| Graph convolutional reinforcement learning J Jiang, C Dun, T Huang, Z Lu arXiv preprint arXiv:1810.09202, 2018 | 486 | 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 | 258 | 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, 2022 | 40 | 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 | 25 | 2023 |
| ResIST: Layer-wise decomposition of resnets for distributed training C Dun, CR Wolfe, CM Jermaine, A Kyrillidis Uncertainty in Artificial Intelligence, 610-620, 2022 | 24 | 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 |
| 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 8 (5), 1363-1415, 2024 | 15 | 2024 |
| Graph convolutional reinforcement learning. arXiv J Jiang, C Dun, T Huang, Z Lu arXiv preprint arXiv:1810.09202, 2018 | 14 | 2018 |
| Graph convolutional reinforcement learning. arXiv 2018 J Jiang, C Dun, T Huang, Z Lu arXiv preprint arXiv:1810.09202, 0 | 10 | |
| 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 | 5 | 2021 |
| Graph convolutional reinforcement learning for multi-agent cooperation 2 (3) J Jiang, C Dun, Z Lu arXiv preprint arXiv:1810.09202, 2018 | 5 | 2018 |
| Sweeping heterogeneity with smart mops: Mixture of prompts for LLM task adaptation C Dun, MH Garcia, G Zheng, AH Awadallah, A Kyrillidis, R Sim arXiv preprint arXiv:2310.02842, 2023 | 4 | 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 | 4 | 2023 |
| Enhancing multi-step reasoning abilities of language models through direct q-function optimization G Liu, K Ji, R Zheng, Z Wu, C Dun, Q Gu, L Yan arXiv preprint arXiv:2410.09302, 2024 | 3 | 2024 |
| 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 |
| Process supervision-guided policy optimization for code generation N Dai, Z Wu, R Zheng, Z Wei, W Shi, X Jin, G Liu, C Dun, L Huang, L Yan arXiv preprint arXiv:2410.17621, 2024 | 2 | 2024 |
| Fast fixmatch: Faster semi-supervised learning with curriculum batch size J Chen, C Dun, A Kyrillidis 2024 IEEE International Symposium on Information Theory (ISIT), 1836-1841, 2024 | 2 | 2024 |
| CrysFormer: Protein structure determination via Patterson maps, deep learning, and partial structure attention T Pan, C Dun, S Jin, MD Miller, A Kyrillidis, GN Phillips Structural Dynamics 11 (4), 2024 | 2 | 2024 |
| 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 | 2 | 2023 |
| Flaming-hot Initiation with Regular Execution Sampling for Large Language Models W Chen, Z Zhang, G Liu, R Zheng, W Shi, C Dun, Z Wu, X Jin, L Yan arXiv preprint arXiv:2410.21236, 2024 | | 2024 |
