4 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.LG2020★ 4 cited
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs
Matilde Gargiani, Andrea Zanelli, Moritz Diehl +1
Following early work on Hessian-free methods for deep learning, we study a stochastic generalized Gauss-Newton method (SGN) for training DNNs. SGN is a second-order optimization me…
cs.LG2019★ 3 cited
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
Matilde Gargiani, Aaron Klein, Stefan Falkner +1
We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based…