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2020 – today
- 2024
[i12]Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna, Federica Baccini, Mathilde Papillon, Miquel Ferriol Galmés, Mustafa Hajij, Theodore Papamarkou, Maria Sofia Bucarelli, Olga Zaghen, Johan Mathe, Audun Myers, Scott Mahan, Hansen Lillemark, Sharvaree P. Vadgama, Erik J. Bekkers, Tim Doster, Tegan Emerson, Henry Kvinge, Katrina Agate, Nesreen K. Ahmed, Pengfei Bai, Michael Banf, Claudio Battiloro, Maxim Beketov, Paul Bogdan, Martin Carrasco, Andrea Cavallo, Yun Young Choi, George Dasoulas, Matous Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia, Hongwei Jin, Graham Johnson
, Nikos Kanakaris, Boshko Koloski, Veljko Kovac, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long, German Magai, Alvaro Martinez, Marissa Masden, Sebastian Meznar, Bertran Miquel-Oliver, Alexis Molina, Alexander Nikitin, Marco Nurisso, Matt Piekenbrock, Yu Qin, Patryk Rygiel, Alessandro Salatiello, Max Schattauer, Pavel Snopov, Julian Suk, Valentina Sánchez, Mauricio Tec, Francesco Vaccarino, Jonas Verhellen, Frédéric Wantiez, Alexander Weers, Patrik Zajec, Blaz Skrlj, Nina Miolane:
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. CoRR abs/2409.05211 (2024)- 2023
- [c5]Thomas Gebhart, Jakob Hansen, Paul Schrater:
Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding. AISTATS 2023: 9094-9116 - [i11]Thomas Gebhart, Russell J. Funk:
A Mathematical Framework for Citation Disruption. CoRR abs/2308.16363 (2023) - [i10]Thomas Gebhart, John Cobb:
Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference. CoRR abs/2309.03773 (2023) - 2022
- [c4]Thomas Gebhart:
Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel. TAG-ML 2022: 124-132 - [i9]Thomas Gebhart:
Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel. CoRR abs/2208.09309 (2022) - 2021
- [c3]Thomas Gebhart, Xiaojun Fu, Russell J. Funk:
Go with the Flow? A Large-Scale Analysis of Health Care Delivery Networks in the United States Using Hodge Theory. IEEE BigData 2021: 3812-3823 - [i8]Thomas Gebhart, Udit Saxena, Paul Schrater:
A Unified Paths Perspective for Pruning at Initialization. CoRR abs/2101.10552 (2021) - [i7]Thomas Gebhart, Jakob Hansen, Paul Schrater:
Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding. CoRR abs/2110.03789 (2021) - [i6]Thomas Gebhart, Xiaojun Fu, Russell J. Funk:
Go with the Flow? A Large-Scale Analysis of Health Care Delivery Networks in the United States Using Hodge Theory. CoRR abs/2110.09637 (2021) - 2020
- [i5]Thomas Gebhart, Russell J. Funk:
The Emergence of Higher-Order Structure in Scientific and Technological Knowledge Networks. CoRR abs/2009.13620 (2020) - [i4]Jakob Hansen, Thomas Gebhart:
Sheaf Neural Networks. CoRR abs/2012.06333 (2020)
2010 – 2019
- 2019
- [c2]Samir Chowdhury, Thomas Gebhart, Steve Huntsman, Matvey Yutin:
Path Homologies of Deep Feedforward Networks. ICMLA 2019: 1077-1082 - [c1]Thomas Gebhart, Paul Schrater, Alan Hylton:
Characterizing the Shape of Activation Space in Deep Neural Networks. ICMLA 2019: 1537-1542 - [i3]Thomas Gebhart, Paul Schrater:
Adversarial Examples Target Topological Holes in Deep Networks. CoRR abs/1901.09496 (2019) - [i2]Samir Chowdhury, Thomas Gebhart, Steve Huntsman, Matvey Yutin:
Path homologies of deep feedforward networks. CoRR abs/1910.07617 (2019) - 2017
- [i1]Thomas Gebhart, Paul Schrater:
Adversary Detection in Neural Networks via Persistent Homology. CoRR abs/1711.10056 (2017)
Coauthor Index
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