Tech Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
Amazon Machine Learning Community Tech Talk, Berlin.
Density Ratio Estimation for KL Divergence Minimization between Implicit Distributions
We show how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions using density ratio estimation by probabilistic classification.
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.
Contributed Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models (TAGDM), Stockholm.
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
We derive cycle-consistent adversarial learning (CycleGAN) as a special case of variational inference in a latent-variable model with implicit priors, establishing a Bayesian …
A Tutorial on Variational Autoencoders with a Concise Keras Implementation
We give an in-depth practical guide to variational autoencoders from a probabilistic perspective.