George E. Dahl

Publication Activity (10 Years)

Years Active: 2010-2023
Publications (10 Years): 29

Top Topics

Gaussian Processes

Top Venues

TextGraphs@NAACL-HLT

Publications

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 (2023)
Jeremy M. 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 (2022)
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)
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 (2022)
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 (2022)
Samuel R. Bowman, George E. Dahl
What Will it Take to Fix Benchmarking in Natural Language Understanding? CoRR (2021)
Samuel R. Bowman, George E. Dahl
What Will it Take to Fix Benchmarking in Natural Language Understanding? NAACL-HLT (2021)
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 (2021)
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 (2021)
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 (2021)
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 (2021)
Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl
Faster Neural Network Training with Data Echoing. CoRR (2019)
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)
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 (2019)
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 (2019)
Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl
On Empirical Comparisons of Optimizers for Deep Learning. CoRR (2019)
Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton
Large scale distributed neural network training through online distillation. CoRR (2018)
Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl
Embedding Text in Hyperbolic Spaces. CoRR (2018)
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 Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu
Relational inductive biases, deep learning, and graph networks. CoRR (2018)
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 (2018)
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)
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 (2018)
Manoj Kumar, George E. Dahl, Vijay Vasudevan, Mohammad Norouzi
Parallel Architecture and Hyperparameter Search via Successive Halving and Classification. CoRR (2018)
Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George E. Dahl
The Importance of Generation Order in Language Modeling. CoRR (2018)
Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George E. Dahl
The Importance of Generation Order in Language Modeling. EMNLP (2018)
Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl
Embedding Text in Hyperbolic Spaces. TextGraphs@NAACL-HLT (2018)
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
Neural Message Passing for Quantum Chemistry. CoRR (2017)
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
Neural Message Passing for Quantum Chemistry. ICML (2017)
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
Detecting Cancer Metastases on Gigapixel Pathology Images. CoRR (2017)
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) (2015)
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 (2015)
George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov
Multi-task Neural Networks for QSAR Predictions. CoRR (2014)
Ryan Prescott Adams, George E. Dahl, Iain Murray
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. CoRR (2014)
Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton
On the importance of initialization and momentum in deep learning. ICML (3) (2013)
George E. Dahl, Tara N. Sainath, Geoffrey E. Hinton
Improving deep neural networks for LVCSR using rectified linear units and dropout. ICASSP (2013)
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 (2013)
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. ASRU (2013)
George E. Dahl, Jack W. Stokes, Li Deng, Dong Yu
Large-scale malware classification using random projections and neural networks. ICASSP (2013)
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) (2012)
Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton
Acoustic Modeling Using Deep Belief Networks. IEEE Trans. Speech Audio Process. 20 (1) (2012)
George E. Dahl, Ryan Prescott Adams, Hugo Larochelle
Training Restricted Boltzmann Machines on Word Observations. ICML (2012)
George E. Dahl, Ryan Prescott Adams, Hugo Larochelle
Training Restricted Boltzmann Machines on Word Observations CoRR (2012)
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)
George E. Dahl, Dong Yu, Li Deng, Alex Acero
Large vocabulary continuous speech recognition with context-dependent DBN-HMMS. ICASSP (2011)
George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. NIPS (2010)
Ryan Prescott Adams, George E. Dahl, Iain Murray
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes CoRR (2010)
Ryan Prescott Adams, George E. Dahl, Iain Murray
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. UAI (2010)