1 research outputs found
Efficient Deep Gaussian Process Models for Variable-Sized Input
Deep Gaussian processes (DGP) have appealing Bayesian properties, can handle
variable-sized data, and learn deep features. Their limitation is that they do
not scale well with the size of the data. Existing approaches address this
using a deep random feature (DRF) expansion model, which makes inference
tractable by approximating DGPs. However, DRF is not suitable for
variable-sized input data such as trees, graphs, and sequences. We introduce
the GP-DRF, a novel Bayesian model with an input layer of GPs, followed by DRF
layers. The key advantage is that the combination of GP and DRF leads to a
tractable model that can both handle a variable-sized input as well as learn
deep long-range dependency structures of the data. We provide a novel efficient
method to simultaneously infer the posterior of GP's latent vectors and infer
the posterior of DRF's internal weights and random frequencies. Our experiments
show that GP-DRF outperforms the standard GP model and DRF model across many
datasets. Furthermore, they demonstrate that GP-DRF enables improved
uncertainty quantification compared to GP and DRF alone, with respect to a
Bhattacharyya distance assessment. Source code is available at
https://github.com/IssamLaradji/GP_DRF.Comment: Accepted in IJCNN 201