Multiresolution methods for large-scale learning
Abstract
There is a surge of new work at the intersection of multiresolution/multiscale methods and machine learning:
- Multiresolution (wavelets) on graphs is one of the hottest topics in harmonic analysis, with important implications for learning on graphs and semi-spervised learning.
- Hierarchical matrices (HODLR, H, H2 and HSS matrices), a very active area in numerical analysis, have also been shown to be effective in Gaussian processes inference.
- Scattering networks are a major breakthrough, and combine ideas from wavelet analysis and deep learning.
- Multiscale graph models are ever more popular because they can capture important structures in real world networks.
- Multiscale matrix decompositions and multiresolution matrix factorizations, mirroring some features of algebraic multigrid methods, are gaining traction in large scale data applications.
The goal of this workshop is to bring together leading researchers from Harmonic Analysis, Signal Processing, Numerical Analysis, and Machine Learning, to explore the synergies between all the above lines of work.
Video
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