Improved Face Detection and Alignment using Cascade Deep Convolutional Network. - Weilin Cong; Sanyuan Zhao; Hui Tian; Jianbing Shen (CoRR, 2017)

Balance of memory footprint and runtime for high-density routing in large-scale FPGAs. - Wei Liu; Weilin Cong; Chengyu Hu; Peng Lu; Jinmei Lai 001 (ASICON, 2019)

Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks. - Weilin Cong; Rana Forsati; Mahmut T. Kandemir; Mehrdad Mahdavi (KDD, 2020)

GCN meets GPU: Decoupling "When to Sample" from "How to Sample". - Morteza Ramezani; Weilin Cong; Mehrdad Mahdavi; Anand Sivasubramaniam; Mahmut T. Kandemir (NeurIPS, 2020)

Encrypted rich-data steganography using generative adversarial networks. - Dule Shu; Weilin Cong; Jiaming Chai; Conrad S. Tucker (WiseML@WiSec, 2020)

On Provable Benefits of Depth in Training Graph Convolutional Networks. - Weilin Cong; Morteza Ramezani; Mehrdad Mahdavi (NeurIPS, 2021)

On the Importance of Sampling in Learning Graph Convolutional Networks. - Weilin Cong; Morteza Ramezani; Mehrdad Mahdavi (CoRR, 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)

Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks. - Morteza Ramezani; Weilin Cong; Mehrdad Mahdavi; Mahmut T. Kandemir; Anand Sivasubramaniam (ICLR, 2022)

Predicting Protein-Ligand Docking Structure with Graph Neural Network. - Huaipan Jiang; Jian Wang 094; Weilin Cong; Yihe Huang; Morteza Ramezani; Anup Sarma; Nikolay V. Dokholyan; Mehrdad Mahdavi; Mahmut T. Kandemir (J. Chem. Inf. Model., 2022)

Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection. - Weilin Cong; Mehrdad Mahdavi (AISTATS, 2023)

Do We Really Need Complicated Model Architectures For Temporal Networks? - Weilin Cong; Si Zhang; Jian Kang 008; Baichuan Yuan; Hao Wu; Xin Zhou; Hanghang Tong; Mehrdad Mahdavi (ICLR, 2023)

BeMap: Balanced Message Passing for Fair Graph Neural Network. - Xiao Lin; Jian Kang 008; Weilin Cong; Hanghang Tong (LoG, 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 (SDM, 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)

On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method. - Weilin Cong; Jian Kang 008; Hanghang Tong; Mehrdad Mahdavi (CoRR, 2024)

Learning Graph Quantized Tokenizers for Transformers. - Limei Wang; Kaveh Hassani; Si Zhang; Dongqi Fu; Baichuan Yuan; Weilin Cong; Zhigang Hua; Hao Wu; Ning Yao; Bo Long (CoRR, 2024)

Learning Graph Quantized Tokenizers. - Limei Wang; Kaveh Hassani; Si Zhang; Dongqi Fu; Baichuan Yuan; Weilin Cong; Zhigang Hua; Hao Wu; Ning Yao; Bo Long (ICLR, 2025)