Training algorithms, broadly construed, are an essential part of every d...
Neural kernels have drastically increased performance on diverse and
non...
Bayesian deep learning seeks to equip deep neural networks with the abil...
Very little is known about the training dynamics of adaptive gradient me...
Bayesian optimization (BO) has become a popular strategy for global
opti...
Black box optimization requires specifying a search space to explore for...
In this work, we study the evolution of the loss Hessian across many
cla...
High-quality estimates of uncertainty and robustness are crucial for num...
Recently the LARS and LAMB optimizers have been proposed for training ne...
ML models often exhibit unexpectedly poor behavior when they are deploye...
Accurate estimation of predictive uncertainty in modern neural networks ...
Covariate shift has been shown to sharply degrade both predictive accura...
Selecting an optimizer is a central step in the contemporary deep learni...
Increasing the batch size is a popular way to speed up neural network
tr...
Modern machine learning methods including deep learning have achieved gr...
There is a perceived trade-off between machine learning code that is eas...