Deep Drive We seek to merge deep learning with automotive perception and bring computer vision technology to the forefront.

Although deep neural networks (DNNs) have achieved impressive performance in several non-trivial machine learning (ML) tasks, many challenges remain. One class of challenges has to do with understanding how and why DNNs obtain their improved performance, given that they do not exhibit properties such as convexity that are common among ML methods. A second class of challenges has to do with using this understanding to develop DNN methods that have better statistical/inferential properties and/or better algorithmic/running time properties. In this work, the team will pursue two related directions. First, to develop a model to make theoretically precise certain intuitions that might explain the performance of DNNs.