$$ \newcommand{\dint}{\mathrm{d}} \newcommand{\vphi}{\boldsymbol{\phi}} \newcommand{\vpi}{\boldsymbol{\pi}} \newcommand{\vpsi}{\boldsymbol{\psi}} \newcommand{\vomg}{\boldsymbol{\omega}} \newcommand{\vsigma}{\boldsymbol{\sigma}} \newcommand{\vzeta}{\boldsymbol{\zeta}} \renewcommand{\vx}{\mathbf{x}} \renewcommand{\vy}{\mathbf{y}} \renewcommand{\vz}{\mathbf{z}} \renewcommand{\vh}{\mathbf{h}} \renewcommand{\b}{\mathbf} \renewcommand{\vec}{\mathrm{vec}} \newcommand{\vecemph}{\mathrm{vec}} \newcommand{\mvn}{\mathcal{MN}} \newcommand{\G}{\mathcal{G}} \newcommand{\M}{\mathcal{M}} \newcommand{\N}{\mathcal{N}} \newcommand{\S}{\mathcal{S}} \newcommand{\I}{\mathcal{I}} \newcommand{\diag}[1]{\mathrm{diag}(#1)} \newcommand{\diagemph}[1]{\mathrm{diag}(#1)} \newcommand{\tr}[1]{\text{tr}(#1)} \renewcommand{\C}{\mathbb{C}} \renewcommand{\R}{\mathbb{R}} \renewcommand{\E}{\mathbb{E}} \newcommand{\D}{\mathcal{D}} \newcommand{\inner}[1]{\langle #1 \rangle} \newcommand{\innerbig}[1]{\left \langle #1 \right \rangle} \newcommand{\abs}[1]{\lvert #1 \rvert} \newcommand{\norm}[1]{\lVert #1 \rVert} \newcommand{\two}{\mathrm{II}} \newcommand{\GL}{\mathrm{GL}} \newcommand{\Id}{\mathrm{Id}} \newcommand{\grad}[1]{\mathrm{grad} \, #1} \newcommand{\gradat}[2]{\mathrm{grad} \, #1 \, \vert_{#2}} \newcommand{\Hess}[1]{\mathrm{Hess} \, #1} \newcommand{\T}{\text{T}} \newcommand{\dim}[1]{\mathrm{dim} \, #1} \newcommand{\partder}[2]{\frac{\partial #1}{\partial #2}} \newcommand{\rank}[1]{\mathrm{rank} \, #1} \newcommand{\inv}1 \newcommand{\map}{\text{MAP}} \newcommand{\L}{\mathcal{L}} \DeclareMathOperator*{\argmax}{arg\,max} \DeclareMathOperator*{\argmin}{arg\,min} $$

Fisher Information Matrix

An introduction and intuition of Fisher Information Matrix.

Posted on March 11, 2018

Introduction to Annealed Importance Sampling

An introduction and implementation of Annealed Importance Sampling (AIS).

Posted on December 23, 2017

Gibbs Sampler for LDA

Implementation of Gibbs Sampler for the inference of Latent Dirichlet Allocation (LDA)

Posted on September 7, 2017

Boundary Seeking GAN

Training GAN by moving the generated samples to the decision boundary.

Posted on March 7, 2017

Least Squares GAN

2017 is the year GAN loss its logarithm. First, it was Wasserstein GAN, and now, it's LSGAN's turn.

Posted on March 2, 2017

CoGAN: Learning joint distribution with GAN

Original GAN and Conditional GAN are for learning marginal and conditional distribution of data respectively. But how can we extend them to learn joint distribution instead?

Posted on February 18, 2017

Wasserstein GAN implementation in TensorFlow and Pytorch

Wasserstein GAN comes with promise to stabilize GAN training and abolish mode collapse problem in GAN.

Posted on February 4, 2017

InfoGAN: unsupervised conditional GAN in TensorFlow and Pytorch

Adding Mutual Information regularization to a GAN turns out gives us a very nice effect: learning data representation and its properties in unsupervised manner.

Posted on January 29, 2017

Maximizing likelihood is equivalent to minimizing KL-Divergence

We will show that doing MLE is equivalent to minimizing the KL-Divergence between the estimator and the true distribution.

Posted on January 26, 2017

Variational Autoencoder (VAE) in Pytorch

With all of those bells and whistles surrounding Pytorch, let's implement Variational Autoencoder (VAE) using it.

Posted on January 24, 2017