A variational autoencoder is a type of neural network that learns to compress (encode) data, in such a way that one can later randomly sample data.

Here we show training of a variational autoencoder on a data set of wave packets that have been placed with a random location and amplitude. Upper left: test set, with VAE decoder results shown in orange; upper right: latent space distribution, with color coding for actual location (left) or amplitude (right) of any data point; lower right: latent space distributions for the few samples from the test set (in each case, the Gaussian spread in latent space is also indicated); lower left: loss evolution.

2021 by Florian Marquardt. For the full explanation, watch [ Ссылка ] .

This animation is part of the online lecture series "Advanced Machine Learning for Physics, Science, and Artificial Scientific Discovery". See the website [ Ссылка ]# and the channel with the full lecture videos: [ Ссылка ].

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