Visualizing the PHATE of Neural Networks

Scott Gigante, Adam S. Charles, Smita Krishnaswamy, Gal Mishne

Neural Information Processing Systems 

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).

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