Previously, we introduced Autoencoders and Hierarchical Variational Autoencoders (HVAEs). In this post, we will cover the details of Denoising Diffusion Probabilistic Models (DDPM). Diffusion Models We can treat DDPM as a restricted HVAE. Here, each only depends on . In DDPM, we do not have parameters to add noises, and it is a predefined GaussianContinue reading “Deep Learning 22: Diffusion Models (2)”

# Tag Archives: Learning Note

## Reinforcement Learning (1): Q-Learning basics

Hi! In the following posts, I will introduce Q-Learning, the first part to learn if you want to pick up reinforcement learning. But before that, let us shed light on some fundamental concepts in reinforcement learning (RL). Kindergarten Example Q-Learning works in this way: do an action and get reward and observation from the environment,Continue reading “Reinforcement Learning (1): Q-Learning basics”

## Deep Learning 07: Are you talking to a machine?

Recently working on a shared task job of image annotation. An interesting paper saw on NIPS’15 was proposed by Baidu Research. Find paper here . Official website. This post is the study notes.

## Random Forest: intro and an example

About Decision Trees * All samples will start from the root. * At each node, one feature will split the samples.

## Understanding SVM(1)

About advanced machine learning:

## Tinkerpop3 GraphComputer: VertexPrograms

GraphComputer TP3 provides OLTP and OLAP means of interacting with a graph. OLTP-based graph system provides query in real-time, with a limited set of data and respond on the order of milliseconds or seconds. (Only a part). The graph is walked by moving from vertex to another, via incident edges.

## Parallel Graph Coloring Algorithms and an Implementation of Jones-Plassmann

Graph Coloring Algorithms

## Logistic Regression: a quick introduction

Logistic Regression is very popular in Machine Learning, used to give predictions on something. (It is not the exact probabilities, but general values. )

## Parallel Gibbs Sampling and Neural Networks

Parallel in Variables (Vertexes): General huge, undirected graph: each vertex is a variable (parallel sampling on a high dimension).

## Gibbs Sampling: about Parallelization

About BN Belief Network, or directed acyclic graphical model (DAG). When BN is huge: Exact Inference(variable elimination) Stochastic Inference(MCMC)