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Found 2 papers, 1 papers with code
Holistic Deep Learning
This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits.
Learning High Order Feature Interactions with Fine Control Kernels
We provide a methodology for learning sparse statistical models that use as features all possible multiplicative interactions among an underlying atomic set of features.
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