2023年9月22日　　Friday Lunch Seminar （英語で開催します）
12:15 〜 13:00
On-lineで開催いたします。
→申込みは　こちら
（締め切り：9月21日正午、参加要領は9月21日にeメールにてお知らせします。）

演題：Identifiable nonlinear representation learning and some applications

理化学研究所
革新知能統合研究センター
研究員
森岡 博史

担当PI：山下 宙人

Abstract:
Revealing fundamental representation (latent components) generating observational data in a data-driven manner is called representation learning, and has a long history including such as principal component analysis (PCA) and independent component analysis (ICA). Many frameworks were proposed based on deep learning in recent years to somehow extend them to nonlinear cases, including variational autoencoders (VAEs) and generative adversarial networks (GANs). However, such nonlinear representation learning is in general ill-posed, and it is known that there is no theoretical guarantee that they can estimate the “true” components. In this talk, I will introduce our work on nonlinear independent component analysis (NICA), and explain how such problems can be solved and made identifiable by adding some assumptions on the latent components. I will also introduce some recent extensions of NICA to dynamical models and causal discovery, with some applications to neuroimaging data.