144 citations · 269 across the 8 of their papers we have counts for
3 papers · 1 filter
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…
Forest Fire Clustering for Single-cell Sequencing with Iterative Label Propagation and Parallelized Monte Carlo Simulation
Zhanlin Chen, Jeremy Goldwasser, Philip Tuckman +3
In the era of single-cell sequencing, there is a growing need to extract insights from data with clustering methods. Here, we introduce Forest Fire Clustering, an efficient and int…
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…