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DeepLIFT: Learning Important Features Through Propagating Activation Differences

DeepLIFT is a method for assigning feature importance that compares a neuron's activation to its'reference', where the reference is the activation that the neuron has when the network is provided a'reference input'.

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Cite this as

Avanti Shrikumar, Peyton Greenside, Anna Y. Shcherbina, Anshul Kundaje (2024). Dataset: DeepLIFT: Learning Important Features Through Propagating Activation Differences. https://doi.org/10.57702/ns309vo8

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Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.1605.01713
Author	Avanti Shrikumar
More Authors	Peyton Greenside Anna Y. Shcherbina Anshul Kundaje
Homepage	https://arxiv.org/abs/1704.02685