23 citations · 27 across the 3 of their papers we have counts for
5 papers · 1 filter
Influence Functions in Deep Learning Are Fragile
Samyadeep Basu, Philip Pope, Soheil Feizi
Influence functions approximate the effect of training samples in test-time predictions and have a wide variety of applications in machine learning interpretability and uncertainty…
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…
Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model
Soheil Kolouri, Phillip E. Pope, Charles E. Martin +1
In this paper we study generative modeling via autoencoders while using the elegant geometric properties of the optimal transport (OT) problem and the Wasserstein distances. We int…