The left side shows the effect of pruning of neurons in the weight-matrix of afully-connected layer. The rows in white correspond to neurons pruned in theassociated layer while thecolumns inwhite represent theeffectofremoving neurons from the previous layers. On the right, we allude to the possibility of collapsing the pruned matrix into a smaller,denseone.

In many scenarios, this failure can be attributed to obscuring the crucial interplay between the training algorithm and the underlying data distribution.

The fundamental problem of non-parametric two-sample testing consists in detecting the difference between any two distributions having access only to samples from these.

Then momentum acceleration is also introduced into our dual accelerating update step.

Then momentum acceleration is also introduced into our dual accelerating update step.

Pruning methods for neural networks [LeCun et al., 1989] replace dense weight matrices by sparse

Imitation learning from human feedback studies how to train well-performed imitation agents with an annotator's relative comparison of two demonstrations