Causal discovery between collections of time-series data can help diagnose causes of symptoms and hopefully prevent faults before they occur.
Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate through a T-intersection with deep reinforcement learning.
We explore the interpretation of sound for robot decision making, inspired by human speech comprehension.
Our results show the feasibility of a robot learning commonsense knowledge automatically from web-based textual corpora, and the power of learned commonsense reasoning models in enabling a robot to autonomously perform tasks based on incomplete natural language instructions.
The increasing concern with misinformation has stimulated research efforts on automatic fact checking.
Building on the idea that identity classification, attribute recognition and re- identification share the same mid-level semantic representations, they can be trained sequentially by fine-tuning one based on another.