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Chi Hong
Chi Hong
Verified email at tudelft.nl - Homepage
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Cited by
Year
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
1102018
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
192022
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
92021
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
72021
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
62023
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
62022
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
22024
MEGA: Model Stealing via Collaborative Generator-Substitute Networks
C Hong, J Huang, LY Chen
arXiv preprint arXiv:2202.00008, 2022
22022
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
T Younesian, C Hong, A Ghiassi, R Birke, LY Chen
CogMI2020, 2020
22020
Label aggregation via finding consensus between models
C Hong, Y Zhou
arXiv preprint arXiv:1807.07291, 2018
22018
Gradient Inversion of Federated Diffusion Models
J Huang, C Hong, LY Chen, S Roos
arXiv preprint arXiv:2405.20380, 2024
12024
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
12024
Generative Models for Learning from Crowds
C Hong
arXiv preprint arXiv:1706.03930, 2017
12017
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, ...
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