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math.OC2019
Learning deep linear neural networks: Riemannian gradient flows and convergence to global minimizers
Bubacarr Bah, Holger Rauhut, Ulrich Terstiege +1
We study the convergence of gradient flows related to learning deep linear neural networks (where the activation function is the identity map) from data. In this case, the composit…
math.OC2019
Discrete Optimization Methods for Group Model Selection in Compressed Sensing
Bubacarr Bah, Jannis Kurtz, Oliver Schaudt
In this article we study the problem of signal recovery for group models. More precisely for a given set of groups, each containing a small subset of indices, and for given linear…