| Minimal variance sampling with provable guarantees for fast training of graph neural networks W Cong, R Forsati, M Kandemir, M Mahdavi KDD20, 2020 | 89 | 2020 |
| On Provable Benefits of Depth in Training Graph Convolutional Networks W Cong, M Ramezani, M Mahdavi NeurIPS21, 2021 | 70 | 2021 |
| Do We Really Need Complicated Model Architectures For Temporal Networks? W Cong, S Zhang, J Kang, B Yuan, H Wu, X Zhou, H Tong, M Mahdavi ICLR23, 2023 | 44 | 2023 |
| Predicting protein–ligand docking structure with graph neural network H Jiang, J Wang, W Cong, Y Huang, M Ramezani, A Sarma, ... Journal of chemical information and modeling 62 (12), 2923-2932, 2022 | 35 | 2022 |
| Gcn meets gpu: Decoupling “when to sample” from “how to sample” M Ramezani*, W Cong*, M Mahdavi, A Sivasubramaniam, M Kandemir NeurIPS20, 2020 | 31 | 2020 |
| Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks M Ramezani*, W Cong*, M Mahdavi, MT Kandemir, A Sivasubramaniam ICLR22, 2021 | 25 | 2021 |
| DyFormer : A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability W Cong, Y Wu, Y Tian, M Gu, Y Xia, CJ Chen, M Mahdavi SDM23, 2022 | 18* | 2022 |
| On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction W Cong, M Ramezani, M Mahdavi arXiv preprint arXiv:2103.02696, 2021 | 12* | 2021 |
| Bemap: Balanced message passing for fair graph neural network X Lin, J Kang, W Cong, H Tong Learning on Graphs Conference, 37: 1-37: 25, 2024 | 6 | 2024 |
| Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection W Cong, M Mahdavi AISTATS23, 2023 | 6 | 2023 |
| End-to-end cascade cnn for simultaneously face detection and alignment S Zhao, H Song, W Cong, Q Qi, H Tian 2017 International Conference on Virtual Reality and Visualization (ICVRV …, 2017 | 5* | 2017 |
| Encrypted rich-data steganography using generative adversarial networks D Shu, W Cong, J Chai, CS Tucker Proceedings of the 2nd ACM Workshop on Wireless Security and Machine …, 2020 | 4 | 2020 |
| GRAPHEDITOR: An Efficient Graph Representation Learning and Unlearning Approach W Cong, M Mahdavi | 3 | |
| Understanding the structural components behind the psychological effects of autonomous sensory meridian response (ASMR) with machine learning and experimental methods R Tan, H Shoenberger, W Cong, M Mahdavi Journal of Media Psychology, 2022 | 2 | 2022 |
| On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method W Cong, J Kang, H Tong, M Mahdavi arXiv preprint arXiv:2402.16387, 2024 | | 2024 |
| Fundamental problems in graph learning: optimization, generalization, privacy, and model design W Cong | | 2024 |
| Journal of Media Psychology: Theories, Methods, and Applications R Tan, H Shoenberger, W Cong, M Mahdavi, R Tan, H Shoenberger, ... | | 2022 |
Mehrdad MahdaviAssistant Professor of Computer Science, Pennsylvania State Universitypsu.edu의 이메일 확인됨
