Zobrazeno 1 - 10
of 15 977
pro vyhledávání: '"Chan, A. S."'
Autor:
Mei, Yongsheng, Yuan, Liangqi, Han, Dong-Jun, Chan, Kevin S., Brinton, Christopher G., Lan, Tian
Federated learning (FL) has become a cornerstone in decentralized learning, where, in many scenarios, the incoming data distribution will change dynamically over time, introducing continuous learning (CL) problems. This continual federated learning (
Externí odkaz:
http://arxiv.org/abs/2411.06618
This study presents a dynamic Bayesian network framework that facilitates intuitive gradual edge changes. We use two conditional dynamics to model the edge addition and deletion, and edge selection separately. Unlike previous research that uses a mix
Externí odkaz:
http://arxiv.org/abs/2409.08965
Autor:
Zhao, Joshua C., Bagchi, Saurabh, Avestimehr, Salman, Chan, Kevin S., Chaterji, Somali, Dimitriadis, Dimitris, Li, Jiacheng, Li, Ninghui, Nourian, Arash, Roth, Holger R.
Deep learning has shown incredible potential across a vast array of tasks and accompanying this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices and recen
Externí odkaz:
http://arxiv.org/abs/2405.03636
Autor:
Pareek, Vivek, Bacon, David R., Zhu, Xing, Chan, Yang-Hao, Bussolotti, Fabio, Chan, Nicholas S., Urquizo, Joel Pérez, Watanabe, Kenji, Taniguchi, Takashi, Man, Michael K. L., Madéo, Julien, Qiu, Diana Y., Goh, Kuan Eng Johnson, da Jornada, Felipe H., Dani, Keshav M.
Inducing novel quantum phases and topologies in materials using intense light fields is a key objective of modern condensed matter physics, but nonetheless faces significant experimental challenges. Alternately, theory predicts that in the dense limi
Externí odkaz:
http://arxiv.org/abs/2403.08725
Time-series with volatility clustering pose a unique challenge to uncertainty quantification (UQ) for returns forecasts. Methods for UQ such as Deep Evidential regression offer a simple way of quantifying return forecast uncertainty without the costs
Externí odkaz:
http://arxiv.org/abs/2402.14476
Federated learning (FL) enables distributed model training from local data collected by users. In distributed systems with constrained resources and potentially high dynamics, e.g., mobile edge networks, the efficiency of FL is an important problem.
Externí odkaz:
http://arxiv.org/abs/2212.08496
Autor:
Chan, Louis S.1 (AUTHOR), Cochon, Kim L.1,2 (AUTHOR), Li, Tin C.1 (AUTHOR), Chung, Jacqueline P. W.1 (AUTHOR), Kim, Jean H.1 (AUTHOR) jhkim@cuhk.edu.hk
Publikováno v:
PLoS ONE. 9/11/2024, Vol. 19 Issue 9, p1-17. 17p.
Autor:
Usman, Farha1 (AUTHOR) fusm0507@uni.sydney.edu.au, Chan, Jennifer S. K.1 (AUTHOR) fusm0507@uni.sydney.edu.au, Makov, Udi E.2 (AUTHOR) makov@stat.haifa.ac.il, Wang, Yang1 (AUTHOR), Dong, Alice X. D.3 (AUTHOR) xiaodan.dong@uts.edu.au
Publikováno v:
Risks. Sep2024, Vol. 12 Issue 9, p137. 33p.
Autor:
Lu, Hanlin, Liu, Changchang, Wang, Shiqiang, He, Ting, Narayanan, Vijay, Chan, Kevin S., Pasteris, Stephen
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication and computation costs. To achieve a better trade-off between ML error
Externí odkaz:
http://arxiv.org/abs/2204.06652
Autor:
Zhao, Mengxue, Chan, Hon S.
Publikováno v:
In Technological Forecasting & Social Change November 2024 208