This is a PyTorch implementation of the vector quantized variational autoencoder (https://arxiv.org/abs/1711.00937). You can find the author's original implementation ...

Abstract: The variational autoencoder (VAE) has proven highly effective in monitoring nonlinear stochastic processes, primarily under the assumption of complete and temporally independent data.

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...

├── autoencoder.py # Main VQ-VAE model implementation ├── config.yaml # Configuration file for model and training ├── dataset.py # Dataset loader for image data ├── ema.py # Exponential Moving Average ...