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Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

6 June 2023
Alexander Immer
Tycho F. A. van der Ouderaa
Mark van der Wilk
Gunnar Rätsch
Bernhard Schölkopf
    BDL
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Papers citing "Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels"

12 / 12 papers shown
Title
Gradients of Functions of Large Matrices
Gradients of Functions of Large Matrices
Nicholas Krämer
Pablo Moreno-Muñoz
Hrittik Roy
Søren Hauberg
27
0
0
27 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
35
3
0
04 Mar 2024
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural
  Networks Using the Marginal Likelihood
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks Using the Marginal Likelihood
Rayen Dhahri
Alexander Immer
Bertrand Charpentier
Stephan Günnemann
Vincent Fortuin
BDL
UQCV
22
4
0
25 Feb 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou
Maria Skoularidou
Konstantina Palla
Laurence Aitchison
Julyan Arbel
...
David Rügamer
Yee Whye Teh
Max Welling
Andrew Gordon Wilson
Ruqi Zhang
UQCV
BDL
35
27
0
01 Feb 2024
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
31
12
0
09 Oct 2023
Online Laplace Model Selection Revisited
Online Laplace Model Selection Revisited
J. Lin
Javier Antorán
José Miguel Hernández-Lobato
BDL
22
3
0
12 Jul 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
BDL
MedIm
19
6
0
26 May 2023
Hyperparameter Optimization through Neural Network Partitioning
Hyperparameter Optimization through Neural Network Partitioning
Bruno Mlodozeniec
M. Reisser
Christos Louizos
27
6
0
28 Apr 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
BDL
UQCV
13
8
0
17 Apr 2023
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Zhijie Deng
Feng Zhou
Jun Zhu
BDL
42
19
0
23 Oct 2022
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics
Approximately Equivariant Networks for Imperfectly Symmetric Dynamics
Rui Wang
Robin G. Walters
Rose Yu
25
73
0
28 Jan 2022
Probing as Quantifying Inductive Bias
Probing as Quantifying Inductive Bias
Alexander Immer
Lucas Torroba Hennigen
Vincent Fortuin
Ryan Cotterell
32
14
0
15 Oct 2021
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