| Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent R Barbano, C Zhang, S Arridge, B Jin 2020 25th International Conference on Pattern Recognition (ICPR), 1392-1399, 2020 | 8 | 2020 |
| Unsupervised Knowledge-Transfer for Learned Image Reconstruction R Barbano, Z Kereta, A Hauptmann, SR Arridge, B Jin arXiv preprint arXiv:2107.02572, 2021 | 3 | 2021 |
| Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction R Barbano, Ž Kereta, C Zhang, A Hauptmann, S Arridge, B Jin NeurIPS 2020 Workshop on Deep Learning and Inverse Problems, 2020 | 3 | 2020 |
| A Probabilistic Deep Image Prior over Image Space R Barbano, J Antoran, JM Hernández-Lobato, B Jin Fourth Symposium on Advances in Approximate Bayesian Inference, 2021 | 2 | 2021 |
| Conditional Variational Autoencoder for Learned Image Reconstruction C Zhang, R Barbano, B Jin Computation 9 (11), 114, 2021 | 2 | 2021 |
| Is Deep Image Prior in Need of a Good Education? R Barbano, J Leuschner, M Schmidt, A Denker, A Hauptmann, P Maaß, ... arXiv preprint arXiv:2111.11926, 2021 | 1 | 2021 |
| Adapting the Linearised Laplace Model Evidence for Modern Deep Learning J Antorán, D Janz, JU Allingham, E Daxberger, R Barbano, E Nalisnick, ... arXiv preprint arXiv:2206.08900, 2022 | | 2022 |
| Uncertainty quantification in medical image synthesis B Jin, R Barbano, S Arridge, R Tanno Elsevier, 2022 | | 2022 |
| A Probabilistic Deep Image Prior for Computational Tomography J Antorán, R Barbano, J Leuschner, JM Hernández-Lobato, B Jin arXiv preprint arXiv:2203.00479, 2022 | | 2022 |
| Investigating Inference in Bayesian Neural Networks via Active Learning R Barbano, J Gordon, R Pinsler, JM Hernández-Lobato | | |
| Variational Continual Learning in Deep Discriminative Models G Bae, R Barbano, J Bunker | | |
Bangti JinDepartment of Computer Science, University College LondonVerified email at ucl.ac.uk
