2k citations · 2.8k across the 4 of their papers we have counts for
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cs.LG2014★ 2k cited
FitNets: Hints for Thin Deep Nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou +3
While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed know…
cs.LG2014★ 5 cited
No more meta-parameter tuning in unsupervised sparse feature learning
Adriana Romero, Petia Radeva, Carlo Gatta
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10…