56 citations · 140 across the 23 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
stat.ML2020★ 10 cited
Improving predictions of Bayesian neural nets via local linearization
Alexander Immer, Maciej Korzepa, Matthias Bauer
The generalized Gauss-Newton (GGN) approximation is often used to make practical Bayesian deep learning approaches scalable by replacing a second order derivative with a product of…
stat.ML2020★ 2 cited
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks
Alexander Immer
In this thesis, we disentangle the generalized Gauss-Newton and approximate inference for Bayesian deep learning. The generalized Gauss-Newton method is an optimization method that…
stat.ML2020★ 56 cited
Continual Deep Learning by Functional Regularisation of Memorable Past
Pingbo Pan, Siddharth Swaroop, Alexander Immer +3
Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past. Recent works address this…