Large language models (LLMs) have recently demonstrated remarkable
capab...
The significance of novice researchers acquiring proficiency in writing
...
We study the problem of collaboratively learning least squares estimates...
In this work we study systems consisting of a group of moving particles....
We present a machine learning (ML) framework for large-scale dynamical
s...
Predicting the behaviors of pedestrian crowds is of critical importance ...
Random matrix theory has become a widely useful tool in high-dimensional...
After the outbreak of COVID-19, mask detection, as the most convenient a...
The construction of most supervised learning datasets revolves around
co...
We examine the necessity of interpolation in overparameterized models, t...
We study a class of deterministic flows in ℝ^d× k,
parametrized by a ran...
Deep learning method has attracted tremendous attention to handle fluid
...
Natural policy gradient (NPG) methods are among the most widely used pol...
A fundamental task that spans numerous applications is inference and
unc...
This paper is concerned with a curious phenomenon in spectral estimation...