70 citations · 215 across the 7 of their papers we have counts for
5 papers · 1 filter
On Empirical Comparisons of Optimizers for Deep Learning
Dami Choi, Christopher J. Shallue, Zachary Nado +3
Selecting an optimizer is a central step in the contemporary deep learning pipeline. In this paper, we demonstrate the sensitivity of optimizer comparisons to the hyperparameter tu…
Faster Neural Network Training with Data Echoing
Dami Choi, Alexandre Passos, Christopher J. Shallue +1
In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training. However, earlier stages of the training pipeline…
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Guodong Zhang, Lala Li, Zachary Nado +5
Increasing the batch size is a popular way to speed up neural network training, but beyond some critical batch size, larger batch sizes yield diminishing returns. In this work, we…
Identifying Exoplanets with Deep Learning III: Automated Triage and Vetting of TESS Candidates
Liang Yu, Andrew Vanderburg, Chelsea Huang +14
NASA's Transiting Exoplanet Survey Satellite (TESS) presents us with an unprecedented volume of space-based photometric observations that must be analyzed in an efficient and unbia…
Identifying Exoplanets with Deep Learning II: Two New Super-Earths Uncovered by a Neural Network in K2 Data
Anne Dattilo, Andrew Vanderburg, Christopher J. Shallue +10
For years, scientists have used data from NASA's Kepler Space Telescope to look for and discover thousands of transiting exoplanets. In its extended K2 mission, Kepler observed sta…