Learning Invariances using the Marginal Likelihood

Learning Invariances using the Marginal Likelihood

16 August 2018

Mark van der Wilk

Papers citing "Learning Invariances using the Marginal Likelihood"

18 / 18 papers shown

Title
A Complexity-Based Theory of Compositionality Eric Elmoznino Thomas Jiralerspong Yoshua Bengio Guillaume Lajoie CoGe 61 4 0 18 Oct 2024
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization Agustinus Kristiadi Felix Dangel Philipp Hennig 22 11 0 14 Feb 2023
A tradeoff between universality of equivariant models and learnability of symmetries Vasco Portilheiro 25 2 0 17 Oct 2022
A Simple Strategy to Provable Invariance via Orbit Mapping Kanchana Vaishnavi Gandikota Jonas Geiping Zorah Lähner Adam Czapliñski Michael Moeller AAML 3DPC 18 3 0 24 Sep 2022
Relaxing Equivariance Constraints with Non-stationary Continuous Filters Tycho F. A. van der Ouderaa David W. Romero Mark van der Wilk 24 33 0 14 Apr 2022
Regularising for invariance to data augmentation improves supervised learning Aleksander Botev Matthias Bauer Soham De 30 14 0 07 Mar 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 52 56 0 23 Feb 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer Tycho F. A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk BDL 29 44 0 22 Feb 2022
Deep invariant networks with differentiable augmentation layers Cédric Rommel Thomas Moreau Alexandre Gramfort OOD 17 8 0 04 Feb 2022
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization Xiaojun Xu Jacky Y. Zhang Evelyn Ma Danny Son Oluwasanmi Koyejo Bo-wen Li 20 10 0 03 Feb 2022
Residual Pathway Priors for Soft Equivariance Constraints Marc Finzi Gregory W. Benton A. Wilson BDL UQCV 24 50 0 02 Dec 2021
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect Lorenzo Noci Kevin Roth Gregor Bachmann Sebastian Nowozin Thomas Hofmann CML 27 23 0 11 Jun 2021
Data augmentation in Bayesian neural networks and the cold posterior effect Seth Nabarro Stoil Ganev Adrià Garriga-Alonso Vincent Fortuin Mark van der Wilk Laurence Aitchison BDL 21 37 0 10 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 29 124 0 14 May 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 21 107 0 24 Feb 2021
Neural Network Approximations for Calabi-Yau Metrics Vishnu Jejjala D. M. Peña Challenger Mishra 24 51 0 31 Dec 2020
A Bayesian Perspective on Training Speed and Model Selection Clare Lyle Lisa Schut Binxin Ru Y. Gal Mark van der Wilk 34 23 0 27 Oct 2020
A Kernel Theory of Modern Data Augmentation Tri Dao Albert Gu Alexander J. Ratner Virginia Smith Christopher De Sa Christopher Ré 19 190 0 16 Mar 2018