Autoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software. We hypothesize that encoder-decoder ...

Jin, W., Barzilay, R. and Jaakkola, T. (2018) Junction Tree Variational Autoencoder for Molecular Graph Generation. International Conference on Machine Learning, Stockholm, 10-15 July 2018, 2323-2332.

This repository includes two Jupyter notebooks to assist with data preparation, model training, and performance evaluation: AE.ipynb: This notebook provides a step-by-step guide for preparing the ...

In this paper, we proposed a neural network (variational autoencoder) architecture that is used to generate an ECG corresponding to a single cardiac cycle. Our method generates synthetic ECGs using ...