April 17, 2020

Langevin Monte Carlo is a class of Markov Chain Monte Carlo algorithms that generate samples from a probability distribution of interest by simulating the Langevin Equation. This post explores the basics of Langevin Monte Carlo.

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April 8, 2020

The 1-Wasserstein distance is a popular integral probability metric. In this post, the dual form of the 1-Wasserstein distance is derived from its primal form.

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September 28, 2018

A normalizing flow is a great tool that can transform simple probability distributions into very complex ones by applying a series of invertible functions to samples from the simple distribution. This post explores two simple flows introduced by Rezende and Mohamed (2015) –– Planar Flow and Radial Flow.

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September 21, 2018

Backpropagation through a stochastic node is an important problem in deep learning. Implicit reparameterization gradients go beyond the reparameterization trick to address the problem of efficient gradient computation in such a setting.

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