Learning Invariant Weights in Neural Networks

v1v2 (latest)

Learning Invariant Weights in Neural Networks

25 February 2022

Tycho F. A. van der Ouderaa

Mark van der Wilk

ArXiv (abs)PDF HTML

Papers citing "Learning Invariant Weights in Neural Networks"

17 / 17 papers shown

Title
A Complexity-Based Theory of Compositionality Eric Elmoznino Thomas Jiralerspong Yoshua Bengio Guillaume Lajoie CoGe 157 10 0 18 Oct 2024
Noether's razor: Learning Conserved Quantities Tycho F. A. van der Ouderaa Mark van der Wilk Pim de Haan 72 2 0 10 Oct 2024
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models Emre Onal Klemens Flöge Emma Caldwell A. Sheverdin Vincent Fortuin UQCV BDL 125 12 0 06 May 2024
A Generative Model of Symmetry Transformations J. Allingham Bruno Mlodozeniec Shreyas Padhy Javier Antorán David Krueger Richard E. Turner Eric T. Nalisnick José Miguel Hernández-Lobato GAN 128 5 0 04 Mar 2024
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks Alexander M¨ollers Alexander Immer Elvin Isufi Vincent Fortuin SSL BDL UQCV 123 1 0 30 Nov 2023
Learning Layer-wise Equivariances Automatically using Gradients Tycho F. A. van der Ouderaa Alexander Immer Mark van der Wilk MLT 108 14 0 09 Oct 2023
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication Irene Cannistraci Luca Moschella Marco Fumero Valentino Maiorca Emanuele Rodolà 108 14 0 02 Oct 2023
Using and Abusing Equivariance Tom Edixhoven A. Lengyel Jan van Gemert 59 3 0 22 Aug 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels Alexander Immer Tycho F. A. van der Ouderaa Mark van der Wilk Gunnar Rätsch Bernhard Schölkopf BDL 64 13 0 06 Jun 2023
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance Jinwoo Kim Tien Dat Nguyen Ayhan Suleymanzade Hyeokjun An Seunghoon Hong 100 24 0 05 Jun 2023
Improving Neural Additive Models with Bayesian Principles Kouroche Bouchiat Alexander Immer Hugo Yèche Gunnar Rätsch Vincent Fortuin BDL MedIm 105 6 0 26 May 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization Agustinus Kristiadi Alexander Immer Runa Eschenhagen Vincent Fortuin BDL UQCV 80 10 0 17 Apr 2023
A tradeoff between universality of equivariant models and learnability of symmetries Vasco Portilheiro 71 2 0 17 Oct 2022
Relaxing Equivariance Constraints with Non-stationary Continuous Filters Tycho F. A. van der Ouderaa David W. Romero Mark van der Wilk 88 37 0 14 Apr 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 147 48 0 22 Feb 2022
Learning Partial Equivariances from Data David W. Romero Suhas Lohit 77 32 0 19 Oct 2021
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey M. Rath Alexandru Paul Condurache ViT AI4CE 114 9 0 30 Jun 2020