35 citations · 163 across the 31 of their papers we have counts for
3 papers · 1 filter
Structured adaptive and random spinners for fast machine learning computations
Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski +6
We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy. The proposed framework relies on projections v…
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations
Yoann Isaac, Quentin Barthélemy, Cédric Gouy-Pailler +2
This paper addresses the structurally-constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries. The contribution of…
TripleSpin - a generic compact paradigm for fast machine learning computations
Krzysztof Choromanski, Francois Fagan, Cedric Gouy-Pailler +3
We present a generic compact computational framework relying on structured random matrices that can be applied to speed up several machine learning algorithms with almost no loss o…