no code implementations • 15 Nov 2023 • Marcello Carioni, Subhadip Mukherjee, Hong Ye Tan, Junqi Tang
Together with a detailed survey, we provide an overview of the key mathematical results that underlie the methods reviewed in the chapter to keep our discussion self-contained.
no code implementations • 24 Feb 2023 • Simone Saitta, Marcello Carioni, Subhadip Mukherjee, Carola-Bibiane Schönlieb, Alberto Redaelli
4D flow MRI is a non-invasive imaging method that can measure blood flow velocities over time.
1 code implementation • NeurIPS 2021 • Subhadip Mukherjee, Marcello Carioni, Ozan Öktem, Carola-Bibiane Schönlieb
We propose an unsupervised approach for learning end-to-end reconstruction operators for ill-posed inverse problems.
no code implementations • 4 Jun 2021 • Georgios Batzolis, Marcello Carioni, Christian Etmann, Soroosh Afyouni, Zoe Kourtzi, Carola Bibiane Schönlieb
We model the conditional distribution of the latent encodings by modeling the auto-regressive distributions with an efficient multi-scale normalizing flow, where each conditioning factor affects image synthesis at its respective resolution scale.
1 code implementation • 21 Dec 2020 • Kristian Bredies, Marcello Carioni, Silvio Fanzon, Francisco Romero
We develop a dynamic generalized conditional gradient method (DGCG) for dynamic inverse problems with optimal transport regularization.
Numerical Analysis Numerical Analysis Optimization and Control 65K10, 65J20, 90C49, 28A33, 35F05
2 code implementations • ICLR 2019 • Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen
We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equivalent to the unconstrained min-min optimization of the p-Wasserstein distance between the encoder aggregated posterior and the prior in latent space, plus a reconstruction error.
no code implementations • 8 Feb 2016 • Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni
We prove that the empirical risk of most well-known loss functions factors into a linear term aggregating all labels with a term that is label free, and can further be expressed by sums of the loss.