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The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks: Roberts, Daniel A., Yaida, Sho, Hanin, Boris: 9781316519332: Amazon.com: Books

Interpretable deep learning for guided microstructure-property explorations in photovoltaics | npj Computational Materials

A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data | Nature Computational Science

A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies | Biomedical Engineering Letters

Theory-training deep neural networks for an alloy solidification benchmark problem - ScienceDirect

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The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks: Roberts, Daniel A., Yaida, Sho, Hanin, Boris: 9781316519332: Amazon.com: Books,Interpretable deep learning for guided microstructure-property explorations in photovoltaics | npj Computational Materials,A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data | Nature Computational Science,A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies | Biomedical Engineering Letters,Theory-training deep neural networks for an alloy solidification benchmark problem - ScienceDirect

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