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Tight lower bounds for the size of epsilon-nets

Publications associées (49)

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Localization errors of the stochastic heat equation

David Jean-Michel Candil

In this thesis, we study the stochastic heat equation (SHE) on bounded domains and on the whole Euclidean space

\R^d.

We confirm the intuition that as the bounded domain increases to the whole space, both solutions become arbitrarily close to one another ...

EPFL2022

Tight lower bounds for the size of epsilon-nets

Graph Chatbot

Chattez avec Graph Search

Geometric Learning: Leveraging differential geometry for learning and control

Anomalous and Chern topological waves in hyperbolic networks

Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

Collapse-invariant properties of spaces equipped with signals or directions

Localization errors of the stochastic heat equation

Realizing doubles: a conjugation zoo

UCLID-Net: Single View Reconstruction in Object Space

Better Guarantees for k-Means and Euclidean k-Median by Primal-Dual Algorithms

Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case

An Improved Analysis of Local Search for Max-Sum Diversification

Geometric Learning: Leveraging differential geometry for learning and control

UCLID-Net: Single View Reconstruction in Object Space

Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case

Anomalous and Chern topological waves in hyperbolic networks

Better Guarantees for k-Means and Euclidean k-Median by Primal-Dual Algorithms

Collapse-invariant properties of spaces equipped with signals or directions

Realizing doubles: a conjugation zoo

An Improved Analysis of Local Search for Max-Sum Diversification

Complex Representation Learning with Graph Convolutional Networks for Knowledge Graph Alignment

Localization errors of the stochastic heat equation