Search Results for author:
Found 8 papers, 5 papers with code
Identifying Exoplanets with Deep Learning III: Automated Triage and Vetting of TESS Candidates
We apply our model on new data from Sector 6, and present 335 new signals that received the highest scores in triage and vetting and were also identified as planet candidates by human vetters.
Earth and Planetary Astrophysics
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
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.
On Empirical Comparisons of Optimizers for Deep Learning
In particular, we find that the popular adaptive gradient methods never underperform momentum or gradient descent.
Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks
We trained our machine learning models on both simulated data (generated with the SOAP 2. 0 software; Dumusque et al. 2014) and observations of the Sun from the HARPS-N Solar Telescope (Dumusque et al. 2015; Phillips et al. 2016; Collier Cameron et al. 2019).
Embedding Text in Hyperbolic Spaces
Ideally, we could incorporate our prior knowledge of this hierarchical structure into unsupervised learning algorithms that work on text data.
Measuring the Effects of Data Parallelism on Neural Network Training
Along the way, we show that disagreements in the literature on how batch size affects model quality can largely be explained by differences in metaparameter tuning and compute budgets at different batch sizes.
Faster Neural Network Training with Data Echoing
In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training.
A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes
Recently the LARS and LAMB optimizers have been proposed for training neural networks faster using large batch sizes.
Ranked #1 on
Question Answering
on SQuAD1.1
(Hardware Burden metric)
