Riccardo Barbano
Riccardo Barbano
Technical University of Munich, University College London
Verified email at - Homepage
Cited by
Cited by
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
J Antorán, D Janz, JU Allingham, E Daxberger, RR Barbano, E Nalisnick, ...
International Conference on Machine Learning, 796-821, 2022
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
R Barbano, J Leuschner, M Schmidt, A Denker, A Hauptmann, P Maaß, ...
IEEE Transactions on Computational Imaging 8, 1210-1222, 2022
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
Uncertainty quantification in medical image synthesis
R Barbano, S Arridge, B Jin, R Tanno
Biomedical Image Synthesis and Simulation, 601-641, 2022
Conditional Variational Autoencoder for Learned Image Reconstruction
C Zhang, R Barbano, B Jin
Computation 9 (11), 114, 2021
Sampling-based inference for large linear models, with application to linearised Laplace
J Antorán, S Padhy, R Barbano, E Nalisnick, D Janz, ...
International Conference on Learning Representations (ICLR), 2023, 2022
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
J Antorán, R Barbano, J Leuschner, JM Hernández-Lobato, B Jin
Transactions on Machine Learning Research (12/2023), 2022
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
Unsupervised Knowledge-Transfer for Learned Image Reconstruction
R Barbano, Z Kereta, A Hauptmann, SR Arridge, B Jin
Inverse Problems 38 (10), 104004, 2022
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
R Barbano, J Leuschner, J Antorán, B Jin, JM Hernández-Lobato
Adaptive Experimental Design and Active Learning workshop at ICML 2022, 2022
Steerable conditional diffusion for out-of-distribution adaptation in imaging inverse problems
R Barbano, A Denker, H Chung, TH Roh, S Arrdige, P Maass, B Jin, ...
arXiv preprint arXiv:2308.14409, 2023
A Probabilistic Deep Image Prior over Image Space
R Barbano, J Antorán, JM Hernández-Lobato, B Jin
Fourth Symposium on Advances in Approximate Bayesian Inference, 2022
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
M Nittscher, M Lameter, R Barbano, J Leuschner, B Jin, P Maass
Medical Imaging with Deep Learning, 2023, 2023
Score-based generative models for PET image reconstruction
IRD Singh, A Denker, R Barbano, Ž Kereta, B Jin, K Thielemans, P Maass, ...
arXiv preprint arXiv:2308.14190, 2023
Image Reconstruction via Deep Image Prior Subspaces
R Barbano, J Antorán, J Leuschner, JM Hernández-Lobato, Ž Kereta, ...
Transactions on Machine Learning Research (1/2024), 2023
3D PET-DIP reconstruction with relative difference prior using a SIRF-based objective
I Singh, R Barbano, Z Kereta, B Jin, K Thielemans, S Arridge
Fully3D, 2023
Deep Image Prior PET Reconstruction using a SIRF-Based Objective
IRD Singh, R Barbano, R Twyman, Ž Kereta, B Jin, S Arridge, ...
2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC …, 2022
MR-blob: Coordinate-Transformed Blobs for Parallel MRI Reconstruction
Z Kereta, A Denker, R Barbano, B Jin, K Thielemans, S Arrdige, I Singh
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
The system can't perform the operation now. Try again later.
Articles 1–20