5 papers
Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
Bohan Zhang, Zihao Wang, Hengyu Fu +1
In deep learning, a central issue is to understand how neural networks efficiently learn high-dimensional features. To this end, we explore the gradient descent learning of a gener…
List Replicable Reinforcement Learning
Bohan Zhang, Michael Chen, A. Pavan +3
Replicability is a fundamental challenge in reinforcement learning (RL), as RL algorithms are empirically observed to be unstable and sensitive to variations in training conditions…
Nonlinear shift along the sensorimotor-association-axis in brain responses to task performance
Fan Cao, Yuqi Yuan, Xiaohui Yan +2
In the literature of cognitive neuroscience, researchers tend to assume a linear relationship between brain activation level and task performance; however, controversial findings h…
Interaction as Intelligence: Deep Research With Human-AI Partnership
Lyumanshan Ye, Xiaojie Cai, Xinkai Wang +23
This paper introduces "Interaction as Intelligence" research series, presenting a reconceptualization of human-AI relationships in deep research tasks. Traditional approaches treat…
A Systematic Review of Machine Learning Methods for Multimodal EEG Data in Clinical Application
Siqi Zhao, Wangyang Li, Xiru Wang +6
Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces…