Publications associées à Topology classification with deep learning to improve real-time event selection at the LHC

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Random sampling of bandlimited signals on graphs

Pierre Vandergheynst, Rémi Gribonval, Gilles Puy, Nicolas Tremblay

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its perform ...

2015

Topology classification with deep learning to improve real-time event selection at the LHC

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Chattez avec Graph Search

Approximate Steepest Coordinate Descent

Measuring the effect of nuisance variables on classifiers

Shapes from Pixels

Influence of loading history on mechanical properties of biomaterials

On Selection of Samples in Algebraic Attacks and a New Technique to Find Hidden Low Degree Equations

Polariton interactions in semiconductor microcavities

Random sampling of bandlimited signals on graphs

Search for the process e(+)e(-) -> J/psi X(1835) at root s approximate to 10.6 GeV

SVM Active Learning Approach for Image Classification Using Spatial Information

Sampling based on timing: Time encoding machines on shift-invariant subspaces

Search for the process e(+)e(-) -> J/psi X(1835) at root s approximate to 10.6 GeV

Shapes from Pixels

Approximate Steepest Coordinate Descent

SVM Active Learning Approach for Image Classification Using Spatial Information

On Selection of Samples in Algebraic Attacks and a New Technique to Find Hidden Low Degree Equations

Measuring the effect of nuisance variables on classifiers

Influence of loading history on mechanical properties of biomaterials

Polariton interactions in semiconductor microcavities

Random sampling of bandlimited signals on graphs

Sampling based on timing: Time encoding machines on shift-invariant subspaces