2 code implementations • 20 Jun 2022 • Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart
When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints.
no code implementations • 2 Jun 2022 • Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart
We consider the setting where the reward function is given, and the constraints are unknown, and propose a method that is able to recover these constraints satisfactorily from the expert data.
1 code implementation • 9 Oct 2019 • Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad, Taylor Denouden, Rick Salay, Krzysztof Czarnecki
By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 25 Sep 2019 • Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad, Taylor Denouden, Rick Salay, Krzysztof Czarnecki
In the context of OOD detection for image classification, one of the recent approaches proposes training a classifier called “confident-classifier” by minimizing the standard cross-entropy loss on in-distribution samples and minimizing the KLdivergence between the predictive distribution of OOD samples in the low-density“boundary” of in-distribution and the uniform distribution (maximizing the entropy of the outputs).
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 21 Aug 2019 • Marko Ilievski, Sean Sedwards, Ashish Gaurav, Aravind Balakrishnan, Atrisha Sarkar, Jaeyoung Lee, Frédéric Bouchard, Ryan De Iaco, Krzysztof Czarnecki
We explore the complex design space of behaviour planning for autonomous driving.
1 code implementation • 27 Apr 2019 • Sachin Vernekar, Ashish Gaurav, Taylor Denouden, Buu Phan, Vahdat Abdelzad, Rick Salay, Krzysztof Czarnecki
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution).
no code implementations • 11 Feb 2019 • Jaeyoung Lee, Aravind Balakrishnan, Ashish Gaurav, Krzysztof Czarnecki, Sean Sedwards
Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge.