# Posts by Tags

## 2D-3D Alignment

## 3D Convolutional Neural Network

## 3D Features

## 3D Reconstruction

## 3D semantic segmentation

## 3D vision

## 4D convolutional neural network

## Classification

## Regression vs. Classification: Distance and Divergence

In Machine Learning, supervised problems can be categorized into regression or classification problems. The categorization is quite intuitive as the name ind...

## Data Processing Inequality

## Data Processing Inequality and Unsurprising Implications

We have heard enough about the great success of neural networks and how they are used in real problems. Today, I want to talk about how it was so successful ...

## Dataset

## Dirichlet Process

## Expectation Maximization

## Expectation Maximization and Variational Inference (Part 2)

In the previous post, we covered variational inference and how to derive update equations. In this post, we will go over a simple Gaussian Mixture Model with...

## Expectation Maximization and Variational Inference (Part 1)

Statistical inference involves finding the right model and parameters that represent the distribution of observations well. Let $\mathbf{x}$ be the observati...

## Extension

## Pytorch Extension with a Makefile

Pytorch is a great neural network library that has both flexibility and power. Personally, I think it is the best neural network library for prototyping (adv...

## FFT

## Free Form Deformation

## Gaussian Process

## Learning Gaussian Process Covariances

A Gaussian process is a non-parametric model which can represent a complex function using a growing set of data. Unlike a neural network, which can also lear...

## Gaussian Process Regression

## Gentle Introduction to Gaussian Process Regression

Parametric Regression uses a predefined function form to fit the data best (i.e, we make an assumption about the distribution of data by implicitly modeling ...

## Generative Adversarial Network

## Google Protocol Buffer

## Reading protobuf DB in Python

Reading protobuf DB in python

## Information Theory

## Data Processing Inequality and Unsurprising Implications

We have heard enough about the great success of neural networks and how they are used in real problems. Today, I want to talk about how it was so successful ...

## Inverse Reinforcement Learning

## Loss

## Regression vs. Classification: Distance and Divergence

In Machine Learning, supervised problems can be categorized into regression or classification problems. The categorization is quite intuitive as the name ind...

## Matrix Calculus

## Short Note on Matrix Differentials and Backpropagation

Mathematical notation is the convention that we all use to denote a concept in a concise mathematical formulation, yet sometimes there is more than one way t...

## NZ-WHO

## Neural Networks

## Data Processing Inequality and Unsurprising Implications

We have heard enough about the great success of neural networks and how they are used in real problems. Today, I want to talk about how it was so successful ...

## Nonparametric Bayesian

## Pytorch

## Pytorch Extension with a Makefile

Pytorch is a great neural network library that has both flexibility and power. Personally, I think it is the best neural network library for prototyping (adv...

## Regression

## Regression vs. Classification: Distance and Divergence

In Machine Learning, supervised problems can be categorized into regression or classification problems. The categorization is quite intuitive as the name ind...

## Representation Learning

## Variational Inference

## Expectation Maximization and Variational Inference (Part 2)

In the previous post, we covered variational inference and how to derive update equations. In this post, we will go over a simple Gaussian Mixture Model with...

## Expectation Maximization and Variational Inference (Part 1)

Statistical inference involves finding the right model and parameters that represent the distribution of observations well. Let $\mathbf{x}$ be the observati...

## Wasserstein distance

## Regression vs. Classification: Distance and Divergence

## back propagation

## Computing Neural Network Gradients

Gradient propagation is the crucial method for training a neural network

## barycentric coordinate

## Barycentric Coordinate for Surface Sampling

Fast way to sample points on a triangular mesh

## caffe

## Making a Caffe Layer

Caffe is one of the most popular open-source neural network frameworks. It is modular, clean, and fast. Extending it is tricky but not as difficult as extend...

## correspondence

## f-divergence

## Regression vs. Classification: Distance and Divergence

## fully convolutional neural network

## matrix norm

## mesh

## Barycentric Coordinate for Surface Sampling

Fast way to sample points on a triangular mesh

## multi view

## neural network

## Computing Neural Network Gradients

Gradient propagation is the crucial method for training a neural network

## point cloud

## Barycentric Coordinate for Surface Sampling

Fast way to sample points on a triangular mesh

## prediction

## python

## Reading protobuf DB in Python

Reading protobuf DB in python