57 citations · 121 across the 9 of their papers we have counts for
9 papers
Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach
Phillip Pope, David Jacobs
The Kohn-Sham equations underlie many important applications such as the discovery of new catalysts. Recent machine learning work on catalyst modeling has focused on prediction of…
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes
Mazda Moayeri, Phillip Pope, Yogesh Balaji +1
While datasets with single-label supervision have propelled rapid advances in image classification, additional annotations are necessary in order to quantitatively assess how model…
Stochastic Training is Not Necessary for Generalization
Jonas Geiping, Micah Goldblum, Phillip E. Pope +2
It is widely believed that the implicit regularization of SGD is fundamental to the impressive generalization behavior we observe in neural networks. In this work, we demonstrate t…
The Intrinsic Dimension of Images and Its Impact on Learning
Phillip Pope, Chen Zhu, Ahmed Abdelkader +2
It is widely believed that natural image data exhibits low-dimensional structure despite the high dimensionality of conventional pixel representations. This idea underlies a common…
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…