George E. Dahl

55 papers A* 5A 2C 1Misc 4Journal 37Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	What do near-optimal learning rate schedules look like? CoRR Hiroki Naganuma, Atish Agarwala, Priya Kasimbeg, George E. Dahl
2025	J	`jnl`	Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition. CoRR 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
2025	A*	`conf`	Accelerating neural network training: An analysis of the AlgoPerf competition. ICLR 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
2025	J	`jnl`	How far away are truly hyperparameter-free learning algorithms? CoRR Priya Kasimbeg, Vincent Roulet, Naman Agarwal, Sourabh Medapati, Fabian Pedregosa, Atish Agarwala, George E. Dahl
2025	J	`jnl`	How far away are truly hyperparameter-free learning algorithms? Trans. Mach. Learn. Res. Priya Kasimbeg, Vincent Roulet, Naman Agarwal, Sourabh Medapati, Fabian Pedregosa, Atish Agarwala, George E. Dahl
2025	J	`jnl`	Training neural networks faster with minimal tuning using pre-computed lists of hyperparameters for NAdamW. CoRR Sourabh Medapati, Priya Kasimbeg, Shankar Krishnan, Naman Agarwal, George E. Dahl
2024	J	`jnl`	Pre-trained Gaussian Processes for Bayesian Optimization. J. Mach. Learn. Res. Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
2023	J	`jnl`	Benchmarking Neural Network Training Algorithms. CoRR 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
2022	J	`jnl`	AI system for fetal ultrasound in low-resource settings. CoRR 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
2022	J	`jnl`	Adaptive Gradient Methods at the Edge of Stability. CoRR Jeremy Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, David Cardoze, Zachary Nado, George E. Dahl, Justin Gilmer
2022	J	`jnl`	Pre-training helps Bayesian optimization too. CoRR Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
2022	A	`conf`	Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. AISTATS Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl
2021	J	`jnl`	A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes. CoRR Zachary Nado, Justin Gilmer, Christopher J. Shallue, Rohan Anil, George E. Dahl
2021	J	`jnl`	A Loss Curvature Perspective on Training Instability in Deep Learning. CoRR Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zachary Nado, Orhan Firat
2021	J	`jnl`	Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. CoRR Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
2021	J	`jnl`	Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. CoRR Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl
2021	—	`conf`	What Will it Take to Fix Benchmarking in Natural Language Understanding? NAACL-HLT Samuel R. Bowman, George E. Dahl
2021	J	`jnl`	What Will it Take to Fix Benchmarking in Natural Language Understanding? CoRR Samuel R. Bowman, George E. Dahl
2019	J	`jnl`	Faster Neural Network Training with Data Echoing. CoRR Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl
2019	J	`jnl`	Measuring the Effects of Data Parallelism on Neural Network Training. J. Mach. Learn. Res. Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl
2019	J	`jnl`	On Empirical Comparisons of Optimizers for Deep Learning. CoRR Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl
2019	A*	`conf`	Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. NeurIPS Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse
2019	J	`jnl`	Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. CoRR Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger B. Grosse
2018	—	`conf`	Embedding Text in Hyperbolic Spaces. TextGraphs@NAACL-HLT Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl
2018	J	`jnl`	Embedding Text in Hyperbolic Spaces. CoRR Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl
2018	—	`conf`	Large scale distributed neural network training through online distillation. ICLR (Poster) Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton
2018	J	`jnl`	Large scale distributed neural network training through online distillation. CoRR Rohan Anil, Gabriel Pereyra, Alexandre Passos, Róbert Ormándi, George E. Dahl, Geoffrey E. Hinton
2018	J	`jnl`	Measuring the Effects of Data Parallelism on Neural Network Training. CoRR Christopher J. Shallue, Jaehoon Lee, Joseph M. Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl
2018	J	`jnl`	Motivating the Rules of the Game for Adversarial Example Research. CoRR Justin Gilmer, Ryan P. Adams, Ian J. Goodfellow, David G. Andersen, George E. Dahl
2018	J	`jnl`	Parallel Architecture and Hyperparameter Search via Successive Halving and Classification. CoRR Manoj Kumar, George E. Dahl, Vijay Vasudevan, Mohammad Norouzi
2018	J	`jnl`	Relational inductive biases, deep learning, and graph networks. CoRR 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
2018	A*	`conf`	The Importance of Generation Order in Language Modeling. EMNLP Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George E. Dahl
2018	J	`jnl`	The Importance of Generation Order in Language Modeling. CoRR Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George E. Dahl
2017	J	`jnl`	Detecting Cancer Metastases on Gigapixel Pathology Images. CoRR 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
2017	A*	`conf`	Neural Message Passing for Quantum Chemistry. ICML Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
2017	J	`jnl`	Neural Message Passing for Quantum Chemistry. CoRR Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
2015	J	`jnl`	Deep Convolutional Neural Networks for Large-scale Speech Tasks. Neural Networks Tara N. Sainath, Brian Kingsbury, George Saon, Hagen Soltau, Abdel-rahman Mohamed, George E. Dahl, Bhuvana Ramabhadran
2015	—	`—`	Deep Learning Approaches to Problems in Speech Recognition, Computational Chemistry, and Natural Language Text Processing. George E. Dahl
2015	J	`jnl`	Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships. J. Chem. Inf. Model. Junshui Ma, Robert P. Sheridan, Andy Liaw, George E. Dahl, Vladimir Svetnik
2014	J	`jnl`	Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. CoRR Ryan Prescott Adams, George E. Dahl, Iain Murray
2014	J	`jnl`	Multi-task Neural Networks for QSAR Predictions. CoRR George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov
2013	C	`conf`	Improvements to Deep Convolutional Neural Networks for LVCSR. ASRU Tara N. Sainath, Brian Kingsbury, Abdel-rahman Mohamed, George E. Dahl, George Saon, Hagen Soltau, Tomás Beran, Aleksandr Y. Aravkin, Bhuvana Ramabhadran
2013	J	`jnl`	Improvements to deep convolutional neural networks for LVCSR. CoRR Tara N. Sainath, Brian Kingsbury, Abdel-rahman Mohamed, George E. Dahl, George Saon, Hagen Soltau, Tomás Beran, Aleksandr Y. Aravkin, Bhuvana Ramabhadran
2013	Misc	`conf`	Improving deep neural networks for LVCSR using rectified linear units and dropout. ICASSP George E. Dahl, Tara N. Sainath, Geoffrey E. Hinton
2013	Misc	`conf`	Large-scale malware classification using random projections and neural networks. ICASSP George E. Dahl, Jack W. Stokes, Li Deng, Dong Yu
2013	—	`conf`	On the importance of initialization and momentum in deep learning. ICML (3) Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton
2012	J	`jnl`	Acoustic Modeling Using Deep Belief Networks. IEEE Trans. Speech Audio Process. Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton
2012	J	`jnl`	Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition. IEEE Trans. Speech Audio Process. George E. Dahl, Dong Yu, Li Deng, Alex Acero
2012	J	`jnl`	Training Restricted Boltzmann Machines on Word Observations CoRR George E. Dahl, Ryan Prescott Adams, Hugo Larochelle
2012	A*	`conf`	Training Restricted Boltzmann Machines on Word Observations. ICML George E. Dahl, Ryan Prescott Adams, Hugo Larochelle
2011	Misc	`conf`	Deep Belief Networks using discriminative features for phone recognition. ICASSP Abdel-rahman Mohamed, Tara N. Sainath, George E. Dahl, Bhuvana Ramabhadran, Geoffrey E. Hinton, Michael A. Picheny
2011	Misc	`conf`	Large vocabulary continuous speech recognition with context-dependent DBN-HMMS. ICASSP George E. Dahl, Dong Yu, Li Deng, Alex Acero
2010	J	`jnl`	Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes CoRR Ryan Prescott Adams, George E. Dahl, Iain Murray
2010	A	`conf`	Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. UAI Ryan Prescott Adams, George E. Dahl, Iain Murray
2010	—	`conf`	Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. NIPS George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton