Publications related to A Primal-Dual Reconstruction Algorithm for Fluorescence and Bioluminescence Tomography

Towards Trustworthy Deep Learning for Image Reconstruction

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

EPFL2024

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

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The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

EPFL2022

Privacy-Enhancing Optical Embeddings for Lensless Classification

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Lensless imaging can provide visual privacy due to the highly multiplexed characteristic of its measurements. However, this alone is a weak form of security, as various adversarial attacks can be designed to invert the one-to-many scene mapping of such cam ...

2022

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Large Steps in Inverse Rendering of Geometry

Wenzel Alban Jakob, Baptiste Alexandre Marie Philippe Claude Nicolet

Inverse reconstruction from images is a central problem in many scientific and engineering disciplines. Recent progress on differentiable rendering has led to methods that can efficiently differentiate the full process of image formation with respect to mi ...

ASSOC COMPUTING MACHINERY2021

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

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Stochastic gradient descent (SGD) and randomized coordinate descent (RCD) are two of the workhorses for training modern automated decision systems. Intriguingly, convergence properties of these methods are not well-established as we move away from the spec ...

EPFL2021

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paper studies exploiting action-level learning (imitation) in the optimal control problem context. Cost functions defined by the optimal control methods are similar to the goal-level learning (emulation) in animals. However, imitating the robot's or others ...

IEEE2021

Passenger-centric timetable rescheduling: A user equilibrium approach

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Unexpected disruptions commonly occur in the railway network, causing delays, and extra cost for operators and inconvenience for passengers by missing their connection and facing overcrowded trains. This paper presents a passenger-centric approach for time ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Matthieu Martin Jean-André Simeoni

Many scientific inquiries in natural sciences involve approximating a spherical field -namely a scalar quantity defined over a continuum of directions- from generalised samples of the latter (e.g. directional samples, local averages, etc). Such an approxim ...

EPFL2020

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Michaël Unser, Michael Thompson McCann

This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most ima ...

2019

A Primal-Dual Reconstruction Algorithm for Fluorescence and Bioluminescence Tomography

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Towards Trustworthy Deep Learning for Image Reconstruction

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Privacy-Enhancing Optical Embeddings for Lensless Classification

A first-order primal-dual method with adaptivity to local smoothness

Large Steps in Inverse Rendering of Geometry

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Passenger-centric timetable rescheduling: A user equilibrium approach

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

A first-order primal-dual method with adaptivity to local smoothness

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Functional Inverse Problems on Spheres: Theory, Algorithms and Applications

Passenger-centric timetable rescheduling: A user equilibrium approach

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Large Steps in Inverse Rendering of Geometry

Privacy-Enhancing Optical Embeddings for Lensless Classification

Towards Trustworthy Deep Learning for Image Reconstruction

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills