| Backpropagating through Fr\'echet Inception Distance A Mathiasen, F Hvilshøj arXiv preprint arXiv:2009.14075, 2020 | 23 | 2020 |
| ECINN: efficient counterfactuals from invertible neural networks F Hvilshøj, A Iosifidis, I Assent arXiv preprint arXiv:2103.13701, 2021 | 20 | 2021 |
| Fast fréchet inception distance A Mathiasen, F Hvilshøj arXiv preprint arXiv:2009.14075 7, 2020 | 15 | 2020 |
| MeLIME: meaningful local explanation for machine learning models T Botari, F Hvilshøj, R Izbicki, AC de Carvalho arXiv preprint arXiv:2009.05818, 2020 | 12 | 2020 |
| On quantitative evaluations of counterfactuals F Hvilshøj, A Iosifidis, I Assent arXiv preprint arXiv:2111.00177, 2021 | 11 | 2021 |
| What if Neural Networks had SVDs? A Mathiasen, F Hvilshøj, JR Jørgensen, A Nasery, D Mottin Thirty-fourth Conference on Neural Information Processing Systems, 2020 | 8 | 2020 |
| One reflection suffice A Mathiasen, F Hvilshøj arXiv preprint arXiv:2009.14554, 2020 | 2 | 2020 |
| Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory A Mathiasen, H Helal, P Balanca, A Krzywaniak, A Parviz, F Hvilshøj, ... arXiv preprint arXiv:2402.04030, 2024 | 1 | 2024 |
| Faster Orthogonal Parameterization with Householder Matrices A Mathiasen, F Hvilshøj, JR Jørgensen, A Nasery, D Mottin ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2020 | 1 | 2020 |
| Fast and Explainable Deep Neural Networks F Hvilshøj Aarhus Universitet, 2021 | | 2021 |
