Machine Learning

Density Ratio Estimation for KL Divergence Minimization between Implicit Distributions
This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification.
Louis Tiao
Last updated on Oct 16, 2022 21 min read Technical
Density Ratio Estimation for KL Divergence Minimization between Implicit Distributions
Building Probability Distributions with the TensorFlow Probability Bijector API
We illustrate how to build complicated probability distributions in a modular fashion using the Bijector API from TensorFlow Probability.
Louis Tiao
Last updated on Oct 16, 2022 8 min read Technical
Contributed Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
Jul 14, 2018 3:20 PM — 3:30 PM Stockholm, Sweden
Louis Tiao
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Contributed Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
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