DNN pruning is an approach for deep model compression, which aimsateliminating someparameters withtolerable performance degradation.

Incontrast toLearning fromDemonstration (LfD) that involves both action and state supervision, LfO is more practical in leveraging previously inapplicable resources (e.g.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Forsurfaceplotsof ResNet-56, see Figure 1.

Wedemonstrate that our visualization provides intuitive, detailed summaries of the learning dynamics beyond simple global measures (i.e., validation loss and accuracy), without the need to access validation data. Furthermore, M-PHATE better captures both the dynamics and community structure of the hidden units as compared to visualization based on standard dimensionality reduction methods (e.g., ISOMAP,t-SNE).

We baselines: FCN52], abasicnetw17], implemented 7 yondigitclassification.

LSTMs are designed to help alleviate the issue of vanishing and exploding gradients commonly associatedwithtrainingRNNs[5].

Its tendency to improve accuracy and speed up training have established BN as a favorite technique in deep learning.