| Sheaf Neural Networks J Hansen, T Gebhart NeurIPS 2020 Workshop on Topological Data Analysis and Beyond, 2020 | 36 | 2020 |
| Characterizing the Shape of Activation Space in Deep Neural Networks T Gebhart, P Schrater, A Hylton 2019 18th IEEE International Conference On Machine Learning And Applications …, 2019 | 32* | 2019 |
| Adversary Detection in Neural Networks via Persistent Homology T Gebhart, P Schrater arXiv preprint arXiv:1711.10056, 2017 | 31 | 2017 |
| Path homologies of deep feedforward networks S Chowdhury, T Gebhart, S Huntsman, M Yutin 2019 18th IEEE International Conference On Machine Learning And Applications …, 2019 | 24 | 2019 |
| A unified paths perspective for pruning at initialization T Gebhart, U Saxena, P Schrater arXiv preprint arXiv:2101.10552, 2021 | 14 | 2021 |
| Go with the flow? A large-scale analysis of health care delivery networks in the United States using Hodge theory T Gebhart, X Fu, RJ Funk 2021 IEEE International Conference on Big Data, 3812-3823, 2021 | 8 | 2021 |
| The emergence of higher-order structure in scientific and technological knowledge networks T Gebhart, RJ Funk Academy of Management Proceedings 2023 (1), 12214, 2023 | 6 | 2023 |
| Applying support-vector machine learning algorithms toward predicting host–guest interactions with cucurbit [7] uril A Tabet, T Gebhart, G Wu, C Readman, MP Smela, VK Rana, C Baker, ... Physical Chemistry Chemical Physics 22 (26), 14976-14982, 2020 | 4 | 2020 |
| A mathematical framework for citation disruption T Gebhart, R Funk arXiv preprint arXiv:2308.16363, 2023 | 3 | 2023 |
| Knowledge sheaves: A sheaf-theoretic framework for knowledge graph embedding T Gebhart, J Hansen, P Schrater International Conference on Artificial Intelligence and Statistics, 9094-9116, 2023 | 3 | 2023 |
| Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel T Gebhart ICML Topological, Algebraic and Geometric Learning Workshops 2022, 124-132, 2022 | 3 | 2022 |
| Extending transductive knowledge graph embedding models for inductive logical relational inference T Gebhart, J Cobb arXiv preprint arXiv:2309.03773, 2023 | 1 | 2023 |
| Disentangling signal and noise in neural responses through generative modeling K Kay, JS Prince, T Gebhart, G Tuckute, J Zhou, T Naselaris, H Schutt bioRxiv, 2024 | | 2024 |
| Sheaf Representation Learning T Gebhart University of Minnesota, 2023 | | 2023 |
| Cryptocurrency Competition and Dynamics T Gebhart Comparative Advantage 4, 14-23, 2016 | | 2016 |
| Sheaf Laplacians and Missing Data J Cobb, T Gebhart 2024 Joint Mathematics Meetings (JMM 2024), 0 | | |
| Inferring Interaction Kernels for Stochastic Agent-Based Opinion Dynamics T Gebhart, L Huynh, V Modisette, W Thompson, M Tian, A Wiedemann, ... 2024 Joint Mathematics Meetings (JMM 2024), 0 | | |
Paul SchraterUniversity of Minnesota在 umn.edu 的电子邮件经过验证
