A
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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Deep learning models for speech recognition
Awni Hannun,Carl Case,Jared Casper,Bryan Catanzaro,Gregory Diamos,Erich Elsen,Ryan Prenger,Sanjeev Satheesh,Sengupta Shubhabrata,Adam Coates,Andrew Y. Ng +10 more
TL;DR: In this paper, state-of-the-art speech recognition systems developed using end-to-end deep learning are described. But they do not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learn a function that is robust to such effects.
CheXphoto: 10,000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness
Nick A. Phillips,Pranav Rajpurkar,Mark Sabini,Rayan Krishnan,Sharon Zhou,Anuj Pareek,Nguyet Minh Phu,Chris Wang,Mudit Jain,Nguyen Duong Du,Steven Truong,Andrew Y. Ng,Matthew P. Lungren +12 more
TL;DR: CheXphoto as mentioned in this paper is a dataset of smartphone photos and synthetic photographic transformations of chest x-rays sampled from the CheXpert dataset, which is used for testing and improving the robustness of deep learning algorithms for automated chest X-ray interpretation.
ReportDOI
Exploring the Utility of ResearchCyc for Reasoning from Natural Language
TL;DR: This project investigated the potential for using ResearchCyc in natural language processing systems, particularly on natural language problems connected to sentence understanding, such as reading comprehension and robust textual inference, and developed software for interaction between robust natural languageprocessing systems and Research Cyc, via its Java interface.
Journal ArticleDOI
Guest editorial: Special issue on robot learning, Part A
Jan Peters,Andrew Y. Ng +1 more
TL;DR: In this Part B of the Autonomous Robots Special Issue on Robot Learning, recent successes in the application of domain-driven machine learning methods to robotics are highlighted to highlight the feasibility of more general methods.