27 citations · 85 across the 17 of their papers we have counts for
Showing 2020 · cs.LGShow all
3 papers · 2 filters
cs.LG2020★ 2 cited
Bespoke vs. Prêt-à-Porter Lottery Tickets: Exploiting Mask Similarity for Trainable Sub-Network Finding
Michela Paganini, Jessica Zosa Forde
The observation of sparse trainable sub-networks within over-parametrized networks - also known as Lottery Tickets (LTs) - has prompted inquiries around their trainability, scaling…
cs.LG2020★ 7 cited
Streamlining Tensor and Network Pruning in PyTorch
Michela Paganini, Jessica Forde
In order to contrast the explosion in size of state-of-the-art machine learning models that can be attributed to the empirical advantages of over-parametrization, and due to the ne…
cs.LG2020★ 4 cited
On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks
Michela Paganini, Jessica Forde
We examine how recently documented, fundamental phenomena in deep learning models subject to pruning are affected by changes in the pruning procedure. Specifically, we analyze diff…