38 citations · 64 across the 7 of their papers we have counts for
10 papers
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
Optimization Induced Equilibrium Networks
Xingyu Xie, Qiuhao Wang, Zenan Ling +4
Implicit equilibrium models, i.e., deep neural networks (DNNs) defined by implicit equations, have been becoming more and more attractive recently. In this paper, we investigate an…
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
Zenan Ling, Haotian Ma, Yu Yang +3
In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of…
Spatio-Temporal Correlation Analysis of Online Monitoring Data for Anomaly Detection and Location in Distribution Networks
Xin Shi, Robert Qiu, Zenan Ling +3
The online monitoring data in distribution networks contain rich information on the running states of the networks. By leveraging the data, this paper proposes a spatio-temporal co…
Spectrum concentration in deep residual learning: a free probability approach
Zenan Ling, Xing He, Robert C. Qiu
We revisit the initialization of deep residual networks (ResNets) by introducing a novel analytical tool in free probability to the community of deep learning. This tool deals with…
A New Approach of Exploiting Self-Adjoint Matrix Polynomials of Large Random Matrices for Anomaly Detection and Fault Location
Zenan Ling, Robert C. Qiu, Xing He +1
Synchronized measurements of a large power grid enable an unprecedented opportunity to study the spatialtemporal correlations. Statistical analytics for those massive datasets star…