George E. Dahl

According to our database, George E. Dahl authored at least 38 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Pre-trained Gaussian Processes for Bayesian Optimization.

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

J. Mach. Learn. Res., 2024

2023

Benchmarking Neural Network Training Algorithms.

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

CoRR, 2023

2022

Adaptive Gradient Methods at the Edge of Stability.

Jeremy Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, David Cardoze, Zachary Nado, George E. Dahl, Justin Gilmer

CoRR, 2022

Pre-training helps Bayesian optimization too.

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

CoRR, 2022

AI system for fetal ultrasound in low-resource settings.

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

CoRR, 2022

A Loss Curvature Perspective on Training Instabilities of Deep Learning Models.

Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, Orhan Firat

Proceedings of the Tenth International Conference on Learning Representations, 2022

Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach.

Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

A Loss Curvature Perspective on Training Instability in Deep Learning.

Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zachary Nado, Orhan Firat

CoRR, 2021

Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers.

Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

CoRR, 2021

A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes.

Zachary Nado, Justin Gilmer, Christopher J. Shallue, Rohan Anil, George E. Dahl

CoRR, 2021

What Will it Take to Fix Benchmarking in Natural Language Understanding?

Samuel R. Bowman, George E. Dahl

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

2019

Measuring the Effects of Data Parallelism on Neural Network Training.

Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl

J. Mach. Learn. Res., 2019

On Empirical Comparisons of Optimizers for Deep Learning.

Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl

CoRR, 2019

Faster Neural Network Training with Data Echoing.

Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl

CoRR, 2019

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model.

Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Motivating the Rules of the Game for Adversarial Example Research.

Justin Gilmer, Ryan P. Adams, Ian J. Goodfellow, David G. Andersen, George E. Dahl

CoRR, 2018

Relational inductive biases, deep learning, and graph networks.

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

CoRR, 2018

Parallel Architecture and Hyperparameter Search via Successive Halving and Classification.

Manoj Kumar, George E. Dahl, Vijay Vasudevan, Mohammad Norouzi

CoRR, 2018

Embedding Text in Hyperbolic Spaces.

Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl

Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing, 2018

Large scale distributed neural network training through online distillation.

Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton

Proceedings of the 6th International Conference on Learning Representations, 2018

The Importance of Generation Order in Language Modeling.

Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George E. Dahl

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31, 2018

2017

Detecting Cancer Metastases on Gigapixel Pathology Images.

Yun Liu, Krishna Gadepalli, Mohammad Norouzi, George E. Dahl, Timo Kohlberger, Aleksey Boyko, Subhashini Venugopalan, Aleksei Timofeev, Philip Q. Nelson, Gregory S. Corrado, Jason D. Hipp, Lily Peng, Martin C. Stumpe

CoRR, 2017

Neural Message Passing for Quantum Chemistry.

Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl

Proceedings of the 34th International Conference on Machine Learning, 2017

2015

Deep Learning Approaches to Problems in Speech Recognition, Computational Chemistry, and Natural Language Text Processing.

George E. Dahl

PhD thesis, 2015

Deep Convolutional Neural Networks for Large-scale Speech Tasks.

Tara N. Sainath, Brian Kingsbury, George Saon, Hagen Soltau, Abdel-rahman Mohamed, George E. Dahl, Bhuvana Ramabhadran

Neural Networks, 2015

Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships.

Junshui Ma, Robert P. Sheridan, Andy Liaw, George E. Dahl, Vladimir Svetnik

J. Chem. Inf. Model., 2015

2014

Multi-task Neural Networks for QSAR Predictions.

George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov

CoRR, 2014

2013

On the importance of initialization and momentum in deep learning.

Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton

Proceedings of the 30th International Conference on Machine Learning, 2013

Improving deep neural networks for LVCSR using rectified linear units and dropout.

George E. Dahl, Tara N. Sainath, Geoffrey E. Hinton

Proceedings of the IEEE International Conference on Acoustics, 2013

Large-scale malware classification using random projections and neural networks.

George E. Dahl, Jack W. Stokes, Li Deng, Dong Yu

Proceedings of the IEEE International Conference on Acoustics, 2013

Improvements to Deep Convolutional Neural Networks for LVCSR.

Tara N. Sainath, Brian Kingsbury, Abdel-rahman Mohamed, George E. Dahl, George Saon, Hagen Soltau, Tomás Beran, Aleksandr Y. Aravkin, Bhuvana Ramabhadran

Proceedings of the 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 2013

2012

Acoustic Modeling Using Deep Belief Networks.

Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton

IEEE Trans. Speech Audio Process., 2012

Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition.

George E. Dahl, Dong Yu, Li Deng, Alex Acero

IEEE Trans. Speech Audio Process., 2012

Training Restricted Boltzmann Machines on Word Observations.

George E. Dahl, Ryan Prescott Adams, Hugo Larochelle

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Deep Belief Networks using discriminative features for phone recognition.

Abdel-rahman Mohamed, Tara N. Sainath, George E. Dahl, Bhuvana Ramabhadran, Geoffrey E. Hinton, Michael A. Picheny

Proceedings of the IEEE International Conference on Acoustics, 2011

Large vocabulary continuous speech recognition with context-dependent DBN-HMMS.

George E. Dahl, Dong Yu, Li Deng, Alex Acero

Proceedings of the IEEE International Conference on Acoustics, 2011

2010

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes.

Ryan Prescott Adams, George E. Dahl, Iain Murray

Proceedings of the UAI 2010, 2010

Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine.

George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010