Gaussian processes for machine learning.

Currently, the data augmentation parameters are chosen by human effort and costly cross-validation, which makes it cumbersome to apply to new datasets. We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network.

It is hard to trust a black-box algorithm, and it is hard to deploy an algorithm if one does not trust its output.

Our derivation of the update for IL (Eq. 3) is based on an expansion of log We examine the consequences of this bias formula for our specific model. Note that the update term in Eq. (S1) is However, we will show in Appendix C that these updates may have high variance. 'reparameterization trick,' in which a change of variables allows the use of stochastic gradient descent It is worth noting that this'reparameterization' will work only for additive Gaussian noise. As already mentioned, WS can be viewed as a special case of IL. Since WS is a special case of IL, the bias properties of its individual samples are identical.

Because the true underlying structure of the data is rarely accessible, this "representation learning" must be