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Everything about Variational autoencoder
Deep learning generative model to encode data representation
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling in 2013. It is part of the families of probabilistic graphical models and variational Bayesian methods. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Variational autoencoderA 5-minute orientation
- Skim a recent paper: A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial networkInfluential work since 2022
- Pick up a classic book: Advanced Deep Learning with KerasThe most-reprinted book on the subject
- Read the landmark paper: An Introduction to Variational AutoencodersCited 2,436 times
- Go sideways: AutoencoderA closely related topic
- Go sideways: Reparameterization trickA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
An Introduction to Variational Autoencoders
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder
Crash data augmentation using variational autoencoder
Quantum variational autoencoder
Deep Clustering With Variational Autoencoder
Variational Autoencoder for Semi-Supervised Text Classification
Dirichlet Variational Autoencoder
Explore Protein Conformational Space With Variational Autoencoder
🆕 Influential since 2022
A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network
β-Variational autoencoders and transformers for reduced-order modelling of fluid flows
Adaptive variational autoencoding generative adversarial networks for rolling bearing fault diagnosis
Dynamical Variational Autoencoders: A Comprehensive Review
Joint Coding-Modulation for Digital Semantic Communications via Variational Autoencoder
📚 Books worth reading
- 📕Advanced Deep Learning with KerasRowel Atienza · 2018
- 📕Quantum Generative Adversarial NetworksPethuru Raj, Amir Masoud Rahmani · 2025
Intelligent Science and Intelligent Data EngineeringJian Yang · 2013- 📕Dynamical Variational AutoencodersLaurent Girin, Simon Leglaive · 2021
- 📕Introduction to Variational AutoencodersDiederik P. Kingma, Max Welling · 2019
- 📕An Introduction to Variational AutoencodersDiederik P. Kingma, Max Welling · 2019
- 📕Generative AI for ProgrammersJonathan Dinu · 2024
- 📕Anomaly Detection Using a Variational Autoencoder Neural Network with a Novel Objective Function and Gaussian Mixture Model Selection TechniqueNaval Postgraduate Naval Postgraduate School, Brandon Bowman · 2019
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Autoencoder An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder learns…
- Reparameterization trick The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational…
- Continuous Bernoulli distribution In probability theory, statistics, and machine learning, the continuous Bernoulli distribution is a family of continuous probability distributions parameterized…
- Diffusion model In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable…
- Generative adversarial network A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept was…
- Bayesian optimization Bayesian optimization is a sequential model-based strategy for global optimization of black-box objective functions whose evaluations are costly. It is…
- Latent space A latent space, also known as a latent feature space or embedding space, is an embedding of a set of items within a manifold in which similar items are…
- Latent diffusion model The latent diffusion model (LDM) is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) group at LMU Munich. Introduced…