Riccardo Barbano

According to our database, Riccardo Barbano authored at least 19 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Image Reconstruction via Deep Image Prior Subspaces.

Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta

Trans. Mach. Learn. Res., 2024

Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research.

Victor Sabanza Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch

CoRR, 2024

DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform.

Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

CoRR, 2024

DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform.

Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Riccardo Barbano, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lió

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.

Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin

Trans. Mach. Learn. Res., 2023

Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Imaging Inverse Problems.

Riccardo Barbano, Alexander Denker, Hyungjin Chung, Tae-Hoon Roh, Simon Arrdige, Peter Maass, Bangti Jin, Jong Chul Ye

CoRR, 2023

Score-Based Generative Models for PET Image Reconstruction.

Imraj RD Singh, Alexander Denker, Riccardo Barbano, Zeljko Kereta, Bangti Jin, Kris Thielemans, Peter Maass, Simon R. Arridge

CoRR, 2023

Fast and Painless Image Reconstruction in Deep Image Prior Subspaces.

Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Zeljko Kereta, Bangti Jin

CoRR, 2023

SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.

Marco Nittscher, Michael Falk Lameter, Riccardo Barbano, Johannes Leuschner, Bangti Jin, Peter Maass

Proceedings of the Medical Imaging with Deep Learning, 2023

Sampling-based inference for large linear models, with application to linearised Laplace.

Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction.

Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maass, Bangti Jin

IEEE Trans. Computational Imaging, 2022

Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior.

Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato

CoRR, 2022

A Probabilistic Deep Image Prior for Computational Tomography.

Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin

CoRR, 2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.

Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato

Proceedings of the International Conference on Machine Learning, 2022

2021

Is Deep Image Prior in Need of a Good Education?

Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maaß, Bangti Jin

CoRR, 2021

Unsupervised Knowledge-Transfer for Learned Image Reconstruction.

Riccardo Barbano, Zeljko Kereta, Andreas Hauptmann, Simon R. Arridge, Bangti Jin

CoRR, 2021

Conditional Variational Autoencoder for Learned Image Reconstruction.

Chen Zhang, Riccardo Barbano, Bangti Jin

Comput., 2021

2020

Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction.

Riccardo Barbano, Zeljko Kereta, Chen Zhang, Andreas Hauptmann, Simon R. Arridge, Bangti Jin

CoRR, 2020

Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent.

Riccardo Barbano, Chen Zhang, Simon R. Arridge, Bangti Jin

Proceedings of the 25th International Conference on Pattern Recognition, 2020