approximation deep

The 10 most recent episodes and tracks on this topic.

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Nonlinear approximation by deep ReLU networks - Ron DeVore, Texas A&MMathematics of data: Structured representations for sensing, approximation and learning Notes
Two decentralised learning problems: Sketching and policy evaluation - Justin Romberg, Georgia TechMathematics of data: Structured representations for sensing, approximation and learning Notes
Clustering and classification from the core to the edge - Thomas Strohmer, California UniversityMathematics of data: Structured representations for sensing, approximation and learning Notes
The mother of all representer theorems for inverse problems & machine learning - Michael UnserMathematics of data: Structured representations for sensing, approximation and learning Notes
From shallow to deep learning for inverse imaging problems - Carola-Bibiane Schönlieb, CambridgeMathematics of data: Structured representations for sensing, approximation and learning Notes
SketchySVD - Joel Tropp, California Institute of TechnologyMathematics of data: Structured representations for sensing, approximation and learning Notes
Optimal transport for machine learning - Gabriel Peyre, Ecole Normale SuperieureMathematics of data: Structured representations for sensing, approximation and learning Notes
On the (unreasonable) effectiveness of compressive imaging – Ben Adcock, Simon Fraser UniversityMathematics of data: Structured representations for sensing, approximation and learning Notes
Deep dictionary learning approaches for image super-resolution - Pier Luigi Dragotti, ImperialMathematics of data: Structured representations for sensing, approximation and learning Notes
Mad Max: Affine spline insights into deep learning - Richard Baraniuk, Rice UniversityMathematics of data: Structured representations for sensing, approximation and learning Notes

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