Graph convolutional reinforcement learning J Jiang, C Dun, T Huang, Z Lu arXiv preprint arXiv:1810.09202, 2018 | 417 | 2018 |
Current progress and open challenges for applying deep learning across the biosciences N Sapoval, A Aghazadeh, MG Nute, DA Antunes, A Balaji, R Baraniuk, ... Nature Communications 13 (1), 1728, 2022 | 169 | 2022 |
Distributed learning of fully connected neural networks using independent subnet training B Yuan, CR Wolfe, C Dun, Y Tang, A Kyrillidis, C Jermaine Proceedings of the VLDB Endowment 15 (8), 2022 | 28 | 2022 |
Distributed learning of deep neural networks using independent subnet training B Yuan, CR Wolfe, C Dun, Y Tang, A Kyrillidis, CM Jermaine arXiv preprint arXiv:1910.02120, 2019 | 19 | 2019 |
ResIST: Layer-wise decomposition of resnets for distributed training C Dun, CR Wolfe, CM Jermaine, A Kyrillidis Uncertainty in Artificial Intelligence, 610-620, 2022 | 18 | 2022 |
Efficient and light-weight federated learning via asynchronous distributed dropout C Dun, M Hipolito, C Jermaine, D Dimitriadis, A Kyrillidis International Conference on Artificial Intelligence and Statistics, 6630-6660, 2023 | 15 | 2023 |
GIST: Distributed training for large-scale graph convolutional networks CR Wolfe, J Yang, F Liao, A Chowdhury, C Dun, A Bayer, S Segarra, ... Journal of Applied and Computational Topology, 1-53, 2023 | 12 | 2023 |
Graph convolutional reinforcement learning. arXiv J Jiang, C Dun, T Huang, Z Lu arXiv preprint arXiv:1810.09202, 2018 | 8 | 2018 |
Graph convolutional reinforcement learning. arXiv 2018 J Jiang, C Dun, T Huang, Z Lu arXiv preprint arXiv:1810.09202, 0 | 7 | |
Graph convolutional reinforcement learning for multi-agent cooperation 2 (3) J Jiang, C Dun, Z Lu arXiv preprint arXiv:1810.09202, 2018 | 5 | 2018 |
LOFT: Finding lottery tickets through filter-wise training Q Wang, C Dun, F Liao, C Jermaine, A Kyrillidis International Conference on Artificial Intelligence and Statistics, 6498-6526, 2023 | 3 | 2023 |
FedJETs: Efficient Just-In-Time Personalization with Federated Mixture of Experts C Dun, MH Garcia, G Zheng, A Awadallah, R Sim, A Kyrillidis, ... R0-FoMo: Robustness of Few-shot and Zero-shot Learning in Large Foundation …, 2023 | 2 | 2023 |
Sweeping heterogeneity with smart mops: Mixture of prompts for llm task adaptation C Dun, MDCH Garcia, G Zheng, AH Awadallah, A Kyrillidis, R Sim arXiv preprint arXiv:2310.02842, 2023 | 2 | 2023 |
Federated multiple label hashing (fedmlh): Communication efficient federated learning on extreme classification tasks Z Dai, C Dun, Y Tang, A Kyrillidis, A Shrivastava arXiv preprint arXiv:2110.12292, 2021 | 2 | 2021 |
CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention C Dun, Q Pan, S Jin, R Stevens, MD Miller, GN Phillips Jr, A Kyrillidis arXiv preprint arXiv:2310.03899, 2023 | 1 | 2023 |
Fast FixMatch: Faster Semi-Supervised Learning with Curriculum Batch Size J Chen, C Dun, A Kyrillidis arXiv preprint arXiv:2309.03469, 2023 | 1 | 2023 |
A General Method for Efficient Distributed Training and Federated Learning in Synchronous and Asynchronous Scenarios C Dun | | 2023 |
Fed-ZERO: Efficient Zero-shot Personalization with Federated Mixture of Experts C Dun, MH Garcia, G Zheng, AH Awadallah, R Sim, A Kyrillidis, ... arXiv preprint arXiv:2306.08586, 2023 | | 2023 |
CrysFormer: Protein Crystallography Prediction via 3d Patterson Maps and Partial Structure Attention C Dun, T Pan, S Jin, R Stevens, MD Miller, GN Phillips Jr, A Kyrillidis | | |