2 citations · 2 across the 3 of their papers we have counts for
5 papers · 1 filter
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Alexander Immer, Matthias Bauer, Vincent Fortuin +2
Marginal-likelihood based model-selection, even though promising, is rarely used in deep learning due to estimation difficulties. Instead, most approaches rely on validation data,…
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…
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…
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…
Approximate Inference Turns Deep Networks into Gaussian Processes
Mohammad Emtiyaz Khan, Alexander Immer, Ehsan Abedi +1
Deep neural networks (DNN) and Gaussian processes (GP) are two powerful models with several theoretical connections relating them, but the relationship between their training metho…