no code implementations • 17 Oct 2020 • Mariusz Bojarski, Chenyi Chen, Joyjit Daw, Alperen Değirmenci, Joya Deri, Bernhard Firner, Beat Flepp, Sachin Gogri, Jesse Hong, Lawrence Jackel, Zhenhua Jia, BJ Lee, Bo Liu, Fei Liu, Urs Muller, Samuel Payne, Nischal Kota Nagendra Prasad, Artem Provodin, John Roach, Timur Rvachov, Neha Tadimeti, Jesper van Engelen, Haiguang Wen, Eric Yang, Zongyi Yang
Four years ago, an experimental system known as PilotNet became the first NVIDIA system to steer an autonomous car along a roadway.
This eliminates the need for human engineers to anticipate what is important in an image and foresee all the necessary rules for safe driving.
We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction.
We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy.
We analyze the performance of the top-down multiclass classification algorithm for decision tree learning called LOMtree, recently proposed in the literature Choromanska and Langford (2014) for solving efficiently classification problems with very large number of classes.
108 code implementations • 25 Apr 2016 • Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, Karol Zieba
The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.
We prove several theoretical results showing that projections via various structured matrices followed by nonlinear mappings accurately preserve the angular distance between input high-dimensional vectors.
We consider supervised learning with random decision trees, where the tree construction is completely random.