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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)PDFHTML

Papers citing "Learning Invariant Weights in Neural Networks"

17 / 17 papers shown
Title
A Complexity-Based Theory of Compositionality
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
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
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models
Emre Onal
Klemens Flöge
Emma Caldwell
A. Sheverdin
Vincent Fortuin
UQCVBDL
125
12
0
06 May 2024
A Generative Model of Symmetry Transformations
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
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
Alexander M¨ollers
Alexander Immer
Elvin Isufi
Vincent Fortuin
SSLBDLUQCV
123
1
0
30 Nov 2023
Learning Layer-wise Equivariances Automatically using Gradients
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
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
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
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
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
Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat
Alexander Immer
Hugo Yèche
Gunnar Rätsch
Vincent Fortuin
BDLMedIm
105
6
0
26 May 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
Agustinus Kristiadi
Alexander Immer
Runa Eschenhagen
Vincent Fortuin
BDLUQCV
80
10
0
17 Apr 2023
A tradeoff between universality of equivariant models and learnability
  of symmetries
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
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
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
Learning Partial Equivariances from Data
David W. Romero
Suhas Lohit
75
32
0
19 Oct 2021
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey
M. Rath
Alexandru Paul Condurache
ViTAI4CE
114
9
0
30 Jun 2020
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