Machine Learning Lecture 16 "Empirical Risk Minimization" -Cornell CS4780 SP17
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YouTubeKilian Weinberger
Machine Learning Lecture 16 "Empirical Risk Minimization" -Cornell CS4780 SP17
Lecture Notes: http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote10.html
30.1K viewsJul 11, 2018
Empirical Risk Minimization Examples
Closed-loop Weighted Empirical Risk Minimization (CW-ERM) addresses a long-standing problem in imitation learning. It is simple to implement and significantly reduces self-driving collisions by a large margin on real-world datasets. Below we are comparing the same scene using ERM (left) and CW-ERM (right) and can see that CW-ERM avoids a front collision. Find out more about this project here  https://wp-research-uk.github.io/cw-erm/ #WovenPlanet | Woven by Toyota | Facebook
0:08
Closed-loop Weighted Empirical Risk Minimization (CW-ERM) addresses a long-standing problem in imitation learning. It is simple to implement and significantly reduces self-driving collisions by a large margin on real-world datasets. Below we are comparing the same scene using ERM (left) and CW-ERM (right) and can see that CW-ERM avoids a front collision. Find out more about this project here  https://wp-research-uk.github.io/cw-erm/ #WovenPlanet | Woven by Toyota | Facebook
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