| Post-hoc Uncertainty Calibration for Domain Drift Scenarios C Tomani, S Gruber, ME Erdem, D Cremers, F Buettner Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 64 | 2021 |
| Towards trustworthy predictions from deep neural networks with fast adversarial calibration C Tomani, F Buettner Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2020 | 28 | 2020 |
| What Makes Graph Neural Networks Miscalibrated? HHH Hsu, Y Shen, C Tomani, D Cremers Advances in Neural Information Processing Systems 36, 2022 | 23 | 2022 |
| Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration C Tomani, D Cremers, F Buettner Proceedings of the European Conference on Computer Vision (ECCV), 2022 | 18 | 2022 |
| Transforming a trained artificial intelligence model into a trustworthy artificial intelligence model F Büttner, C Tomani US Patent App. 17/524,204, 2022 | 4 | 2022 |
| Beyond In-Domain Scenarios: Robust Density-Aware Calibration C Tomani, F Waseda, Y Shen, D Cremers International Conference on Machine Learning (ICML 2023), 2023 | 2 | 2023 |
| Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model C Tomani, D Vilar, M Freitag, C Cherry, S Naskar, M Finkelstein, X Garcia, ... arXiv preprint arXiv:2310.06707, 2023 | 1* | 2023 |
| Trustworthy predictions using deep neural networks based on adversarial calibration F Büttner, C Tomani US Patent 11,455,531, 2022 | 1 | 2022 |
| CHALLENGER: Training with Attribution Maps C Tomani, D Cremers arXiv preprint arXiv:2205.15094, 2022 | 1 | 2022 |
| Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations C Tomani, K Chaudhuri, I Evtimov, D Cremers, M Ibrahim arXiv preprint arXiv:2404.10960, 2024 | | 2024 |
