35 citations · 88 across the 18 of their papers we have counts for
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cs.LG2023
MAST: Model-Agnostic Sparsified Training
Yury Demidovich, Grigory Malinovsky, Egor Shulgin +1
We introduce a novel optimization problem formulation that departs from the conventional way of minimizing machine learning model loss as a black-box function. Unlike traditional f…
cs.LG2023★ 1 cited
Towards a Better Theoretical Understanding of Independent Subnetwork Training
Egor Shulgin, Peter Richtárik
Modern advancements in large-scale machine learning would be impossible without the paradigm of data-parallel distributed computing. Since distributed computing with large-scale mo…