Publications associées à Continuité uniforme

Stability of Image-Reconstruction Algorithms

Michaël Unser, Sebastian Jonas Neumayer, Pol del Aguila Pla

Robustness and stability of image-reconstruction algorithms have recently come under scrutiny. Their importance to medical imaging cannot be overstated. We review the known results for the topical variational regularization strategies ( ℓ2 and ℓ1 regulariz ...

2023

Graphical solutions to one-phase free boundary problems

Xavier Fernandez-Real Girona, Hui Yu

We study viscosity solutions to the classical one-phase problem and its thin counterpart. In low dimensions, we show that when the free boundary is the graph of a continuous function, the solution is the half-plane solution. This answers, in the salient di ...

Berlin2023

Universal Approximation Property of Hamiltonian Deep Neural Networks

Giancarlo Ferrari Trecate, Muhammad Zakwan

This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations. Recently, it has been shown that HDNNs enjoy, by design, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Fabio Nobile, Sundar Subramaniam Ganesh

In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of output quantities of complex differential models with random input data, using gradient-based approaches in combination with the Multi-Level Monte Carlo (MLMC) method ...

2022

On the robustness of the metric dimension of grid graphs to adding a single edge

Patrick Thiran, Gergely Odor, Satvik Mehul Mashkaria

The metric dimension (MD) of a graph is a combinatorial notion capturing the minimum number of landmark nodes needed to distinguish every pair of nodes in the graph based on graph distance. We study how much the MD can increase if we add a single edge to t ...

2022

TOPO-Loss for continuity-preserving crack detection using deep learning

Pascal Fua, Katrin Beyer, Bryan German Pantoja Rosero

We present a method for segmenting cracks in images of masonry buildings damaged by earthquakes. Existing methods of crack detection fail to preserve the continuity of cracks, and their performance deteriorates with imprecise training labels. We address th ...

2022

Do Antidune Trains Undergo Morphological Changes When These Bedforms Migrate Upstream?

Christophe Ancey, Ivan Pascal

Upstream migrating antidunes develop in streams under supercritical flow conditions. These bedforms are often organised in trains (i.e. sequences of well-developed antidunes). In shallow flows over coarse sediments, the dynamics of a single antidune can be ...

IAHR International Association for Hydro-Environment Engineering and Research2022

Uniform parsing for hyperedge replacement grammars

Petter Harald Ericson

It is well known that hyperedge-replacement grammars can generate NP-complete graph languages even under seemingly harsh restrictions. This means that the parsing problem is difficult even in the non-uniform setting, in which the grammar is considered to b ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021

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Continuité uniforme

Unlikely intersections on the p-adic formal ball

Stability of Image-Reconstruction Algorithms

Graphical solutions to one-phase free boundary problems

Universal Approximation Property of Hamiltonian Deep Neural Networks

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Preconditioners for robust optimal control problems under uncertainty

On the robustness of the metric dimension of grid graphs to adding a single edge

TOPO-Loss for continuity-preserving crack detection using deep learning

Do Antidune Trains Undergo Morphological Changes When These Bedforms Migrate Upstream?

Uniform parsing for hyperedge replacement grammars

Graph Chatbot

Chattez avec Graph Search

Graphical solutions to one-phase free boundary problems

Stability of Image-Reconstruction Algorithms

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

Preconditioners for robust optimal control problems under uncertainty

On the robustness of the metric dimension of grid graphs to adding a single edge

Uniform parsing for hyperedge replacement grammars

Universal Approximation Property of Hamiltonian Deep Neural Networks