Jul 12, 2023 · This work re-derives online Laplace methods, showing them to target a variational bound on a mode-corrected variant of the Laplace evidence ...
Online model selection holds the promise of automatically tuning large numbers of neural network (NN) hyperparameters during a single training run, obsoleting ...
Sep 4, 2024 · This work re-derives online Laplace methods, showing them to target a variational bound on a mode-corrected variant of the Laplace evidence which does not make ...
This work re-derives online Laplace methods, showing them to target a variational bound on a mode-corrected variant of the Laplace evidence which does not ...
Jan 9, 2024 · The Laplace approximation provides a closed-form model selection objective for neural networks (NN). Online variants, which optimise NN ...
New paper : “Online Laplace Model Selection Revisited” https://t.co/tlvSbWpI0r The online Laplace method optimises NN hyperparameters during training.
Co-authors ; Online Laplace Model Selection Revisited. JA Lin, J Antorán, JM Hernández-Lobato. Advances in Approximate Bayesian Inference, 2023. 3, 2023 ; Towards ...
Bibliographic details on Online Laplace Model Selection Revisited.
Jun 17, 2022 · The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community.
2023. [Paper]. Online Laplace Model Selection Revisited J. A. Lin, J. Antorán ... Adapting the Linearised Laplace Model Evidence for Modern Deep Learning