| 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 | 13990 | 2012 |
| Neural message passing for quantum chemistry J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl International conference on machine learning, 1263-1272, 2017 | 9148 | 2017 |
| 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 | 6563 | 2013 |
| 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 | 4033 | 2010 |
| 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 | 3926 | 2018 |
| 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 | 2275 | 2011 |
| 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 | 2036 | 2015 |
| 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 | 1921 | 2013 |
| 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 | 1367 | 2015 |
| 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 | 792 | 2017 |
| Deep belief networks for phone recognition A Mohamed, G Dahl, G Hinton NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, 2009 | 675 | 2009 |
| 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 | 654 | 2017 |
| 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 | 652 | 2013 |
| 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 | 512 | 2018 |
| 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 | 451 | 2019 |
| Phone recognition with the mean-covariance restricted Boltzmann machine G Dahl, MA Ranzato, A Mohamed, GE Hinton Advances in neural information processing systems 23, 2010 | 447 | 2010 |
| 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 | 410 | 2011 |
| Multi-task neural networks for QSAR predictions GE Dahl, N Jaitly, R Salakhutdinov arXiv preprint arXiv:1406.1231, 2014 | 403 | 2014 |
| 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 | 387 | 2019 |
| On empirical comparisons of optimizers for deep learning D Choi arXiv preprint arXiv:1910.05446, 2019 | 387 | 2019 |
Geoffrey HintonEmeritus Prof. Computer Science, University of TorontoEmail verificata su cs.toronto.edu