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Learning continuous and piecewise-linear functions and measuring their complexity29 Mar 2021

Joaquim Campos

Continuous-Domain Formulation of Inverse Problems for Composite Sparse Plus Smooth Signals08 Mar 2021

Thomas Debarre

Graphic: Graph-Based Hierarchical Clustering for Single-Molecule Localization Microscopy01 Mar 2021

Mehrsa Pourya

Inverse problems for image-based characterisation of cellular mechanics: how do cells move?23 Feb 2021

Aleix Boquet

Pycsou: A Python 3 package for solving linear inverse problems with state-of-the-art proximal algorithms22 Feb 2021

Matthieu Simeoni

Optical Diffraction Tomography with Single-Molecule Localization Microscopy08 Feb 2021

Thanh-An Pham

Recent algorithmic advances in Phase Retrieval22 Dec 2020

Jonathan Dong

Robust and Sparse Regression Models for One-Dimensional Data08 Dec 2020

Shayan Aziznejad

Shortest Multi-Spline Bases for Generalized Sampling24 Nov 2020

Alexis Goujon

A Hybrid Stochastic Framework for Signal Recovery10 Nov 2020

Pakshal Bohra

Wavelets in harmonic analysis and signal processing27 Oct 2020

Michael Unser

Optimal transport-based metric for single-molecule localization microscopy (SMLM)20 Oct 2020

Pol del Aguila Pla

Inverse Problems with Fourier-Domain Measurements and gTV Regularization:

 uniqueness and reconstruction algorithm22 Sep 2020

Thomas Debarre

Time-dependent deep image prior for dynamic MRI08 Sep 2020

Jaejun Yoo

Shortest Multi-spline Bases for Generalized Sampling03 Aug 2020

Alexis Goujon

Convex Optimization in Infinite Sums of Banach Spaces Using Besov Regularization13 Jul 2020

Benoît Sauty De Chalon

Measuring Complexity of Deep Neural Networks29 Jun 2020

Shayan Aziznejad

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging22 Jun 2020

Fangshu Yang

Space Varying Blurs: Estimation, Identification and Applications18 May 2020

Valentin Debarnot

Matrix factorization and phase retrieval for deep fluorescence microscopy11 May 2020

Jonathan Dong

CryoGAN: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning27 Apr 2020

Harshit Gupta

In this talk, we present CryoGAN, a new paradigm for single-particle cryo-EM reconstruction based on unsupervised deep adversarial learning. The major challenge in single-particle cryo-EM is that the measured particles have unknown poses. Current reconstruction techniques either estimate the poses or marginalize them away—steps that are computationally challenging. CryoGAN sidesteps this problem by using a generative adversarial network (GAN) to learn the 3D structure whose simulated projections most closely match the real data in a distributional sense. The architecture of CryoGAN resembles that of standard GAN, with the twist that the generator network is replaced by a cryo-EM physics simulator. CryoGAN is an unsupervised algorithm that only demands picked particle images and CTF estimation as inputs; no initial volume estimate or prior training are needed. Moreover, it requires minimal user interaction and can provide reconstructions in a matter of hours on a high-end GPU. The current results on synthetic datasets show that the CryoGAN can reconstruct a high-resolution volume with its adversarial learning scheme. Preliminary results on real β-galactosidase data demonstrate its ability to capture and exploit real data statistics in more challenging imaging conditions. If the time permits, we would also like to discuss its extension for multiple conformations.

CryoGAN: A New Reconstruction Paradigm for Single-particle Cryo-EM Via Deep Adversarial Learning27 Apr 2020

Laurène Donati

Gibbs Sampling-Based Statistical Inference for Inverse Problems20 Apr 2020

Pakshal Bohra

Rethinking Data Augmentation for Low-level Vision Tasks: A Comprehensive Analysis and A New Strategy "CutBlur"23 Mar 2020

Jaejun Yoo

Robust Reconstruction of Fluorescence Molecular Tomography With An Optimized Illumination Pattern04 Mar 2020

Yan Liu

Solving various domain translation problems using deep convolutional framelets11 Feb 2020

Jaejun Yoo

Adaptive regularization for three-dimensional optical diffraction tomography17 Dec 2019

Thanh-An Pham

About the use of non-imaging data to improve domain adaptation for spinal cord segmentation on MRI26 Nov 2019

Benoît Sauty De Chalon

Lagrangian Tracking of Bubbles Entrained by a Plunging Jet19 Nov 2019

Alexis Goujon

Multigrid Methods for Helmholtz equation and its application in Optical Diffraction Tomography05 Nov 2019

Tao Hong
Department of Computer Science, Technion – Israel Institute of Technology

Efficient methods for solving large scale inverse problems17 Oct 2019

Eran Treister
Computer Science Department at Ben Gurion University of the Negev, Beer Sheva, Israel

© 2010 EPFL • • 26.01.2010