Deep Learning
An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling Shenbao Y u
By exploiting students' response logs, SCM aims to predict their exercise
Newton Informed Neural Operator for Solving Nonlinear Partial Differential Equations
These methods can be broadly categorized into two types: function learning and operator learning approaches. In function learning, the goal is to directly learn the solution.
Imitation learning (IL) aims to mimic the behavior of an expert in a sequential decision making task by learning from demonstrations, and has been widely applied
Full experimental details are provided in Appendix C.
Learning the Latent Causal Structure for Modeling Label Noise
In label-noise learning, the noise transition matrix reveals how an instance transitions from its clean label to its noisy label. Accurately estimating an instance's noise transition matrix is crucial for estimating its clean label.
EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection
This study proposes a novel framework, Event-based Graph Spatiotemporal Sensitive Transformer (EGSST), for the exploitation of spatial and temporal properties of event data.