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  • Latent GP-ODEs with Informative Priors - OpenReview
    Our model learns a physically meaningful latent representation (position, momentum) and solves in the latent space an ODE system The use of GP allows us to account for uncertainty as well as to extend our work with informative priors We demonstrate our framework on an image rotation dataset
  • IlzeAmandaA VAE-GP-ODE - GitHub
    We propose VAE-GP-ODE: a probabilistic dynamic model that extends previous work by learning dynamics from high-dimensional data with a structured GP prior Our model is trained end-to-end using variational inference The code was developed and tested with python3 8 and Pytorch 1 13
  • Gaussian Process Latent Variable Models (GPLVM) with SVI
    GPLVMs use Gaussian processes in an unsupervised context, where a low dimensional representation of the data (X ≡ {x n} n = 1 N ∈ R N × Q) is learnt given some high dimensional real valued observations Y ≡ {y n} n = 1 N ∈ R N × D Q <D provides dimensionality reduction
  • latent Gaussian process ODEs - arXiv. org
    We employ latent Gaussian process ordinary differential equations (GP-ODEs) for dynamics learning, allowing to learn complex relationships between interacting objects without the need of having access to fully observed systems
  • Latent GP-ODEs with Informative Priors - GitHub Pages
    We combine a VAE with a GP-ODE The resulting dynamics are learned in a latent space and our model supports both 1st and 2nd order diferential euqations We train our model end-to-end because decoupled training leads to embeddings that are unconstrained by the dynamics of the observed process, leading to poor generalization (see Pretrained VAE)
  • NeurIPS Latent GP-ODEs with Informative Priors
    Our model learns a physically meaningful latent representation (position, momentum) and solves in the latent space an ODE system The use of GP allows us to account for uncertainty as well as to extend our work with informative priors
  • GPLaSDI: Gaussian Process-based interpretable Latent Space Dynamics . . .
    In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations Using GPs offers two significant advantages First, it enables the quantification of uncertainty over the ROM predictions
  • Hierarchical Gaussian Process Latent Variable Models
    The Gaussian process latent variable model (GP-LVM) is a powerful approach for prob-abilistic modelling of high dimensional data through dimensional reduction In this paper we extend the GP-LVM through hierarchies A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure





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