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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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Proceedings Article
Energy Disaggregation via Discriminative Sparse Coding
TL;DR: This paper develops a method, based upon structured prediction, for discriminatively training sparse coding algorithms specifically to maximize disaggregation performance, and demonstrates how these disaggregation results can provide useful information about energy usage.
Proceedings Article
Recurrent Neural Networks for Noise Reduction in Robust ASR
TL;DR: This work introduces a model which uses a deep recurrent auto encoder neural network to denoise input features for robust ASR, and demonstrates the model is competitive with existing feature denoising approaches on the Aurora2 task, and outperforms a tandem approach where deep networks are used to predict phoneme posteriors directly.
Proceedings Article
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning
TL;DR: A robust soft reconstruction cost for ICA is proposed that allows us to learn highly overcomplete sparse features even on unwhitened data and reveals formal connections between ICA and sparse autoencoders, which have previously been observed only empirically.
Proceedings ArticleDOI
Web question answering: is more always better?
TL;DR: This paper describes a question answering system that is designed to capitalize on the tremendous amount of data that is now available online, and uses the redundancy available in large corpora as an important resource to simplify the query rewrites and support answer mining from returned snippets.
Proceedings Article
Autonomous Helicopter Flight via Reinforcement Learning
TL;DR: This paper first fit a stochastic, nonlinear model of the helicopter dynamics, then uses the model to learn to hover in place, and to fly a number of maneuvers taken from an RC helicopter competition.