no code implementations • 4 Nov 2021 • Alaa Maalouf, Gilad Eini, Ben Mussay, Dan Feldman, Margarita Osadchy
Our approach offers a new definition of coreset, which is a natural relaxation of the standard definition and aims at approximating the \emph{average} loss of the original data over the queries.
no code implementations • 19 Aug 2020 • Ben Mussay, Daniel Feldman, Samson Zhou, Vladimir Braverman, Margarita Osadchy
Our method is based on the coreset framework and it approximates the output of a layer of neurons/filters by a coreset of neurons/filters in the previous layer and discards the rest.
no code implementations • ICLR 2020 • Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, Dan Feldman
We propose the first efficient, data-independent neural pruning algorithm with a provable trade-off between its compression rate and the approximation error for any future test sample.