List: Autoencoder / VAE | Curated by Simon Kwan

Jun 12, 2023

32 stories

Autoencoder / VAE
In
TDS Archive
by
Nick Hespe
Building Autoencoders on Sparse, One Hot Encoded DataA hands-on review of loss functions suitable for embedding sparse one-hot-encoded data in PyTorch
Sep 28, 2020
1
Sep 28, 2020
1
In
TDS Archive
by
Chitta Ranjan
Step-by-step understanding LSTM Autoencoder layersHere we will break down an LSTM autoencoder network to understand them layer-by-layer. We will go over the input and output flow between…
Jun 4, 2019
22
Jun 4, 2019
22
In
TDS Archive
by
Brent Larzalere
LSTM Autoencoder for Anomaly DetectionCreate an AI deep learning anomaly detection model using Python, Keras and TensorFlow
Sep 25, 2019
14
Sep 25, 2019
14
Shashank Yadav
Understanding Vector Quantized Variational Autoencoders (VQ-VAE)From my most recent escapade into the deep learning literature I present to you this paper by Oord et. al.  which presents the idea of…
Sep 1, 2019
11
Sep 1, 2019
11
Sebastian Orbell
Discrete Latent spaces in deep generative modelsMany recent advances in the accomplishments of deep generative models have stemmed from a simple yet powerful concept. The discretisation…
Oct 24, 2021
1
Oct 24, 2021
1
In
YOCTOL.AI
by
沈家豪
優拓 Paper Note ep.16: Neural Discrete Representation Learning註：以下的圖皆截自這篇論文 《Neural Discrete Representation Learning》，論文中的範例請參考此連結
Dec 15, 2017
1
Dec 15, 2017
1
Mackerel Chang
[筆記][NeurIPS2017] VQ-VAE: Neural Discrete Representation LearningLatent space 離散化
Jun 5, 2020
Jun 5, 2020
In
Analytics Vidhya
by
Sieun Park
An overview on VQ-VAE: Learning Discrete Representation SpaceKey Concepts of learning discrete representation space in VQ-VAE
Apr 3, 2021
Apr 3, 2021
Unajacimovic
Autoencoders… a scary name?When starting learning machine learning on my own, I was aware of the autoencoders and their existence. But from its name, I thought that…
Mar 22, 2022
Mar 22, 2022
In
TDS Archive
by
Joey Mach
Unlocking Drug Discovery through Machine LearningAccelerate drug discovery by leveraging machine learning to generate and create retro-synthesis pathways for molecules.
Nov 23, 2019
Nov 23, 2019
In
TDS Archive
by
Max Frenzel, PhD
The Variational Autoencoder as a Two-Player Game — Part IIVariational Return to the Autoencoding Olympics
Apr 9, 2018
1
Apr 9, 2018
1
In
TDS Archive
by
Max Frenzel, PhD
The Variational Autoencoder as a Two-Player Game — Part IAlice and Bob at the Autoencoding Olympics
Apr 2, 2018
2
Apr 2, 2018
2
In
TDS Archive
by
Joseph Rocca
Understanding Variational Autoencoders (VAEs)Building, step by step, the reasoning that leads to VAEs.
Sep 24, 2019
115
Sep 24, 2019
115
In
TDS Archive
by
Luigi Bungaro
How to generate new data in Machine Learning with VAE (Variational Autoencoder) applied to Mnist…link to github
Oct 25, 2018
Oct 25, 2018
In
TDS Archive
by
Max Frenzel, PhD
The Variational Autoencoder as a Two-Player Game — Part IIIThe Difficulties of Encoding Text
Apr 16, 2018
1
Apr 16, 2018
1
In
TDS Archive
by
Matthew Stewart, PhD
Comprehensive Introduction to Autoencoders
Apr 14, 2019
6
Apr 14, 2019
6
In
TDS Archive
by
Chris Kuo/Dr. Dataman
Handbook of Anomaly Detection with Python Outlier Detection — (12) AutoencodersRevision: Oct. 1, 2021
Oct 26, 2019
10
Oct 26, 2019
10
In
TDS Archive
by
Dr. Robert Kübler
Building a simple Auto Encoder via Decision TreesHow to build an Auto Encoder using random Decision Trees: eForest
Jan 3, 2020
3
Jan 3, 2020
3
Octavio Gonzalez-Lugo
Autoencoder architecture optimization by genetic programming.One of the main challenges in the development of neural networks is to determine the architecture. That means how the different layers are…
Jan 29, 2020
Jan 29, 2020
In
TDS Archive
by
Irhum Shafkat
Intuitively Understanding Variational AutoencodersAnd why they’re so useful in creating your own generative text, art and even music
Feb 4, 2018
56
Feb 4, 2018
56

Autoencoder / VAE

Building Autoencoders on Sparse, One Hot Encoded Data

A hands-on review of loss functions suitable for embedding sparse one-hot-encoded data in PyTorch

Step-by-step understanding LSTM Autoencoder layers

Here we will break down an LSTM autoencoder network to understand them layer-by-layer. We will go over the input and output flow between…

LSTM Autoencoder for Anomaly Detection

Create an AI deep learning anomaly detection model using Python, Keras and TensorFlow

Understanding Vector Quantized Variational Autoencoders (VQ-VAE)

From my most recent escapade into the deep learning literature I present to you this paper by Oord et. al. which presents the idea of…

Discrete Latent spaces in deep generative models

Many recent advances in the accomplishments of deep generative models have stemmed from a simple yet powerful concept. The discretisation…

優拓 Paper Note ep.16: Neural Discrete Representation Learning

註：以下的圖皆截自這篇論文 《Neural Discrete Representation Learning》，論文中的範例請參考此連結

[筆記][NeurIPS2017] VQ-VAE: Neural Discrete Representation Learning

Latent space 離散化

An overview on VQ-VAE: Learning Discrete Representation Space

Key Concepts of learning discrete representation space in VQ-VAE

Autoencoders… a scary name?

When starting learning machine learning on my own, I was aware of the autoencoders and their existence. But from its name, I thought that…

Unlocking Drug Discovery through Machine Learning

Accelerate drug discovery by leveraging machine learning to generate and create retro-synthesis pathways for molecules.

The Variational Autoencoder as a Two-Player Game — Part II

Variational Return to the Autoencoding Olympics

The Variational Autoencoder as a Two-Player Game — Part I

Alice and Bob at the Autoencoding Olympics

Understanding Variational Autoencoders (VAEs)

Building, step by step, the reasoning that leads to VAEs.

How to generate new data in Machine Learning with VAE (Variational Autoencoder) applied to Mnist…

link to github

The Variational Autoencoder as a Two-Player Game — Part III

The Difficulties of Encoding Text

Comprehensive Introduction to Autoencoders

Handbook of Anomaly Detection with Python Outlier Detection — (12) Autoencoders

Revision: Oct. 1, 2021

Building a simple Auto Encoder via Decision Trees

How to build an Auto Encoder using random Decision Trees: eForest

Autoencoder architecture optimization by genetic programming.

One of the main challenges in the development of neural networks is to determine the architecture. That means how the different layers are…

Intuitively Understanding Variational Autoencoders

And why they’re so useful in creating your own generative text, art and even music

Simon Kwan

註：以下的圖皆截自這篇論文《Neural Discrete Representation Learning》，論文中的範例請參考此連結