Scalable Gradients and Variational Inference for Stochastic Differential Equations.
Xuechen LiTing-Kam Leonard WongRicky T. Q. ChenDavid Kristjanson DuvenaudPublished in: AABI (2019)
Keyphrases
- variational inference
- stochastic differential equations
- bayesian inference
- topic models
- gaussian process
- posterior distribution
- variational methods
- latent dirichlet allocation
- probabilistic model
- mixture model
- closed form
- maximum a posteriori estimation
- brownian motion
- latent variables
- approximate inference
- bayesian framework
- graphical models
- probabilistic inference
- hyperparameters
- image processing
- multiscale