L2M: Practical posterior Laplace approximation with optimization-driven second moment estimation

9 July 2021

Papers citing "L2M: Practical posterior Laplace approximation with optimization-driven second moment estimation"

9 / 9 papers shown

Title
Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting Adam D. Cobb Brian Jalaian BDL 68 76 0 14 Oct 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 80 286 0 24 Feb 2020
On Empirical Comparisons of Optimizers for Deep Learning Dami Choi Christopher J. Shallue Zachary Nado Jaehoon Lee Chris J. Maddison George E. Dahl 70 260 0 11 Oct 2019
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent Frederik Kunstner Lukas Balles Philipp Hennig 70 217 0 29 May 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 82 808 0 07 Feb 2019
Practical Gauss-Newton Optimisation for Deep Learning Aleksandar Botev H. Ritter David Barber ODL 52 231 0 12 Jun 2017
Deep Bayesian Active Learning with Image Data Y. Gal Riashat Islam Zoubin Ghahramani BDL UQCV 70 1,735 0 08 Mar 2017
Overcoming catastrophic forgetting in neural networks J. Kirkpatrick Razvan Pascanu Neil C. Rabinowitz J. Veness Guillaume Desjardins ... A. Grabska-Barwinska Demis Hassabis Claudia Clopath D. Kumaran R. Hadsell CLL 360 7,504 0 02 Dec 2016
ADADELTA: An Adaptive Learning Rate Method Matthew D. Zeiler ODL 150 6,624 0 22 Dec 2012