Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders | PLOS Computational Biology
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
Posit AI Blog: FNN-VAE for noisy time series forecasting
How to Build a Variational Autoencoder in Keras | Paperspace Blog
14. Variational Autoencoder — deep learning for molecules & materials
Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells | bioRxiv
Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model - ScienceDirect
Frontiers | Variational Autoencoder Modular Bayesian Networks for Simulation of Heterogeneous Clinical Study Data
MAKE | Free Full-Text | Semi-Supervised Adversarial Variational Autoencoder
Posit AI Blog: FNN-VAE for noisy time series forecasting
Variational autoencoder-based estimation of chronological age and changes in morphological features of teeth | Scientific Reports
Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation | PLOS Computational Biology
Variational Autoencoder in TensorFlow (Python Code)
Molecules | Free Full-Text | VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder
Variational autoencoder - Wikipedia
Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic | PLOS Computational Biology
How to Build a Variational Autoencoder in Keras | Paperspace Blog
Molecules | Free Full-Text | VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder
Image Compression and Generation using Variational Autoencoders in Python
VAE: Variational Autoencoder – KejiTech
Embedding of Molecular Structure Using Molecular Hypergraph Variational Autoencoder with Metric Learning - Koge - 2021 - Molecular Informatics - Wiley Online Library
Simple and Effective VAE Training with Calibrated Decoders
L4 Latent Variable Models (VAE) -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley - YouTube
Learning Efficient, Collective Monte Carlo Moves with Variational Autoencoders | Journal of Chemical Theory and Computation
7_VAE_priors
