Variational Inference

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a …

Variational Graph Convolutional Networks

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a …

A Cheatsheet for Sparse Variational Gaussian Processes

A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.

Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference

We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from …