Adversary Detection in Neural Networks via Persistent Homology. - Thomas Gebhart; Paul Schrater (CoRR, 2017)

Path Homologies of Deep Feedforward Networks. - Samir Chowdhury 001; Thomas Gebhart; Steve Huntsman; Matvey Yutin (ICMLA, 2019)

Characterizing the Shape of Activation Space in Deep Neural Networks. - Thomas Gebhart; Paul Schrater; Alan Hylton (ICMLA, 2019)

Adversarial Examples Target Topological Holes in Deep Networks. - Thomas Gebhart; Paul Schrater (CoRR, 2019)

The Emergence of Higher-Order Structure in Scientific and Technological Knowledge Networks. - Thomas Gebhart; Russell J. Funk (CoRR, 2020)

Sheaf Neural Networks. - Jakob Hansen; Thomas Gebhart (CoRR, 2020)

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 (IEEE BigData, 2021)

A Unified Paths Perspective for Pruning at Initialization. - Thomas Gebhart; Udit Saxena; Paul Schrater (CoRR, 2021)

Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel. - Thomas Gebhart (TAG-ML, 2022)

Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding. - Thomas Gebhart; Jakob Hansen; Paul Schrater (AISTATS, 2023)

A Mathematical Framework for Citation Disruption. - Thomas Gebhart; Russell J. Funk (CoRR, 2023)

Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference. - Thomas Gebhart; John Cobb (CoRR, 2023)

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; et al. (CoRR, 2024)

Sheaf theory: from deep geometry to deep learning. - Anton Ayzenberg; Thomas Gebhart; German Magai; Grigory Solomadin (CoRR, 2025)