1 citations · 1 across the 3 of their papers we have counts for
4 papers
Sparse Training of Neural Networks based on Multilevel Mirror Descent
Yannick Lunk, Sebastian J. Scott, Leon Bungert
We introduce a dynamic sparse training algorithm based on linearized Bregman iterations / mirror descent that exploits the naturally incurred sparsity by alternating between period…
Efficient gradient-based methods for bilevel learning via recycling Krylov subspaces
Matthias J. Ehrhardt, Silvia Gazzola, Sebastian J. Scott
Many optimization problems require hyperparameters, i.e., parameters that must be pre-specified in advance, such as regularization parameters and parametric regularizers in variati…
Majorization-Minimization for sparse SVMs
Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini +5
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other…
On Optimal Regularization Parameters via Bilevel Learning
Matthias J. Ehrhardt, Silvia Gazzola, Sebastian J. Scott
Variational regularization is commonly used to solve linear inverse problems, and involves augmenting a data fidelity by a regularizer. The regularizer is used to promote a priori…