| On Provable Benefits of Depth in Training Graph Convolutional Networks W Cong, M Ramezani, M Mahdavi NeurIPS 34, 2021 | 89 | 2021 |
| 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 | 53 | 2022 |
| GCN meets GPU: Decoupling "When to Sample" from "How to Sample". M Ramezani, W Cong, M Mahdavi, A Sivasubramaniam, MT Kandemir NeurIPS 33, 2020 | 38 | 2020 |
| Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks M Ramezani, W Cong, M Mahdavi, M Kandemir, A Sivasubramaniam International Conference on Learning Representations (ICLR) 10, 2022 | 37 | 2022 |
| On the importance of sampling in learning graph convolutional networks W Cong, M Ramezani, M Mahdavi arXiv preprint arXiv:2103.02696, 2021 | 14* | 2021 |
| Lignn: Graph neural networks at linkedin F Borisyuk, S He, Y Ouyang, M Ramezani, P Du, X Hou, C Jiang, ... Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 9 | 2024 |
| Exploring the impact of memory block permutation on performance of a crossbar ReRAM main memory M Ramezani, N Elyasi, M Arjomand, MT Kandemir, A Sivasubramaniam 2017 IEEE International Symposium on Workload Characterization (IISWC), 167-176, 2017 | 6 | 2017 |
| CEDAR: Modeling impact of component error derating and read frequency on system-level vulnerability in high-performance processors H Asadi, A Haghdoost, M Ramezani, N Elyasi, A Baniasadi Microelectronics Reliability 54 (5), 1009-1021, 2014 | 1 | 2014 |
| Systems Optimizations for Learning and Processing on Large Scale Graphs M Ramezani The Pennsylvania State University, 2022 | | 2022 |
| GraphGuess: Approximate Graph Processing System with Adaptive Correction M Ramezani, MT Kandemir, A Sivasubramaniam European Conference on Parallel Processing, 285-300, 2022 | | 2022 |
Weilin CongPenn State University確認したメール アドレス: psu.edu