Neural Additive Models: Interpretable ML with Neural Nets
Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models.
neural-additive-models interpretability feed-forward-neural-networks additive-models article code paper tutorial research arxiv:2004.13912

We propose Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models. NAMs learn a linear combination of neural networks that each attend to a single input feature. These networks are trained jointly and can learn arbitrarily complex relationships between their input feature and the output.

Official Google implementation: https://github.com/google-research/google-research/tree/master/neuraladditivemodels

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Researcher at Google Brain
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