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DeepLIFT (Deep Learning Important FeaTures)

Project description

Algorithms for computing importance scores in deep neural networks.

Implements the methods in “Learning Important Features Through Propagating Activation Differences” by Shrikumar, Greenside & Kundaje, as well as other commonly-used methods such as gradients, guided backprop and integrated gradients. See https://github.com/kundajelab/deeplift for documentation.

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