BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Physics in (Federated) Deep Neural Networks and Beyond: A Parametr
 ic Perspective - Zexi Li-Zhejiang University
DTSTART:20241114T140000Z
DTEND:20241114T150000Z
UID:TALK224242@talks.cam.ac.uk
CONTACT:Sally Matthews
DESCRIPTION:Physics is about the mechanisms behind our physical world. For
  Physics in Deep Learning\, we try to understand the mechanisms behind the
  deep learning phenomena and how to build up effective methods based on su
 ch understanding. This talk mainly focuses on the model parameters\, the b
 ehind insights\, and the algorithms that can be built upon these insights\
 , which include the issues that 1) how the learning dynamics and weight no
 rm landscape emerge in federated deep learning\, 2) how data heterogeneity
  affects parameter drifts and the relation to neural collapse\, and 3) how
  to locate\, edit\, and inject knowledge in LLM parameters under a continu
 al manner.\n\nBio: Zexi Li is a visiting PhD student at CaMLSys Lab\, Univ
 ersity of Cambridge\, and he is a PhD student of Artificial Intelligence i
 n Zhejiang University\, China. He focuses on optimization\, generalization
 \, and personalization of deep learning models\, especially under federate
 d/collaborative setups\, through the lens of mechanistic interpretability 
 and learning dynamics. He has published 8 (co)first-author top-tier machin
 e learning papers\, including ICML\, ICCV\, NeurIPS\, Patterns (Cell Press
 )\, and etc. Personal website: zexilee.github.io/about-zexili.
LOCATION:Computer Lab\, LT1
END:VEVENT
END:VCALENDAR