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


Feb 16, 2021
Zachary Nado, Justin M. Gilmer, Christopher J. Shallue, Rohan Anil, George E. Dahl


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Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks


Nov 04, 2020
Zoe L. de Beurs, Andrew Vanderburg, Christopher J. Shallue, Xavier Dumusque, Andrew Collier Cameron, Lars A. Buchhave, Rosario Cosentino, Adriano Ghedina, Raphaëlle D. Haywood, Nicholas Langellier, David W. Latham, Mercedes López-Morales, Michel Mayor, Giusi Micela, Timothy W. Milbourne, Annelies Mortier, Emilio Molinari, Francesco Pepe, David F. Phillips, Matteo Pinamonti, Giampaolo Piotto, Ken Rice, Dimitar Sasselov, Alessandro Sozzetti, Stéphane Udry, Christopher A. Watson

* 26 pages, 12 figures, Submitted to the Astronomical Journal, v2 fixes some typos 

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On Empirical Comparisons of Optimizers for Deep Learning


Oct 11, 2019
Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl


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Faster Neural Network Training with Data Echoing


Jul 12, 2019
Dami Choi, Alexandre Passos, Christopher J. Shallue, George E. Dahl


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


Jul 09, 2019
Guodong Zhang, Lala Li, Zachary Nado, James Martens, Sushant Sachdeva, George E. Dahl, Christopher J. Shallue, Roger Grosse


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Measuring the Effects of Data Parallelism on Neural Network Training


Nov 21, 2018
Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl

* Submitted to JMLR 

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Embedding Text in Hyperbolic Spaces


Jun 12, 2018
Bhuwan Dhingra, Christopher J. Shallue, Mohammad Norouzi, Andrew M. Dai, George E. Dahl

* TextGraphs 2018 

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