank Kernel Embeddings of Multivariate Distributions
CUP: Critic-Guided Policy Reuse
Learning Abstract Options
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers
Implicit Differentiation by Perturbation
A Prototype for Automatic Recognition of Spontaneous Facial Actions
Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation
Attack-Resilient Image Watermarking Using Stable Diffusion
Robust low-rank training via approximate orthonormal constraints
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
A Unifying View of Optimism in Episodic Reinforcement Learning
Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery
Eye Movements for Reward Maximization
Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions
Algorithmic Instabilities of Accelerated Gradient Descent
Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019
Variational Inference for Gaussian Process Models with Linear Complexity
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks
CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation
SlimSAM: 0.1% Data Makes Segment Anything Slim
Memory Efficient Adaptive Optimization
$(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations
Grouping Contours Via a Related Image
Learning Global Transparent Models consistent with Local Contrastive Explanations
Robust and Communication-Efficient Collaborative Learning
Failure-Aware Gaussian Process Optimization with Regret Bounds
Different Cortico-Basal Ganglia Loops Specialize in Reward Prediction at Different Time Scales
Practical Locally Private Heavy Hitters
Using Analytic QP and Sparseness to Speed Training of Support Vector Machines
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making
Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization
Statistical Modeling of Images with Fields of Gaussian Scale Mixtures
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Matching neural paths: transfer from recognition to correspondence search
An Empirical Study on The Properties of Random Bases for Kernel Methods
Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Inference
DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
Differentially Private Equivalence Testing for Continuous Distributions and Applications
DreamSparse: Escaping from Plato's Cave with 2D Diffusion Model Given Sparse Views
ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
Discrete MDL Predicts in Total Variation
Is Learning in Games Good for the Learners?
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
Individualized Dosing Dynamics via Neural Eigen Decomposition
MIMEx: Intrinsic Rewards from Masked Input Modeling
Deep Networks Provably Classify Data on Curves
Graph Diffusion Policy Optimization
Ode to an ODE
Uncovering and Quantifying Social Biases in Code Generation
InterpBench: Semi-Synthetic Transformers for Evaluating Mechanistic Interpretability Techniques
Beyond the Signs: Nonparametric Tensor Completion via Sign Series
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization
Why Transformers Need Adam: A Hessian Perspective
FrugalML: How to use ML Prediction APIs more accurately and cheaply
Avoiding False Positive in Multi-Instance Learning
Density Level Detection is Classification
The Noisy Power Method: A Meta Algorithm with Applications
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
Hierarchical Randomized Smoothing
Evaluating neuronal codes for inference using Fisher information
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Welfare Guarantees from Data
Elementary Estimators for Graphical Models
ForecastPFN: Synthetically-Trained Zero-Shot Forecasting
Modeling homophily and stochastic equivalence in symmetric relational data
First is Better Than Last for Language Data Influence
Should We Learn Most Likely Functions or Parameters?
Deep Generative Markov State Models
Pylon Model for Semantic Segmentation
Approximating Semidefinite Programs in Sublinear Time
A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks
HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach
Top-KAST: Top-K Always Sparse Training
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation
A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs
Iterative Refinement of the Approximate Posterior for Directed Belief Networks
Reliable training and estimation of variance networks
Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms
Empirical Entropy Manipulation for Real-World Problems
Advancing Bayesian Optimization via Learning Correlated Latent Space
Building powerful and equivariant graph neural networks with structural message-passing
Distributionally Robust Parametric Maximum Likelihood Estimation
Triangulation by Continuous Embedding
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Synchronized Auditory and Cognitive 40 Hz Attentional Streams, and the Impact of Rhythmic Expectation on Auditory Scene Analysis
Pitfalls in EEG-Based Brain Effective Connectivity Analysis
PCF-GAN: generating sequential data via the characteristic function of measures on the path space
Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
Let Images Give You More: Point Cloud Cross-Modal Training for Shape Analysis
Fast and Provably Good Seedings for k-Means
Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation
Robust Semi-Supervised Learning when Not All Classes have Labels
Active Perception for Grasp Detection via Neural Graspness Field
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Commonsense and Semantic-Guided Navigation through Language in Embodied Environment
EAGLE: Efficient Adaptive Geometry-based Learning in Cross-view Understanding
Meta-Learning with Adaptive Hyperparameters
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
End-to-End Video Semantic Segmentation in Adverse Weather using Fusion Blocks and Temporal-Spatial Teacher-Student Learning
Learning Markov State Abstractions for Deep Reinforcement Learning
Results and findings of the 2021 Image Similarity Challenge
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
Sparse PCA via Bipartite Matchings
Iteration Head: A Mechanistic Study of Chain-of-Thought
Modeling image patches with a directed hierarchy of Markov random fields
Stable and low-precision training for large-scale vision-language models
A Bayesian Approach to Diffusion Models of Decision-Making and Response Time
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
Causal Inference through a Witness Protection Program
Predictive State Recurrent Neural Networks
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal
Turing Computation with Recurrent Artificial Neural Networks
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions
Speculative Decoding with Big Little Decoder
M4Singer: A Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus
Learning Anchor Planes for Classification
Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks
Learning to Draw: Emergent Communication through Sketching
Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation
Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning
Pulse-Firing Neural Chips for Hundreds of Neurons
TETRIS: TilE-matching the TRemendous Irregular Sparsity
Neural Net Receivers in Multiple Access-Communications
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
CondConv: Conditionally Parameterized Convolutions for Efficient Inference
Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation
Representational Strengths and Limitations of Transformers
Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness
A Spectral Algorithm for Latent Dirichlet Allocation
The Tunnel Effect: Building Data Representations in Deep Neural Networks
Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers
Multi-View Learning of Word Embeddings via CCA
Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases
First order expansion of convex regularized estimators
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation
Perceptual Score: What Data Modalities Does Your Model Perceive?
$\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models
Active Learning Polynomial Threshold Functions
Hierarchical Learning of Dimensional Biases in Human Categorization
Learning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds
Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval
M-Statistic for Kernel Change-Point Detection
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
Faster and Scalable Algorithms for Densest Subgraph and Decomposition
Ordered Momentum for Asynchronous SGD
Inference in Graphical Models via Semidefinite Programming Hierarchies
Discovering Design Concepts for CAD Sketches
Neural Implementation of Motivated Behavior: Feeding in an Artificial Insect
BIGOS V2 Benchmark for Polish ASR: Curated Datasets and Tools for Reproducible Evaluation
SE(3) Equivariant Convolution and Transformer in Ray Space
End-to-end Stochastic Optimization with Energy-based Model
UltraMedical: Building Specialized Generalists in Biomedicine
Sampling for Bayesian Program Learning
Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence
Deep Fisher Networks for Large-Scale Image Classification
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth
Prefix-Tree Decoding for Predicting Mass Spectra from Molecules
Cold Case: The Lost MNIST Digits
Efficient Policy Evaluation Across Multiple Different Experimental Datasets
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization
Geoclidean: Few-Shot Generalization in Euclidean Geometry
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization
Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments
Geometric Clustering Using the Information Bottleneck Method
Active Learning from Crowd in Document Screening
Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning
Towards Better Generalization of Adaptive Gradient Methods
Fully Understanding The Hashing Trick
On Herding and the Perceptron Cycling Theorem
Result Analysis of the NIPS 2003 Feature Selection Challenge
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion
Consensus Learning with Deep Sets for Essential Matrix Estimation
Decoupling Features in Hierarchical Propagation for Video Object Segmentation
Transfer learning for text classification
Robotic Grasping of Novel Objects
Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining
Graph Coarsening with Message-Passing Guarantees
Learning to Elect
Self-Supervised Image Restoration with Blurry and Noisy Pairs
Retrospective on the 2021 MineRL BASALT Competition on Learning from Human Feedback
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow
Learning from Stochastically Revealed Preference
Unleash the Potential of Image Branch for Cross-modal 3D Object Detection
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
Generalizing Nonlinear ICA Beyond Structural Sparsity
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
The Belief in TAP
Prioritizing Samples in Reinforcement Learning with Reducible Loss
E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
Saliency Based on Information Maximization
"Neurolocator", A Model of Attention
Inference for Batched Bandits
DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion
Fine-Grained Visual Prompting
Over-complete representations on recurrent neural networks can support persistent percepts
Benign Overfitting in Two-layer Convolutional Neural Networks
Beyond black box densities: Parameter learning for the deviated components
BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?
Learning Interface Conditions in Domain Decomposition Solvers
The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Trade
Mutual Information Estimation via Normalizing Flows
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
Amortized Bethe Free Energy Minimization for Learning MRFs
Maximum Margin Multi-Instance Learning
Adaptation Accelerating Sampling-based Bayesian Inference in Attractor Neural Networks
Convex Deep Learning via Normalized Kernels
(S)GD over Diagonal Linear Networks: Implicit bias, Large Stepsizes and Edge of Stability
Alternating minimization for dictionary learning with random initialization
Smoothly Bounding User Contributions in Differential Privacy
Gradient Surgery for Multi-Task Learning
Inductive Representation Learning on Large Graphs
From global to local MDI variable importances for random forests and when they are Shapley values
Off-Policy Selection for Initiating Human-Centric Experimental Design
On Testing of Samplers
Variational Minimax Estimation of Discrete Distributions under KL Loss
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
Optimal Positive Generation via Latent Transformation for Contrastive Learning
Distributed Probabilistic Learning for Camera Networks with Missing Data
One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
Bayesian estimation of orientation preference maps
Learning Invariances in Neural Networks from Training Data
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet
Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch
Skill Characterization Based on Betweenness
Compositional Explanations of Neurons
Differentially Private Model Personalization
An Estimation-Theoretic Framework for the Presentation of Multiple Stimuli
Unified Optimal Transport Framework for Universal Domain Adaptation
Factor Graph Grammars
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
RangePerception: Taming LiDAR Range View for Efficient and Accurate 3D Object Detection
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models
Task Agnostic and Task Specific Self-Supervised Learning from Speech with LeBenchmark
How Sampling Impacts the Robustness of Stochastic Neural Networks
Learning Linear Programs from Optimal Decisions
Cobot: A Social Reinforcement Learning Agent
3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model
Segment Any Change
μP 2 : Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
HOPE: Shape Matching Via Aligning Different K-hop Neighbourhoods
A Geometric take on Metric Learning
Local Causal Discovery of Direct Causes and Effects
Zero-shot Learning via Simultaneous Generating and Learning
Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms
Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization
Evolution and Learning in Neural Networks
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation
BiScope: AI-generated Text Detection by Checking Memorization of Preceding Tokens
VarGrad: A Low-Variance Gradient Estimator for Variational Inference
Riemannian approach to batch normalization
ConceptMix: A Compositional Image Generation Benchmark with Controllable Difficulty
The Concave-Convex Procedure (CCCP)
Norm matters: efficient and accurate normalization schemes in deep networks
Making Templates Rotationally Invariant. An Application to Rotated Digit Recognition
Testing Closeness With Unequal Sized Samples
Beyond Gaussian Processes: On the Distributions of Infinite Networks
Counterfactual harm
LG-VQ: Language-Guided Codebook Learning
Non-Stochastic Bandit Slate Problems
Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples
Neural Network Ensembles, Cross Validation, and Active Learning
The Expressive Capacity of State Space Models: A Formal Language Perspective
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Dual Swap Disentangling
Community size effect in artificial learning systems
Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings
Dual-Tree Fast Gauss Transforms
Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization
Potential Boosters?
Towards Safe Concept Transfer of Multi-Modal Diffusion via Causal Representation Editing
Natural continual learning: success is a journey, not (just) a destination
Residual Multiplicative Filter Networks for Multiscale Reconstruction
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
Learning Graph Models for Retrosynthesis Prediction
Ladder Variational Autoencoders
TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation
Oracle-Efficient Differentially Private Learning with Public Data
Fast Sparse Gaussian Process Methods: The Informative Vector Machine
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
Computing with Almost Optimal Size Neural Networks
Latent Bayesian melding for integrating individual and population models
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
Bounding training data reconstruction in DP-SGD
Linear Dynamical Systems as a Core Computational Primitive
Optimality of Reinforcement Learning Algorithms with Linear Function Approximation
Coherence-free Entrywise Estimation of Eigenvectors in Low-rank Signal-plus-noise Matrix Models
Learning first-order Markov models for control
Towards Understanding Evolving Patterns in Sequential Data
Just Add $100 More: Augmenting Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem
The Adaptive Complexity of Maximizing a Gross Substitutes Valuation