Statistical Learning
Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking Xi Chen
To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models.
Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios
The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroen-cephalography (EEG) and electromyography (EMG) recordings.
MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities Hao Dong
OOD Detection, we introduce the first-of-its-kind benchmark, MultiOOD, characterized by diverse dataset sizes and varying modality combinations.
Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
Detecting dependencies among variables is a fundamental task across scientific disciplines.
Geometric Analysis of Nonlinear Manifold Clustering
Manifold clustering is an important problem in motion and video segmentation, natural image clustering, and other applications where high-dimensional data lie on multiple, low-dimensional, nonlinear manifolds.
Decision-aware learning for geographical districting
Districting is a complex combinatorial problem that consists in partitioning a geographical area into small districts.
