Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Session Overview | |
Location: VG2.103 |
Date: Monday, 04/Sept/2023 | |
1:30pm - 3:30pm |
MS03 1: Compressed Sensing meets Statistical Inverse Learning Location: VG2.103 Chair: Tatiana Alessandra Bubba Chair: Luca Ratti Chair: Matteo Santacesaria Compressed sensing for the sparse Radon transform Regularization for learning from unlabeled data using related labeled data Random tree Besov priors for detail detection |
4:00pm - 6:00pm |
MS03 2: Compressed Sensing meets Statistical Inverse Learning Location: VG2.103 Chair: Tatiana Alessandra Bubba Chair: Luca Ratti Chair: Matteo Santacesaria SGD for statistical inverse problems Convex regularization in statistical inverse learning problems An off-the-grid approach to multi-compartment magnetic fingerprinting How many Neurons do we need? A refined Analysis. |
Date: Tuesday, 05/Sept/2023 | |
1:30pm - 3:30pm |
MS21 1: Prior Information in Inverse Problems Location: VG2.103 Chair: Andreas Horst Chair: Jakob Lemvig Reconstructing spatio-temporal, sparse tomographic data using cylindrical shearlets Fractal priors for imaging using random wavelet trees Sampling from a posterior with Besov prior Regularizing Inverse Problems through Translation Invariant Diagonal Frame Decompositions |
4:00pm - 6:00pm |
MS21 2: Prior Information in Inverse Problems Location: VG2.103 Chair: Andreas Horst Chair: Jakob Lemvig Regularized, pretrained and subspace-restricted Deep Image Prior for CT reconstruction Monitoring of hemorrhagic stroke using Electrical Impedance Tomography Edge-preserving inversion with $\alpha$-stable priors Optimal learning of high-dimensional classification problems using deep neural networks |
Date: Wednesday, 06/Sept/2023 | |
9:00am - 11:00am |
MS30 1: Inverse Problems on Graphs and Machine Learning Location: VG2.103 Chair: Emilia Lavie Kyllikki Blåsten Chair: Matti Lassas Chair: Jinpeng Lu Continuum limit for lattice Hamiltonians Quantum computing algorithms for inverse problems on graphs Inverse problems for the graph Laplacian Inverse problems on manifolds via graph-based semi-supervised learning |
Date: Thursday, 07/Sept/2023 | |
1:30pm - 3:30pm |
MS30 2: Inverse Problems on Graphs and Machine Learning Location: VG2.103 Chair: Emilia Lavie Kyllikki Blåsten Chair: Matti Lassas Chair: Jinpeng Lu Deep Invertible Approximation of Topologically Rich Maps between Manifolds Some inverse problems on graphs with internal functionals Imaging water supply pipes using pressure waves Recontructing Interactions from Dynamics |
4:00pm - 6:00pm |
MS30 3: Inverse Problems on Graphs and Machine Learning Location: VG2.103 Chair: Emilia Lavie Kyllikki Blåsten Chair: Matti Lassas Chair: Jinpeng Lu Learned Solvers for Forward and Backward Image Flow Schemes |
Date: Friday, 08/Sept/2023 | |
1:30pm - 3:30pm |
MS59 1: Advanced Reconstruction and Phase Retrieval in Nano X-ray Tomography Location: VG2.103 Chair: Tim Salditt Chair: Anne Wald Resolution of reconstruction from discrete Radon transform data Deep Learning for Reconstruction in Nano CT Learned post-processing approaches for nano-CT reconstruction X-ray phase and dark-field retrieval from propagation-based images, via the Fokker-Planck Equation |
4:00pm - 6:00pm |
MS59 2: Advanced Reconstruction and Phase Retrieval in Nano X-ray Tomography Location: VG2.103 Chair: Tim Salditt Chair: Anne Wald Multi-stage Deep Learning Artifact Reduction for Computed Tomography Deep learning for phase retrieval from Fresnel diffraction patterns Time resolved and multi-resolution tomographic reconstruction strategies in practice. Tomographic Reconstruction in X-ray Near-field Diffractive Imaging: from Laboratory $\mu$CT to Synchrotron Nano-Imaging |