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cs.LG2022
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives
Jonathan Warrell, Mark Gerstein
Deep Probabilistic Programming (DPP) allows powerful models based on recursive computation to be learned using efficient deep-learning optimization techniques. Additionally, DPP of…
cs.LG2018
Rank Projection Trees for Multilevel Neural Network Interpretation
Jonathan Warrell, Hussein Mohsen, Mark Gerstein
A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the o…