| Scaling Distributed Machine Learning with In-Network Aggregation A Sapio, M Canini, CY Ho, J Nelson, P Kalnis, C Kim, A Krishnamurthy, ... Proceedings of the 18th USENIX Symposium on Networked Systems Design and …, 2021 | 417 | 2021 |
| Natural compression for distributed deep learning S Horvóth, CY Ho, L Horvath, AN Sahu, M Canini, P Richtárik Mathematical and Scientific Machine Learning, 129-141, 2022 | 161 | 2022 |
| GRACE: A compressed communication framework for distributed machine learning H Xu, CY Ho, AM Abdelmoniem, A Dutta, EH Bergou, K Karatsenidis, ... 2021 IEEE 41st international conference on distributed computing systems …, 2021 | 158* | 2021 |
| On the discrepancy between the theoretical analysis and practical implementations of compressed communication for distributed deep learning A Dutta, EH Bergou, AM Abdelmoniem, CY Ho, AN Sahu, M Canini, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3817-3824, 2020 | 94 | 2020 |
| Efficient sparse collective communication and its application to accelerate distributed deep learning J Fei, CY Ho, AN Sahu, M Canini, A Sapio Proceedings of the 2021 ACM SIGCOMM 2021 Conference, 676-691, 2021 | 83 | 2021 |
| A Comprehensive Empirical Study of Heterogeneity in Federated Learning AM Abdelmoniem, CY Ho, P Papageorgiou, M Canini IEEE Internet of Things Journal, 2023 | 64* | 2023 |
| Tackling the Communication Bottlenecks of Distributed Deep Learning Training Workloads CY Ho | | 2023 |
Marco CaniniKAUST在 kaust.edu.sa 的电子邮件经过验证
