Discovering Invariances in Healthcare Neural Networks

8 Nov 2019Mohammad Taha BahadoriLayne C. Price

We study the invariance characteristics of pre-trained predictive models by empirically learning transformations on the input that leave the prediction function approximately unchanged. To learn invariant transformations, we minimize the Wasserstein distance between the predictive distribution conditioned on the data instances and the predictive distribution conditioned on the transformed data instances... (read more)

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