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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Journal ArticleDOI
Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography
TL;DR: Deep learning models pretrained on labeled 12-lead ECGs can predict myocardial injury from noisy, continuous monitor data early in a patient's presentation, and has implications for wearable devices and preclinical settings, where external validation of the approach is needed.
Approximate Dynamic Programming
Antonis C. Kakas,David Cohn,Sanjoy Dasgupta,Andrew G. Barto,Gail A. Carpenter,Stephen Grossberg,Geoffrey I. Webb,Marco Dorigo,Mauro Birattari,Hannu Toivonen,Jon Timmis,Jürgen Branke,Alexander L. Strehl,Chris Drummond,Adam Coates,Pieter Abbeel,Andrew Y. Ng,Fei Zheng,Prasad Tadepalli +18 more
Book ChapterDOI
Reinforcement learning and apprenticeship learning for robotic control
TL;DR: In this paper, apprenticeship learning is used to learn from a human demonstration of a task and apply it to the STAIR (STanford AI Robot) project, which has the long term goal of integrating methods from all major areas of AI, including spoken dialog/NLP, manipulation, vision, navigation, and planning.
Journal ArticleDOI
METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping
B. Zhu,NIcholas Lui,Jeremy Irvin,Jimmy Le,Sahil Tadwalkar,Chenghao Wang,Zutao Ouyang,Frankie Y. Liu,Andrew Y. Ng,Robert B. Jackson +9 more
TL;DR: The METER-ML dataset as discussed by the authors contains 86,599 georeferenced NAIP, Sentinel-1, and Sentinel-2 images in the U.S. labeled for the presence or absence of methane source facilities.
Posted Content
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
TL;DR: The authors proposed a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources for chest X-ray interpretation, which is based on a trainable deep learning selector that uses image embeddings obtained from contrastive pretraining for determining which images to label, and a non-parametric selector using cosine similarity to classify unseen images.