Weilin Cong

According to our database, Weilin Cong authored at least 16 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method.

Weilin Cong, Jian Kang, Hanghang Tong, Mehrdad Mahdavi

CoRR, 2024

2023

Understanding the Structural Components Behind the Psychological Effects of Autonomous Sensory Meridian Response (ASMR) With Machine Learning and Experimental Methods.

Ryan Tan, Heather Shoenberger, Weilin Cong, Mehrdad Mahdavi

J. Media Psychol. Theor. Methods Appl., 2023

BeMap: Balanced Message Passing for Fair Graph Neural Network.

Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong

CoRR, 2023

DyFormer : A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability.

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

Proceedings of the 2023 SIAM International Conference on Data Mining, 2023

Do We Really Need Complicated Model Architectures For Temporal Networks?

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Weilin Cong, Mehrdad Mahdavi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Predicting Protein-Ligand Docking Structure with Graph Neural Network.

Huaipan Jiang, Jian Wang, Weilin Cong, Yihe Huang, Morteza Ramezani, Anup Sarma, Nikolay V. Dokholyan, Mehrdad Mahdavi, Mahmut T. Kandemir

J. Chem. Inf. Model., 2022

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

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Dynamic Graph Representation Learning via Graph Transformer Networks.

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

CoRR, 2021

On the Importance of Sampling in Learning Graph Convolutional Networks.

Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi

CoRR, 2021

On Provable Benefits of Depth in Training Graph Convolutional Networks.

Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Encrypted rich-data steganography using generative adversarial networks.

Dule Shu, Weilin Cong, Jiaming Chai, Conrad S. Tucker

Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning, 2020

GCN meets GPU: Decoupling "When to Sample" from "How to Sample".

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Weilin Cong, Rana Forsati, Mahmut T. Kandemir, Mehrdad Mahdavi

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2019

Balance of memory footprint and runtime for high-density routing in large-scale FPGAs.

Wei Liu, Weilin Cong, Chengyu Hu, Peng Lu, Jinmei Lai

Proceedings of the 13th IEEE International Conference on ASIC, 2019

2017

Improved Face Detection and Alignment using Cascade Deep Convolutional Network.

Weilin Cong, Sanyuan Zhao, Hui Tian, Jianbing Shen

CoRR, 2017