G
George E. Dahl
Researcher at Google
Publications - 66
Citations - 36393
George E. Dahl is an academic researcher from Google. The author has contributed to research in topics: Artificial neural network & Hidden Markov model. The author has an hindex of 36, co-authored 56 publications receiving 29759 citations. Previous affiliations of George E. Dahl include Microsoft & University of Toronto.
Papers
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Journal ArticleDOI
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Proceedings Article
On the importance of initialization and momentum in deep learning
TL;DR: It is shown that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs to levels of performance that were previously achievable only with Hessian-Free optimization.
Proceedings Article
Neural Message Passing for Quantum Chemistry
TL;DR: The Message Passing Neural Networks (MPNNs) as mentioned in this paper are a generalization of the message passing algorithm and aggregation procedure to compute a function of their entire input graph, and have been shown to achieve state-of-the-art results on an important molecular property prediction benchmark.
Journal ArticleDOI
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
TL;DR: A pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output that can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs.
Journal Article
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition.