Tech Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
Amazon Machine Learning Community Tech Talk, Berlin.
Louis Tiao
Research Scientist
My name is Louis Tiao, and I graduated from one of Australia’s
top engineering schools with really good grades.
Now, I’m using my knowledge to help up-and-coming tech companies
make it in this competitive world.
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.
NumPy mgrid vs. meshgrid
We compare NumPy's `mgrid` and `meshgrid` for building coordinate grids — what each does, why both exist, and how broadcasting often makes them optional.