Gradient Descent with Stochastic Subspaces via Persistence of Memory
This paper introduces the technique of "persistence of memory" to greatly extend and improve the random subspace methods, and establishes to the knowledge the first theoretical analysis of classical SSD methods for sparse functions.
Subhroshekhar Ghosh, Clement Z. Q. Ng, Pierre-Louis Poirion et al.
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