7 papers
On the Nonasymptotic Scaling Guarantee of Hyperparameter Estimation in Inhomogeneous, Weakly-Dependent Complex Network Dynamical Systems
Yi Yu, Yubo Hou, Yinchong Wang +3
Hierarchical Bayesian models are increasingly used in large, inhomogeneous complex network dynamical systems by modeling parameters as draws from a hyperparameter-governed distribu…
Automated Optimization Modeling via a Localizable Error-Driven Perspective
Weiting Liu, Han Wu, Yufei Kuang +4
Automated optimization modeling via Large Language Models (LLMs) has emerged as a promising approach to assist complex human decision-making. While post-training has become a pivot…
Dark Signals in the Brain: Augment Brain Network Dynamics to the Complex-valued Field
Jiangnan Zhang, Chengyuan Qian, Wenlian Lu +3
Recordings of brain activity, such as functional MRI (fMRI), provide low-dimensional, indirect observations of neural dynamics evolving in high-dimensional, unobservable spaces. Em…
EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios
Yubo Hou, Zhengxin Zhang, Ziyi Wang +3
Current methodologies typically integrate biophysical brain models with functional magnetic resonance imaging(fMRI) data - while offering millimeter-scale spatial resolution (0.5-2…
Stochastic Forward-Forward Learning through Representational Dimensionality Compression
Zhichao Zhu, Yang Qi, Hengyuan Ma +2
The Forward-Forward (FF) learning algorithm provides a bottom-up alternative to backpropagation (BP) for training neural networks, relying on a layer-wise "goodness" function with…
Optimal signal transmission and timescale diversity in a model of human brain operating near criticality
Yang Qi, Jiexiang Wang, Weiyang Ding +4
Cortical neurons exhibit a hierarchy of timescales across brain regions in response to input stimuli, which is thought to be crucial for information processing of different tempora…