4 citations · 13 across the 9 of their papers we have counts for
6 papers · 1 filter
A Neural-Network-Based Convex Regularizer for Inverse Problems
Alexis Goujon, Sebastian Neumayer, Pakshal Bohra +2
The emergence of deep-learning-based methods to solve image-reconstruction problems has enabled a significant increase in reconstruction quality. Unfortunately, these new methods o…
Mechanical Artifacts in Optical Projection Tomography: Classification and Automatic Calibration
Yan Liu, Jonathan Dong, Thanh-an Pham +2
Optical projection tomography (OPT) is a powerful tool for biomedical studies. It achieves 3D visualization of mesoscopic biological samples with high spatial resolution using conv…
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
Selin Aviyente, Alejandro Frangi, Erik Meijering +6
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical i…
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions
Stanislas Ducotterd, Alexis Goujon, Pakshal Bohra +3
Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep lear…
Delaunay-Triangulation-Based Learning with Hessian Total-Variation Regularization
Mehrsa Pourya, Alexis Goujon, Michael Unser
Regression is one of the core problems tackled in supervised learning. Rectified linear unit (ReLU) neural networks generate continuous and piecewise-linear (CPWL) mappings and are…
From Kernel Methods to Neural Networks: A Unifying Variational Formulation
Michael Unser
The minimization of a data-fidelity term and an additive regularization functional gives rise to a powerful framework for supervised learning. In this paper, we present a unifying…