15 citations · 32 across the 24 of their papers we have counts for
6 papers · 1 filter
One for All, All for One: Coordinated Multi-Agent Diffusion Steering via Stochastic Optimal Control
Riccardo Barbano, Vincent Pauline, Runchang Li +6
Deep generative models often produce structured outputs composed of interacting components. Modelling these outputs with a single model requires learning both the component distrib…
Deep Image Prior for Computed Tomography Reconstruction
Simon Arridge, Riccardo Barbano, Alexander Denker +1
We present a comprehensive overview of the Deep Image Prior (DIP) framework and its applications to image reconstruction in computed tomography. Unlike conventional deep learning m…
Split, Skip and Play: Variance-Reduced ProxSkip for Tomography Reconstruction is Extremely Fast
Evangelos Papoutsellis, Zeljko Kereta, Kostas Papafitsoros
Many modern iterative solvers for large-scale tomographic reconstruction incur two major computational costs per iteration: expensive forward/adjoint projections to update the data…
Trajectory Stitching for Solving Inverse Problems with Flow-Based Models
Alexander Denker, Moshe Eliasof, Zeljko Kereta +1
Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial latent code (noise), such that the flow ou…
CMAD: Cooperative Multi-Agent Diffusion via Stochastic Optimal Control
Riccardo Barbano, Alexander Denker, Zeljko Kereta +2
Continuous-time generative models have achieved remarkable success in image restoration and synthesis. However, controlling the composition of multiple pre-trained models remains a…
Stochastic Gradient Descent for Nonlinear Inverse Problems in Banach Spaces
Bangti Jin, Zeljko Kereta, Yuxin Xia
Stochastic gradient descent (SGD) and its variants are widely used and highly effective optimization methods in machine learning, especially for neural network training. By using a…