| Learning simplicial complexes from persistence diagrams RL Belton, BT Fasy, R Mertz, S Micka, DL Millman, D Salinas, ... arXiv preprint arXiv:1805.10716, 2018 | 24 | 2018 |
| Reconstructing embedded graphs from persistence diagrams RL Belton, BT Fasy, R Mertz, S Micka, DL Millman, D Salinas, ... Computational Geometry 90, 101658, 2020 | 23 | 2020 |
| Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images P Lawson, J Schupbach, BT Fasy, JW Sheppard Medical Imaging 2019: Digital Pathology 10956, 72-85, 2019 | 19 | 2019 |
| Quantifying uncertainty in neural network ensembles using u-statistics J Schupbach, JW Sheppard, T Forrester 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | 9 | 2020 |
| Combining dynamic Bayesian networks and continuous time Bayesian networks for diagnostic and prognostic modeling J Schupbach, E Pryor, K Webster, J Sheppard 2022 IEEE AUTOTESTCON, 1-8, 2022 | 1 | 2022 |
| The Manifold Density Function: An Intrinsic Method for the Validation of Manifold Learning B Holmgren, E Quist, J Schupbach, BT Fasy, B Rieck arXiv preprint arXiv:2402.09529, 2024 | | 2024 |
| A Risk-Based Approach to Prognostics and Health Management Combining Bayesian Networks and Continuous-Time Bayesian Networks J Schupbach, E Pryor, K Webster, J Sheppard IEEE Instrumentation & Measurement Magazine 26 (5), 3-11, 2023 | | 2023 |
| Statistical Consulting and Research Services: Past, Present, and Future KA Flagg, C Barbour, A Mack, J Schupbach, H Zhang Montana State Univeristy, 2017 | | 2017 |
| Variance Estimation using Subbagging to Quantify Uncertainty in Neural Network Ensembles T Forrester, J Schupbach, J Sheppard | | |
