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Deep Latent Force Models: ODE-based Process Convolutions for Bayesian
  Deep Learning

Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning

24 November 2023
Thomas Baldwin-McDonald
Mauricio A. Álvarez
ArXivPDFHTML

Papers citing "Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning"

3 / 3 papers shown
Title
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models
Xinxing Shi
Thomas Baldwin-McDonald
Mauricio A. Álvarez
57
0
0
01 Jul 2024
On the inability of Gaussian process regression to optimally learn
  compositional functions
On the inability of Gaussian process regression to optimally learn compositional functions
M. Giordano
Kolyan Ray
Johannes Schmidt-Hieber
29
10
0
16 May 2022
Approximate Latent Force Model Inference
Approximate Latent Force Model Inference
Jacob Moss
Felix L. Opolka
Bianca Dumitrascu
Pietro Lió
39
3
0
24 Sep 2021
1