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George E. Dahl
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Books and Theses
- 2015
- [b1]George E. Dahl:
Deep Learning Approaches to Problems in Speech Recognition, Computational Chemistry, and Natural Language Text Processing. University of Toronto, Canada, 2015
Journal Articles
- 2024
- [j6]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-trained Gaussian Processes for Bayesian Optimization. J. Mach. Learn. Res. 25: 212:1-212:83 (2024) - 2019
- [j5]Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl:
Measuring the Effects of Data Parallelism on Neural Network Training. J. Mach. Learn. Res. 20: 112:1-112:49 (2019) - 2015
- [j4]Junshui Ma, Robert P. Sheridan, Andy Liaw, George E. Dahl, Vladimir Svetnik:
Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. 55(2): 263-274 (2015) - [j3]Tara N. Sainath, Brian Kingsbury, George Saon
, Hagen Soltau, Abdel-rahman Mohamed, George E. Dahl, Bhuvana Ramabhadran:
Deep Convolutional Neural Networks for Large-scale Speech Tasks. Neural Networks 64: 39-48 (2015) - 2012
- [j2]Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton:
Acoustic Modeling Using Deep Belief Networks. IEEE Trans. Speech Audio Process. 20(1): 14-22 (2012) - [j1]George E. Dahl, Dong Yu, Li Deng, Alex Acero
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition. IEEE Trans. Speech Audio Process. 20(1): 30-42 (2012)
Conference and Workshop Papers
- 2022
- [c17]Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl:
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. AISTATS 2022: 11056-11071 - [c16]Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, Orhan Firat:
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models. ICLR 2022 - 2021
- [c15]Samuel R. Bowman, George E. Dahl:
What Will it Take to Fix Benchmarking in Natural Language Understanding? NAACL-HLT 2021: 4843-4855 - 2019
- [c14]Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse:
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. NeurIPS 2019: 8194-8205 - 2018
- [c13]Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George E. Dahl:
The Importance of Generation Order in Language Modeling. EMNLP 2018: 2942-2946 - [c12]Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton:
Large scale distributed neural network training through online distillation. ICLR (Poster) 2018 - [c11]Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl:
Embedding Text in Hyperbolic Spaces. TextGraphs@NAACL-HLT 2018: 59-69 - 2017
- [c10]Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl:
Neural Message Passing for Quantum Chemistry. ICML 2017: 1263-1272 - 2013
- [c9]Tara N. Sainath, Brian Kingsbury, Abdel-rahman Mohamed, George E. Dahl, George Saon
Improvements to Deep Convolutional Neural Networks for LVCSR. ASRU 2013: 315-320 - [c8]George E. Dahl, Jack W. Stokes, Li Deng, Dong Yu:
Large-scale malware classification using random projections and neural networks. ICASSP 2013: 3422-3426 - [c7]George E. Dahl, Tara N. Sainath, Geoffrey E. Hinton:
Improving deep neural networks for LVCSR using rectified linear units and dropout. ICASSP 2013: 8609-8613 - [c6]Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton:
On the importance of initialization and momentum in deep learning. ICML (3) 2013: 1139-1147 - 2012
- [c5]George E. Dahl, Ryan Prescott Adams, Hugo Larochelle:
Training Restricted Boltzmann Machines on Word Observations. ICML 2012 - 2011
- [c4]George E. Dahl, Dong Yu, Li Deng, Alex Acero
Large vocabulary continuous speech recognition with context-dependent DBN-HMMS. ICASSP 2011: 4688-4691 - [c3]Abdel-rahman Mohamed, Tara N. Sainath, George E. Dahl, Bhuvana Ramabhadran, Geoffrey E. Hinton, Michael A. Picheny:
Deep Belief Networks using discriminative features for phone recognition. ICASSP 2011: 5060-5063 - 2010
- [c2]George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton:
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. NIPS 2010: 469-477 - [c1]Ryan Prescott Adams, George E. Dahl, Iain Murray:
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. UAI 2010: 1-9
Informal and Other Publications
- 2025
- [i28]Priya Kasimbeg, Frank Schneider, Runa Eschenhagen, Juhan Bae, Chandramouli Shama Sastry, Mark Saroufim, Boyuan Feng, Less Wright, Edward Z. Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, George E. Dahl:
Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition. CoRR abs/2502.15015 (2025) - [i27]Sourabh Medapati, Priya Kasimbeg, Shankar Krishnan, Naman Agarwal, George E. Dahl:
Training neural networks faster with minimal tuning using pre-computed lists of hyperparameters for NAdamW. CoRR abs/2503.03986 (2025) - 2023
- [i26]George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson:
Benchmarking Neural Network Training Algorithms. CoRR abs/2306.07179 (2023) - 2022
- [i25]Ryan G. Gomes, Bellington Vwalika, Chace Lee, Angelica Willis, Marcin Sieniek, Joan T. Price, Christina Chen, Margaret P. Kasaro, James A. Taylor, Elizabeth M. Stringer, Scott Mayer McKinney, Ntazana Sindano, George E. Dahl, William Goodnight III, Justin Gilmer, Benjamin H. Chi, Charles Lau, Terry Spitz, T. Saensuksopa, Kris Liu, Jonny Wong, Rory Pilgrim, Akib Uddin, Greg Corrado, Lily Peng, Katherine Chou, Daniel Tse, Jeffrey S. A. Stringer, Shravya Shetty:
AI system for fetal ultrasound in low-resource settings. CoRR abs/2203.10139 (2022) - [i24]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-training helps Bayesian optimization too. CoRR abs/2207.03084 (2022) - [i23]Jeremy Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, David Cardoze, Zachary Nado, George E. Dahl, Justin Gilmer:
Adaptive Gradient Methods at the Edge of Stability. CoRR abs/2207.14484 (2022) - 2021
- [i22]Zachary Nado, Justin Gilmer, Christopher J. Shallue, Rohan Anil, George E. Dahl:
A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes. CoRR abs/2102.06356 (2021) - [i21]Samuel R. Bowman, George E. Dahl:
What Will it Take to Fix Benchmarking in Natural Language Understanding? CoRR abs/2104.02145 (2021) - [i20]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. CoRR abs/2109.08215 (2021) - [i19]Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zachary Nado, Orhan Firat:
A Loss Curvature Perspective on Training Instability in Deep Learning. CoRR abs/2110.04369 (2021) - [i18]Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl:
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. CoRR abs/2112.08250 (2021) - 2019
- [i17]Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse:
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. CoRR abs/1907.04164 (2019) - [i16]Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl:
Faster Neural Network Training with Data Echoing. CoRR abs/1907.05550 (2019) - [i15]Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl:
On Empirical Comparisons of Optimizers for Deep Learning. CoRR abs/1910.05446 (2019) - 2018
- [i14]Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton:
Large scale distributed neural network training through online distillation. CoRR abs/1804.03235 (2018) - [i13]Manoj Kumar
Parallel Architecture and Hyperparameter Search via Successive Halving and Classification. CoRR abs/1805.10255 (2018) - [i12]Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew M. Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu:
Relational inductive biases, deep learning, and graph networks. CoRR abs/1806.01261 (2018) - [i11]Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl:
Embedding Text in Hyperbolic Spaces. CoRR abs/1806.04313 (2018) - [i10]Justin Gilmer, Ryan P. Adams, Ian J. Goodfellow, David G. Andersen, George E. Dahl:
Motivating the Rules of the Game for Adversarial Example Research. CoRR abs/1807.06732 (2018) - [i9]Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George E. Dahl:
The Importance of Generation Order in Language Modeling. CoRR abs/1808.07910 (2018) - [i8]Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl:
Measuring the Effects of Data Parallelism on Neural Network Training. CoRR abs/1811.03600 (2018) - 2017
- [i7]Yun Liu
Detecting Cancer Metastases on Gigapixel Pathology Images. CoRR abs/1703.02442 (2017) - [i6]Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl:
Neural Message Passing for Quantum Chemistry. CoRR abs/1704.01212 (2017) - 2014
- [i5]George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov:
Multi-task Neural Networks for QSAR Predictions. CoRR abs/1406.1231 (2014) - [i4]Ryan Prescott Adams, George E. Dahl, Iain Murray:
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. CoRR abs/1408.2039 (2014) - 2013
- [i3]Tara N. Sainath, Brian Kingsbury, Abdel-rahman Mohamed, George E. Dahl, George Saon, Hagen Soltau, Tomás Beran, Aleksandr Y. Aravkin, Bhuvana Ramabhadran:
Improvements to deep convolutional neural networks for LVCSR. CoRR abs/1309.1501 (2013) - 2012
- [i2]George E. Dahl, Ryan Prescott Adams, Hugo Larochelle:
Training Restricted Boltzmann Machines on Word Observations. CoRR abs/1202.5695 (2012) - 2010
- [i1]Ryan Prescott Adams, George E. Dahl, Iain Murray:
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. CoRR abs/1003.4944 (2010)
Coauthor Index
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