Abstract: Deep Gaussian processes (DGP) have appealing Bayesian properties, can handle variable-sized data, and learn deep features.

May 16, 2019 · Abstract—Deep Gaussian processes (DGP) have appealing. Bayesian properties, can handle variable-sized data, and learn deep features.

Efficient Deep Gaussian Process Models for Variable-Sized Inputs. Issam H. Laradji, Mark Schmidt, Vladimir Pavlovic, Minyoung Kim. Related Work. Motivation.

This repository combines Gaussian processes (GP), deep random feature (DRF) model, and our GP-DRF model. Requirements. Pytorch version 0.4 or higher. Running ...

PDF | On Jul 1, 2019, Issam H. Laradji and others published Efficient Deep Gaussian Process Models for Variable-Sized Inputs | Find, read and cite all the

The GP-DRF is introduced, a novel Bayesian model with an input layer of GPs, followed by DRF layers that enables improved uncertainty quantification ...

Deep Gaussian processes (DGP) have appealing Bayesian properties, can handle variable-sized data, and learn deep features. Their limitation is that they do ...

Deep Gaussian processes (DGP) have appealing Bayesian properties, can handle variable-sized data, and learn deep features. Their limitation is that they do ...

Fingerprint. Dive into the research topics of 'Efficient Deep Gaussian Process Models for Variable-Sized Inputs'. Together they form a unique fingerprint.

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 ...