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Andrew Y. Ng
Researcher at Stanford University
Publications - 356
Citations - 184387
Andrew Y. Ng is an academic researcher from Stanford University. The author has contributed to research in topics: Deep learning & Supervised learning. The author has an hindex of 130, co-authored 345 publications receiving 164995 citations. Previous affiliations of Andrew Y. Ng include Max Planck Society & Baidu.
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Building DNN Acoustic Models for Large Vocabulary Speech Recognition
Andrew L. Maas,Peng Qi,Ziang Xie,Awni Hannun,Christopher T. Lengerich,Dan Jurafsky,Andrew Y. Ng +6 more
TL;DR: An empirical investigation on which aspects of DNN acoustic model design are most important for speech recognition system performance, and suggests that a relatively simple DNN architecture and optimization technique produces strong results.
Proceedings Article
Learning vehicular dynamics, with application to modeling helicopters
TL;DR: This paper presents an efficient algorithm for (approximately) minimizing the prediction error over long time scales of a helicopter's dynamics, and presents empirical results on two different helicopters.
Proceedings Article
Make3D: depth perception from a single still image
TL;DR: This paper presents algorithms for estimating depth from a single still image, and discusses applications of the depth perception algorithm in robotic navigation, in improving the performance of stereovision, and in creating large-scale 3-d models given only a small number of images.
Proceedings ArticleDOI
A probabilistic approach to mixed open-loop and closed-loop control, with application to extreme autonomous driving
TL;DR: This work applies its approach to the task of autonomous sideways sliding into a parking spot, and shows that it can repeatedly and accurately control the system, placing the car within about 2 feet of the desired location; this represents the state of the art in terms of accurately controlling a vehicle in such a maneuver.
Patent
Support for real-time queries concerning current state, data and history of a process
TL;DR: In this article, a computer-implemented method is provided for defining interesting portions of a workflow of a business or other type of process. Using a tracking profile editor, a portion of a given workflow is selected and associated with a named process part.