180 citations · 291 across the 12 of their papers we have counts for
17 papers · 1 filter
The Effect of Data Dimensionality on Neural Network Prunability
Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite +2
Practitioners prune neural networks for efficiency gains and generalization improvements, but few scrutinize the factors determining the prunability of a neural network the maximum…
Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation
Cody Blakeney, Jessica Zosa Forde, Jonathan Frankle +2
Methods for improving the efficiency of deep network training (i.e. the resources required to achieve a given level of model quality) are of immediate benefit to deep learning prac…
Pruning's Effect on Generalization Through the Lens of Training and Regularization
Tian Jin, Michael Carbin, Daniel M. Roy +2
Practitioners frequently observe that pruning improves model generalization. A long-standing hypothesis based on bias-variance trade-off attributes this generalization improvement…
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
Mansheej Paul, Feng Chen, Brett W. Larsen +3
Modern deep learning involves training costly, highly overparameterized networks, thus motivating the search for sparser networks that can still be trained to the same accuracy as…
Trade-offs of Local SGD at Scale: An Empirical Study
Jose Javier Gonzalez Ortiz, Jonathan Frankle, Mike Rabbat +2
As datasets and models become increasingly large, distributed training has become a necessary component to allow deep neural networks to train in reasonable amounts of time. Howeve…
Revisiting "Qualitatively Characterizing Neural Network Optimization Problems"
Jonathan Frankle
We revisit and extend the experiments of Goodfellow et al. (2014), who showed that - for then state-of-the-art networks - "the objective function has a simple, approximately convex…