Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine. - George E. Dahl; Marc'Aurelio Ranzato; Abdel-rahman Mohamed; Geoffrey E. Hinton (NIPS, 2010)

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. - Ryan Prescott Adams; George E. Dahl; Iain Murray 001 (UAI, 2010)

Large vocabulary continuous speech recognition with context-dependent DBN-HMMS. - George E. Dahl; Dong Yu 001; Li Deng 001; Alex Acero (ICASSP, 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 (ICASSP, 2011)

Training Restricted Boltzmann Machines on Word Observations. - George E. Dahl; Ryan Prescott Adams; Hugo Larochelle (ICML, 2012)

Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition. - George E. Dahl; Dong Yu 001; Li Deng 001; Alex Acero (IEEE Trans. Speech Audio Process., 2012)

Acoustic Modeling Using Deep Belief Networks. - Abdel-rahman Mohamed; George E. Dahl; Geoffrey E. Hinton (IEEE Trans. Speech Audio Process., 2012)

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 (ASRU, 2013)

Large-scale malware classification using random projections and neural networks. - George E. Dahl; Jack W. Stokes; Li Deng 001; Dong Yu 001 (ICASSP, 2013)

Improving deep neural networks for LVCSR using rectified linear units and dropout. - George E. Dahl; Tara N. Sainath; Geoffrey E. Hinton (ICASSP, 2013)

On the importance of initialization and momentum in deep learning. - Ilya Sutskever; James Martens; George E. Dahl; Geoffrey E. Hinton (ICML (3), 2013)

Multi-task Neural Networks for QSAR Predictions. - George E. Dahl; Navdeep Jaitly; Ruslan Salakhutdinov (CoRR, 2014)

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)

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 Learning Approaches to Problems in Speech Recognition, Computational Chemistry, and Natural Language Text Processing. - George E. Dahl (2015)

Neural Message Passing for Quantum Chemistry. - Justin Gilmer; Samuel S. Schoenholz; Patrick F. Riley; Oriol Vinyals; George E. Dahl (ICML, 2017)

Detecting Cancer Metastases on Gigapixel Pathology Images. - Yun Liu 013; Krishna Gadepalli; Mohammad Norouzi 002; George E. Dahl; Timo Kohlberger; Aleksey Boyko; Subhashini Venugopalan; Aleksei Timofeev; Philip Q. Nelson; Gregory S. Corrado; Jason D. Hipp; Lily Peng; et al. (CoRR, 2017)

The Importance of Generation Order in Language Modeling. - Nicolas Ford; Daniel Duckworth; Mohammad Norouzi 002; George E. Dahl (EMNLP, 2018)

Large scale distributed neural network training through online distillation. - Rohan Anil; Gabriel Pereyra; Alexandre Passos; Róbert Ormándi; George E. Dahl; Geoffrey E. Hinton (ICLR (Poster), 2018)

Embedding Text in Hyperbolic Spaces. - Bhuwan Dhingra; Christopher J. Shallue; Mohammad Norouzi 002; Andrew M. Dai; George E. Dahl (TextGraphs@NAACL-HLT, 2018)

Parallel Architecture and Hyperparameter Search via Successive Halving and Classification. - Manoj Kumar; George E. Dahl; Vijay Vasudevan; Mohammad Norouzi 002 (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 001; Çaglar Gülçehre; H. Francis Song; et al. (CoRR, 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)

Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model. - Guodong Zhang 006; Lala Li; Zachary Nado; James Martens; Sushant Sachdeva; George E. Dahl; Christopher J. Shallue; Roger B. Grosse (NeurIPS, 2019)

Faster Neural Network Training with Data Echoing. - Dami Choi; Alexandre Passos; Christopher J. Shallue; George E. Dahl (CoRR, 2019)

On Empirical Comparisons of Optimizers for Deep Learning. - Dami Choi; Christopher J. Shallue; Zachary Nado; Jaehoon Lee 001; Chris J. Maddison; George E. Dahl (CoRR, 2019)

Measuring the Effects of Data Parallelism on Neural Network Training. - Christopher J. Shallue; Jaehoon Lee 001; Joseph M. Antognini; Jascha Sohl-Dickstein; Roy Frostig; George E. Dahl (J. Mach. Learn. Res., 2019)

What Will it Take to Fix Benchmarking in Natural Language Understanding? - Samuel R. Bowman; George E. Dahl (NAACL-HLT, 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)

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 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)

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 (AISTATS, 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 (ICLR, 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 003; Elizabeth M. Stringer; Scott Mayer McKinney; Ntazana Sindano; et al. (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)

Adaptive Gradient Methods at the Edge of Stability. - Jeremy Cohen 001; Behrooz Ghorbani; Shankar Krishnan; Naman Agarwal; Sourabh Medapati; Michal Badura; Daniel Suo; David Cardoze; Zachary Nado; George E. Dahl; Justin Gilmer (CoRR, 2022)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (CoRR, 2023)

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)

Accelerating neural network training: An analysis of the AlgoPerf competition. - Priya Kasimbeg; Frank Schneider 001; Runa Eschenhagen; Juhan Bae; Chandramouli Shama Sastry; Mark Saroufim; Boyuan Feng; Less Wright; Edward Z. Yang; Zachary Nado; Sourabh Medapati; Philipp Hennig; et al. (ICLR, 2025)

How far away are truly hyperparameter-free learning algorithms? - Priya Kasimbeg; Vincent Roulet; Naman Agarwal; Sourabh Medapati; Fabian Pedregosa; Atish Agarwala; George E. Dahl (Trans. Mach. Learn. Res., 2025)

Training neural networks faster with minimal tuning using pre-computed lists of hyperparameters for NAdamW. - Sourabh Medapati; Priya Kasimbeg; Shankar Krishnan; Naman Agarwal; George E. Dahl (CoRR, 2025)