DEEP-INセミナー
42 イベント
-
セミナー
Discovering Physical Laws with Artificial Intelligence
2024年7月12日(金) 10:00 - 11:30
刘 子鸣 (Ph.D. Student, Department of Physics, Massachusetts Institute of Technology, USA)
Deep neural networks have been extremely successful in language and vision tasks. However, their black-box nature makes them undesirable for scientific tasks. In this talk, I will show how we can make these black-box AI models more interpretable and transparent and use them to discover physical laws, including conservation laws (AI Poincare), symmetries, phase transitions and symbolic relations (Kolmogorov-Arnold Networks). Ziming is a physicist and a machine learning researcher. Ziming received BS in physics from Peking Univeristy in 2020, and is current a fourth-year PhD student at MIT and IAIFI, advised by Max Tegmark. His research interests lie generally in the intersection of artificial intelligence (AI) and physics (science in general).
会場: via Zoom
イベント公式言語: 英語
-
セミナー
Inferring collective behavior from social interactions to population coding
2024年6月27日(木) 16:00 - 17:30
Chen Xiaowen (Postdoctoral Researcher, Laboratoire de Physique de l’École normale supérieure, CNRS, France)
(This is a joint iTHEMS Biology Seminar) From social animals to neuronal networks, collective behavior is ubiquitous in living systems. How are these behaviors encoded in interactions, and how do they drive biological functions? Recent insights from statistical physics applied to biological data have offer exciting new perspectives. However, previous research has mostly focused on the statics, i.e. the steady-state distributions of the collective behavior, without taking into consideration of time. In this talk, I will present two recent progresses tapping into the temporal domain. First, I will present a study of collective behavior in social mice from their co-localization patterns. To capture both static and dynamic features of the data, we developed a novel inference method termed the generalized Glauber dynamics (GGD) that can tune the dynamics while keeping the steady state distribution fixed. I will first outline the explanation power of the GGD dynamics, then explain how to infer the dynamics from data. The inferred interactions characterize sociability for different mice strains. In the second example, we studied information flow among neurons in the larval zebrafish hindbrain. By adapting the method of Granger causality to single cell calcium transient data, we were able to detect both a global information flow among neurons, as well as identifying brain regions that are key in locomotion.
会場: via Zoom
イベント公式言語: 英語
42 イベント