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Phillip Pope
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2020 – today
- 2023
- [c9]Phillip Pope, David Jacobs:
Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach. NeurIPS 2023 - [i8]Phillip Pope, David Jacobs:
Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach. CoRR abs/2310.18702 (2023) - 2022
- [c8]Mazda Moayeri, Phillip Pope, Yogesh Balaji, Soheil Feizi:
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes. CVPR 2022: 19065-19075 - [c7]Jonas Geiping, Micah Goldblum, Phillip Pope, Michael Moeller, Tom Goldstein:
Stochastic Training is Not Necessary for Generalization. ICLR 2022 - [i7]Mazda Moayeri, Phillip Pope, Yogesh Balaji, Soheil Feizi:
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes. CoRR abs/2201.10766 (2022) - 2021
- [c6]Samyadeep Basu, Phillip Pope, Soheil Feizi:
Influence Functions in Deep Learning Are Fragile. ICLR 2021 - [c5]Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein:
The Intrinsic Dimension of Images and Its Impact on Learning. ICLR 2021 - [i6]Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein:
The Intrinsic Dimension of Images and Its Impact on Learning. CoRR abs/2104.08894 (2021) - [i5]Jonas Geiping, Micah Goldblum, Phillip E. Pope, Michael Moeller, Tom Goldstein:
Stochastic Training is Not Necessary for Generalization. CoRR abs/2109.14119 (2021) - 2020
- [c4]Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. AISTATS 2020: 3795-3805 - [i4]Samyadeep Basu, Phillip Pope, Soheil Feizi:
Influence Functions in Deep Learning Are Fragile. CoRR abs/2006.14651 (2020)
2010 – 2019
- 2019
- [c3]Alexander J. Gabourie, Mohammad Rostami, Phillip E. Pope, Soheil Kolouri, Kyungnam Kim:
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment. Allerton 2019: 352-359 - [c2]Phillip E. Pope, Soheil Kolouri, Mohammad Rostami
, Charles E. Martin, Heiko Hoffmann
Explainability Methods for Graph Convolutional Neural Networks. CVPR 2019: 10772-10781 - [c1]Soheil Kolouri, Phillip E. Pope, Charles E. Martin, Gustavo K. Rohde:
Sliced Wasserstein Auto-Encoders. ICLR (Poster) 2019 - [i3]Alex Gabourie, Mohammad Rostami, Phillip E. Pope, Soheil Kolouri, Kyungnam Kim:
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment. CoRR abs/1907.02271 (2019) - [i2]Phillip Pope, Yogesh Balaji, Soheil Feizi:
Adversarial Robustness of Flow-Based Generative Models. CoRR abs/1911.08654 (2019) - 2018
- [i1]Phillip E. Pope, Soheil Kolouri, Mohammad Rostami, Charles E. Martin, Heiko Hoffmann:
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks. CoRR abs/1812.00265 (2018)
Coauthor Index
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