3 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
Sparse Model Soups: A Recipe for Improved Pruning via Model Averaging
Max Zimmer, Christoph Spiegel, Sebastian Pokutta
Neural networks can be significantly compressed by pruning, yielding sparse models with reduced storage and computational demands while preserving predictive performance. Model sou…
Compression-aware Training of Neural Networks using Frank-Wolfe
Max Zimmer, Christoph Spiegel, Sebastian Pokutta
Many existing Neural Network pruning approaches rely on either retraining or inducing a strong bias in order to converge to a sparse solution throughout training. A third paradigm,…
How I Learned to Stop Worrying and Love Retraining
Max Zimmer, Christoph Spiegel, Sebastian Pokutta
Many Neural Network Pruning approaches consist of several iterative training and pruning steps, seemingly losing a significant amount of their performance after pruning and then re…
Deep Neural Network Training with Frank-Wolfe
Sebastian Pokutta, Christoph Spiegel, Max Zimmer
This paper studies the empirical efficacy and benefits of using projection-free first-order methods in the form of Conditional Gradients, a.k.a. Frank-Wolfe methods, for training N…