ACRE: Abstract Causal REasoning Beyond Covariation

no code implementations • CVPR 2021 • Chi Zhang, Baoxiong Jia, Mark Edmonds, Song-Chun Zhu, Yixin Zhu

Causal induction, i. e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data.

Blocking Causal Discovery +1

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

no code implementations • 20 Apr 2020 • Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu

We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning.

Common Sense Reasoning Small Data Image Classification

Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning

no code implementations • 25 Nov 2019 • Mark Edmonds, Xiaojian Ma, Siyuan Qi, Yixin Zhu, Hongjing Lu, Song-Chun Zhu

Given these general theories, the goal is to train an agent by interactively exploring the problem space to (i) discover, form, and transfer useful abstract and structural knowledge, and (ii) induce useful knowledge from the instance-level attributes observed in the environment.

Reinforcement Learning (RL) Transfer Learning