2 citations · 2 across the 1 of their papers we have counts for
3 papers
Adversarial Robustness of Flow-Based Generative Models
Phillip Pope, Yogesh Balaji, Soheil Feizi
Flow-based generative models leverage invertible generator functions to fit a distribution to the training data using maximum likelihood. Despite their use in several application d…
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment
Alex Gabourie, Mohammad Rostami, Philip Pope +2
We address the problem of unsupervised domain adaptation (UDA) by learning a cross-domain agnostic embedding space, where the distance between the probability distributions of the…
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
Phillip Pope, Soheil Kolouri, Mohammad Rostrami +2
Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules. Automatic discovery of FGs will i…