4 citations · 8 across the 13 of their papers we have counts for
6 papers · 1 filter
EMoE: Eigenbasis-Guided Routing for Mixture-of-Experts
Anzhe Cheng, Shukai Duan, Shixuan Li +5
The relentless scaling of deep learning models has led to unsustainable computational demands, positioning Mixture-of-Experts (MoE) architectures as a promising path towards greate…
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks
Anzhe Cheng, Zhenkun Wang, Chenzhong Yin +5
Backpropagation (BP) has been a successful optimization technique for deep learning models. However, its limitations, such as backward- and update-locking, and its biological impla…
Leader-Follower Neural Networks with Local Error Signals Inspired by Complex Collectives
Chenzhong Yin, Mingxi Cheng, Xiongye Xiao +4
The collective behavior of a network with heterogeneous, resource-limited information processing units (e.g., group of fish, flock of birds, or network of neurons) demonstrates hig…
Fractional dynamics foster deep learning of COPD stage prediction
Chenzhong Yin, Mihai Udrescu, Gaurav Gupta +6
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends…
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Xiongye Xiao, Defu Cao, Ruochen Yang +5
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes. Recently, neural operators have shown the ability to solve…
Non-Markovian Reinforcement Learning using Fractional Dynamics
Gaurav Gupta, Chenzhong Yin, Jyotirmoy V. Deshmukh +1
Reinforcement learning (RL) is a technique to learn the control policy for an agent that interacts with a stochastic environment. In any given state, the agent takes some action, a…