63 citations · 256 across the 18 of their papers we have counts for
18 papers
On the Equivalence between Implicit and Explicit Neural Networks: A High-dimensional Viewpoint
Zenan Ling, Zhenyu Liao, Robert C. Qiu
Implicit neural networks have demonstrated remarkable success in various tasks. However, there is a lack of theoretical analysis of the connections and differences between implicit…
Codebook Configuration for RIS-aided Systems via Implicit Neural Representations
Huiying Yang, Rujing Xiong, Yao Xiao +4
Reconfigurable Intelligent Surface (RIS) is envisioned to be an enabling technique in 6G wireless communications. By configuring the reflection beamforming codebook, RIS focuses si…
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…
An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images
Zenan Ling, Robert C. Qiu, Zhijian Jin +4
The location of broken insulators in aerial images is a challenging task. This paper, focusing on the self-blast glass insulator, proposes a deep learning solution. We address the…
Early Anomaly Detection and Location in Distribution Network: A Data-Driven Approach
Xin Shi, Robert Qiu, Xing He +3
The measurement data collected from the supervisory control and data acquisition (SCADA) system installed in distribution network can reflect the operational state of the network e…
A Data-driven Approach to Multi-event Analytics in Large-scale Power Systems Using Factor Model
Fan Yang, Xing He, Robert Caiming Qiu +1
Multi-event detection and recognition in real time is of challenge for a modern grid as its feature is usually non-identifiable. Based on factor model, this paper porposes a data-d…
Invisible Units Detection and Estimation Based on Random Matrix Theory
Xing He, Lei Chu, Robert C. Qiu +3
Invisible units mainly refer to small-scale units that are not monitored by, and thus are not visible to utilities. Integration of these invisible units into power systems does sig…
Spatio-Temporal Big Data Analysis for Smart Grids Based on Random Matrix Theory: A Comprehensive Study
Robert Qiu, Lei Chu, Xing He +2
A cornerstone of the smart grid is the advanced monitorability on its assets and operations. Increasingly pervasive installation of the phasor measurement units (PMUs) allows the s…
Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams
Lei Chu, Robert Qiu, Haichun Liu +3
Recently, there has been a growing interest in monitoring brain activity for individual recognition system. So far these works are mainly focussing on single channel data or fragme…
A Novel Approach for Big Data Analytics in Future Grids Based on Free Probability
Zenan Ling, Robert C. Qiu, Xing He +1
Based on the random matrix model, we can build statistical models using massive datasets across the power grid, and employ hypothesis testing for anomaly detection. First, the aim…
A Novel Data-Driven Situation Awareness Approach for Future Grids--Using Large Random Matrices for Big Data Modeling
Xing He, Lei Chu, Robert C. Qiu +2
Data-driven approaches, when tasked with situation awareness, are suitable for complex grids with massive datasets. It is a challenge, however, to efficiently turn these massive da…
Massive Streaming PMU Data Modeling and Analytics in Smart Grid State Evaluation Based on Multiple High-Dimensional Covariance Tests
Lei Chu, Robert Qiu, Xing He +2
The analogous deployment of phase measurement units (PMUs), the increase of data quantum and the deregulation of energy market, all call for the robust state evaluation in large sc…
Designing for Situation Awareness of Future Power Grids: An Indicator System Based on Linear Eigenvalue Statistics of Large Random Matrices
Xing He, Robert C. Qiu, Qian Ai +3
Future power grids are fundamentally different from current ones, both in size and in complexity; this trend imposes challenges for situation awareness (SA) based on classical indi…