Thomas Gebhart

According to our database, Thomas Gebhart authored at least 16 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Opening Knowledge Gaps Drives Scientific Progress.

Kara Kedrick, Wenlong Yang, Thomas Gebhart, Yang Wang, Russell J. Funk

CoRR, September, 2025

Sheaf theory: from deep geometry to deep learning.

Anton Ayzenberg, Thomas Gebhart, German Magai, Grigory Solomadin

CoRR, February, 2025

Disentangling signal and noise in neural responses through generative modeling.

Kendrick N. Kay, Jacob S. Prince, Thomas Gebhart, Greta Tuckute, Jingyang Zhou, Thomas Naselaris, Heiko H. Schütt

PLoS Comput. Biol., 2025

2024

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain.

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 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

Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at ICML 2024, 2024

2023

Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference.

Thomas Gebhart, John Cobb

CoRR, 2023

A Mathematical Framework for Citation Disruption.

Thomas Gebhart, Russell J. Funk

CoRR, 2023

Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding.

Thomas Gebhart, Jakob Hansen, Paul Schrater

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel.

Thomas Gebhart

Proceedings of the Topological, 2022

2021

A Unified Paths Perspective for Pruning at Initialization.

Thomas Gebhart, Udit Saxena, Paul Schrater

CoRR, 2021

Go with the Flow? A Large-Scale Analysis of Health Care Delivery Networks in the United States Using Hodge Theory.

Thomas Gebhart, Xiaojun Fu, Russell J. Funk

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Sheaf Neural Networks.

Jakob Hansen, Thomas Gebhart

CoRR, 2020

The Emergence of Higher-Order Structure in Scientific and Technological Knowledge Networks.

Thomas Gebhart, Russell J. Funk

CoRR, 2020

2019

Adversarial Examples Target Topological Holes in Deep Networks.

Thomas Gebhart, Paul Schrater

CoRR, 2019

Characterizing the Shape of Activation Space in Deep Neural Networks.

Thomas Gebhart, Paul Schrater, Alan Hylton

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

Path Homologies of Deep Feedforward Networks.

Samir Chowdhury, Thomas Gebhart, Steve Huntsman, Matvey Yutin

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

2017

Adversary Detection in Neural Networks via Persistent Homology.

Thomas Gebhart, Paul Schrater

CoRR, 2017