Variational Inference

Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for …
Louis Tiao, Vincent Dutordoir, Victor Picheny
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Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Our proposed framework uses a joint probabilistic model and stochastic variational inference to improve the performance and robustness of graph convolutional networks (GCNs) in scenarios without input graph data, outperforming state-of-the-art algorithms on semi-supervised classification tasks.
Pantelis Elinas, Edwin V. Bonilla, Louis Tiao
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Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
A Handbook for Sparse Variational Gaussian Processes
A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.
Louis Tiao
Last updated on Oct 19, 2022 21 min read technical
A Handbook for Sparse Variational Gaussian Processes
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 …
Louis Tiao, Edwin V. Bonilla, Fabio Ramos
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Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
A Tutorial on Variational Autoencoders with a Concise Keras Implementation
An in-depth practical guide to variational encoders from a probabilistic perspective.
Louis Tiao
Last updated on Oct 16, 2022 24 min read technical
A Tutorial on Variational Autoencoders with a Concise Keras Implementation