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Phillip E. Pope
Phillip E. Pope
University of Maryland, College Park
在 cs.umd.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Explainability methods for graph convolutional neural networks
PE Pope, S Kolouri, M Rostami, CE Martin, H Hoffmann
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
5312019
The intrinsic dimension of images and its impact on learning
P Pope, C Zhu, A Abdelkader, M Goldblum, T Goldstein
arXiv preprint arXiv:2104.08894, 2021
2192021
Sliced Wasserstein auto-encoders
S Kolouri, PE Pope, CE Martin, GK Rohde
International Conference on Learning Representations, 2018
1862018
Influence functions in deep learning are fragile
S Basu, P Pope, S Feizi
arXiv preprint arXiv:2006.14651, 2020
1772020
Sliced-wasserstein autoencoder: An embarrassingly simple generative model
S Kolouri, PE Pope, CE Martin, GK Rohde
arXiv preprint arXiv:1804.01947, 2018
902018
Stochastic training is not necessary for generalization
J Geiping, M Goldblum, PE Pope, M Moeller, T Goldstein
arXiv preprint arXiv:2109.14119, 2021
722021
A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes
M Moayeri, P Pope, Y Balaji, S Feizi
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
472022
Inverse design of two-dimensional airfoils using conditional generative models and surrogate log-likelihoods
Q Chen, J Wang, P Pope, W Chen, M Fuge
Journal of Mechanical Design 144 (2), 021712, 2022
292022
Learning a domain-invariant embedding for unsupervised domain adaptation using class-conditioned distribution alignment
AJ Gabourie, M Rostami, PE Pope, S Kolouri, K Kim
2019 57th Annual Allerton Conference on Communication, Control, and …, 2019
232019
Adversarial robustness of flow-based generative models
P Pope, Y Balaji, S Feizi
International Conference on Artificial Intelligence and Statistics, 3795-3805, 2020
172020
Discovering molecular functional groups using graph convolutional neural networks
P Pope, S Kolouri, M Rostrami, C Martin, H Hoffmann
arXiv preprint arXiv:1812.00265, 2018
152018
Learning airfoil manifolds with optimal transport
Q Chen, P Pope, M Fuge
AIAA SCITECH 2022 Forum, 2352, 2022
72022
Towards combinatorial generalization for catalysts: a kohn-sham charge-density approach
P Pope, D Jacobs
Advances in Neural Information Processing Systems 36, 2024
12024
System and method for discovering chemically active compounds of a molecule
S Kolouri, PE Pope, M Rostami, CE Martin, H Hoffmann
US Patent 11,791,018, 2023
2023
Sliced Wasserstein auto-encoders
S Kolouri, PE Pope, CE Martin, GK Rohde
International Conference on Learning Representations, 2018
2018
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