2019年7月24日（水）
14:00
会場： CiNet　1F　大会議室

“Priors for affective representation learning”

神戸大学 助教
Wenjun Bai

担当：春野雅彦　（PI）

Abstract:
Learning an optimal affective representation is at the root of affective computing. However, the discussion on priors for affective representation learning receives limited research attention. Here, I propose that an optimal affective representation should be continuous and reward orientated. Through deriving a computational model with constraints on early visual learning, I will argue that it is possible to learn continuous affective representations without human-curated labels. One step further, I will share my thoughts on how to implement other biological constraints under the information-theoretic framework, and to recast affective representation learning under the reward based reinforcement learning.