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Weilin Cong

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Do We Really Need Complicated Model Architectures For Temporal Networks?

Feb 22, 2023
Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, Mehrdad Mahdavi

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Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection

Feb 17, 2023
Weilin Cong, Mehrdad Mahdavi

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Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks

Dec 07, 2021
Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Mahmut T. Kandemir, Anand Sivasubramaniam

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Dynamic Graph Representation Learning via Graph Transformer Networks

Nov 19, 2021
Weilin Cong, Yanhong Wu, Yuandong Tian, Mengting Gu, Yinglong Xia, Mehrdad Mahdavi, Chun-cheng Jason Chen

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On Provable Benefits of Depth in Training Graph Convolutional Networks

Oct 28, 2021
Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi

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On the Importance of Sampling in Learning Graph Convolutional Networks

Mar 03, 2021
Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi

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Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks

Jun 24, 2020
Weilin Cong, Rana Forsati, Mahmut Kandemir, Mehrdad Mahdavi

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Scene Graph Generation via Conditional Random Fields

Nov 20, 2018
Weilin Cong, William Wang, Wang-Chien Lee

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Improved Face Detection and Alignment using Cascade Deep Convolutional Network

Jul 28, 2017
Weilin Cong, Sanyuan Zhao, Hui Tian, Jianbing Shen

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