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Learning Invariant Weights in Neural Networks
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
25 February 2022
Tycho F. A. van der Ouderaa
Mark van der Wilk
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Papers citing
"Learning Invariant Weights in Neural Networks"
17 / 17 papers shown
A Complexity-Based Theory of Compositionality
Eric Elmoznino
Thomas Jiralerspong
Yoshua Bengio
Guillaume Lajoie
CoGe
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0
18 Oct 2024
Noether's razor: Learning Conserved Quantities
Neural Information Processing Systems (NeurIPS), 2024
Tycho F. A. van der Ouderaa
Mark van der Wilk
Pim de Haan
266
5
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
398
14
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
307
12
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
356
2
0
30 Nov 2023
Learning Layer-wise Equivariances Automatically using Gradients
Neural Information Processing Systems (NeurIPS), 2023
Tycho F. A. van der Ouderaa
Alexander Immer
Mark van der Wilk
MLT
377
23
0
09 Oct 2023
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication
International Conference on Learning Representations (ICLR), 2023
Irene Cannistraci
Luca Moschella
Marco Fumero
Valentino Maiorca
Emanuele Rodolà
321
22
0
02 Oct 2023
Using and Abusing Equivariance
Tom Edixhoven
A. Lengyel
Jan van Gemert
237
4
0
22 Aug 2023
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
International Conference on Machine Learning (ICML), 2023
Alexander Immer
Tycho F. A. van der Ouderaa
Mark van der Wilk
Gunnar Rätsch
Bernhard Schölkopf
BDL
295
17
0
06 Jun 2023
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
Neural Information Processing Systems (NeurIPS), 2023
Jinwoo Kim
Tien Dat Nguyen
Ayhan Suleymanzade
Hyeokjun An
Seunghoon Hong
445
30
0
05 Jun 2023
Improving Neural Additive Models with Bayesian Principles
International Conference on Machine Learning (ICML), 2023
Kouroche Bouchiat
Alexander Immer
Hugo Yèche
Gunnar Rätsch
Vincent Fortuin
BDL
MedIm
695
14
0
26 May 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
Agustinus Kristiadi
Alexander Immer
Runa Eschenhagen
Vincent Fortuin
BDL
UQCV
363
12
0
17 Apr 2023
A tradeoff between universality of equivariant models and learnability of symmetries
Vasco Portilheiro
298
2
0
17 Oct 2022
Relaxing Equivariance Constraints with Non-stationary Continuous Filters
Neural Information Processing Systems (NeurIPS), 2022
Tycho F. A. van der Ouderaa
David W. Romero
Mark van der Wilk
316
45
0
14 Apr 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Neural Information Processing Systems (NeurIPS), 2022
Alexander Immer
Tycho F. A. van der Ouderaa
Gunnar Rätsch
Vincent Fortuin
Mark van der Wilk
BDL
450
54
0
22 Feb 2022
Learning Partial Equivariances from Data
David W. Romero
Suhas Lohit
429
46
0
19 Oct 2021
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey
M. Rath
Alexandru Paul Condurache
ViT
AI4CE
429
15
0
30 Jun 2020
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