SNrram: An efficient sparse neural network computation architecture based on resistive random-access memory P Wang, Y Ji, C Hong, Y Lyu, D Wang, Y Xie Proceedings of the 55th Annual Design Automation Conference, 1-6, 2018 | 110 | 2018 |
Agic: Approximate gradient inversion attack on federated learning J Xu, C Hong, J Huang, LY Chen, J Decouchant 2022 41st International Symposium on Reliable Distributed Systems (SRDS), 12-22, 2022 | 19 | 2022 |
Is Shapley value fair? Improving client selection for mavericks in federated learning J Huang, C Hong, LY Chen, S Roos arXiv preprint arXiv:2106.10734, 2021 | 9 | 2021 |
Online label aggregation: A variational bayesian approach C Hong, A Ghiassi, Y Zhou, R Birke, LY Chen WWW 2021, Proceedings of the Web Conference 2021, 1904-1915, 2021 | 7 | 2021 |
Maverick matters: Client contribution and selection in federated learning J Huang, C Hong, Y Liu, LY Chen, S Roos Pacific-Asia Conference on Knowledge Discovery and Data Mining, 269-282, 2023 | 6 | 2023 |
Tackling mavericks in federated learning via adaptive client selection strategy J Huang, C Hong, Y Liu, LY Chen, S Roos International Workshop on Trustable, Verifiable and Auditable Federated …, 2022 | 6 | 2022 |
On Dark Knowledge for Distilling Generators C Hong, R Birke, PY Chen, LY Chen Pacific-Asia Conference on Knowledge Discovery and Data Mining, 235-247, 2024 | 2 | 2024 |
MEGA: Model Stealing via Collaborative Generator-Substitute Networks C Hong, J Huang, LY Chen arXiv preprint arXiv:2202.00008, 2022 | 2 | 2022 |
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models T Younesian, C Hong, A Ghiassi, R Birke, LY Chen CogMI2020, 2020 | 2 | 2020 |
Label aggregation via finding consensus between models C Hong, Y Zhou arXiv preprint arXiv:1807.07291, 2018 | 2 | 2018 |
Gradient Inversion of Federated Diffusion Models J Huang, C Hong, LY Chen, S Roos arXiv preprint arXiv:2405.20380, 2024 | 1 | 2024 |
SFDDM: Single-fold Distillation for Diffusion models C Hong, J Huang, R Birke, D Epema, S Roos, LY Chen arXiv preprint arXiv:2405.14961, 2024 | 1 | 2024 |
Generative Models for Learning from Crowds C Hong arXiv preprint arXiv:1706.03930, 2017 | 1 | 2017 |
Exploring and Exploiting Data-Free Model Stealing C Hong, J Huang, R Birke, LY Chen ECML PKDD 2023, Joint European Conference on Machine Learning and Knowledge …, 2023 | | 2023 |
Item Difficulty-Based Label Aggregation Models for Crowdsourcing C Hong arXiv preprint arXiv:1706.03930, 2017 | | 2017 |
PASCMP: A novel cache framework for data mining application C Hong, H Wang, D Wang 2016 2nd IEEE International Conference on Computer and Communications (ICCC …, 2016 | | 2016 |
2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)| 978-1-7281-4144-2/20/$31.00© 2020 IEEE| DOI: 10.1109/COGMI50398. 2020.00039 T Abdelzaher, GD Abowd, A Alten, J Bae, R Bagwe, L Barbaglia, ... | | |