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George E. Dahl
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G Hinton, L Deng, D Yu, GE Dahl, A Mohamed, N Jaitly, A Senior, ...
IEEE Signal processing magazine 29 (6), 82-97, 2012
142222012
Neural message passing for quantum chemistry
J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl
International conference on machine learning, 1263-1272, 2017
99402017
On the importance of initialization and momentum in deep learning
I Sutskever, J Martens, G Dahl, G Hinton
International conference on machine learning, 1139-1147, 2013
67892013
Relational inductive biases, deep learning, and graph networks
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
41102018
Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition
G Dahl, D Yu, L Deng, A Acero
Audio, Speech, and Language Processing, IEEE Transactions on, 1-1, 2010
40932010
Acoustic modeling using deep belief networks
A Mohamed, GE Dahl, G Hinton
IEEE transactions on audio, speech, and language processing 20 (1), 14-22, 2011
23072011
Deep convolutional neural networks for large-scale speech tasks
TN Sainath, B Kingsbury, G Saon, H Soltau, A Mohamed, G Dahl, ...
Neural networks 64, 39-48, 2015
20572015
Improving deep neural networks for LVCSR using rectified linear units and dropout
GE Dahl, TN Sainath, GE Hinton
2013 IEEE international conference on acoustics, speech and signal …, 2013
19552013
Deep neural nets as a method for quantitative structure–activity relationships
J Ma, RP Sheridan, A Liaw, GE Dahl, V Svetnik
Journal of chemical information and modeling 55 (2), 263-274, 2015
14022015
Detecting cancer metastases on gigapixel pathology images
Y Liu, K Gadepalli, M Norouzi, GE Dahl, T Kohlberger, A Boyko, ...
arXiv preprint arXiv:1703.02442, 2017
8072017
Deep belief networks for phone recognition
A Mohamed, G Dahl, G Hinton
NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, 2009
6922009
Prediction errors of molecular machine learning models lower than hybrid DFT error
FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, ...
Journal of chemical theory and computation 13 (11), 5255-5264, 2017
6862017
Large-scale malware classification using random projections and neural networks
GE Dahl, JW Stokes, L Deng, D Yu
2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013
6662013
Large scale distributed neural network training through online distillation
R Anil, G Pereyra, A Passos, R Ormandi, GE Dahl, GE Hinton
arXiv preprint arXiv:1804.03235, 2018
5272018
Measuring the effects of data parallelism on neural network training
CJ Shallue, J Lee, J Antognini, J Sohl-Dickstein, R Frostig, GE Dahl
Journal of Machine Learning Research 20 (112), 1-49, 2019
4772019
Phone recognition with the mean-covariance restricted Boltzmann machine
G Dahl, M Ranzato, A Mohamed, GE Hinton
Advances in neural information processing systems 23, 2010
4492010
On empirical comparisons of optimizers for deep learning
D Choi, CJ Shallue, Z Nado, J Lee, CJ Maddison, GE Dahl
arXiv preprint arXiv:1910.05446, 2019
4192019
Multi-task neural networks for QSAR predictions
GE Dahl, N Jaitly, R Salakhutdinov
arXiv preprint arXiv:1406.1231, 2014
4162014
Deep belief networks using discriminative features for phone recognition
A Mohamed, TN Sainath, G Dahl, B Ramabhadran, GE Hinton, ...
2011 IEEE international conference on acoustics, speech and signal …, 2011
4122011
Artificial intelligence–based breast cancer nodal metastasis detection: insights into the black box for pathologists
Y Liu, T Kohlberger, M Norouzi, GE Dahl, JL Smith, A Mohtashamian, ...
Archives of pathology & laboratory medicine 143 (7), 859-868, 2019
4042019
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