34 citations · 269 across the 42 of their papers we have counts for
10 papers · 1 filter
Provable Filter Pruning for Efficient Neural Networks
Lucas Liebenwein, Cenk Baykal, Harry Lang +2
We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized…
Introduction to Coresets: Accurate Coresets
Ibrahim Jubran, Alaa Maalouf, Dan Feldman
A coreset (or core-set) of an input set is its small summation, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem…
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski +2
We introduce a pruning algorithm that provably sparsifies the parameters of a trained model in a way that approximately preserves the model's predictive accuracy. Our algorithm use…
Tight Sensitivity Bounds For Smaller Coresets
Alaa Maalouf, Adiel Statman, Dan Feldman
An -coreset for Least-Mean-Squares (LMS) of a matrix is a small weighted subset of its rows that approximates the sum of squared distanc…
Data-Independent Neural Pruning via Coresets
Ben Mussay, Margarita Osadchy, Vladimir Braverman +2
Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy. Model compression became a central research topic,…
Coresets for Gaussian Mixture Models of Any Shape
Dan Feldman, Zahi Kfir, Xuan Wu
An -coreset for a given set of points, is usually a small weighted set, such that querying the coreset \emph{provably} yields a -factor approx…