243 citations · 406 across the 14 of their papers we have counts for
5 papers · 1 filter
Prune Responsibly
Michela Paganini
Irrespective of the specific definition of fairness in a machine learning application, pruning the underlying model affects it. We investigate and document the emergence and exacer…
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…
dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration
Michela Paganini, Jessica Zosa Forde
Many research directions in machine learning, particularly in deep learning, involve complex, multi-stage experiments, commonly involving state-mutating operations acting on models…
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…
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…