PDF version of slides included. Slides also uploaded to https://speakerdeck.com/kastnerkyle
Video of talk here: https://www.youtube.com/watch?v=TBBtOeY2Q78
To run the code in the graphics directory, you will need Theano.
I have not run this on CPU yet, but it runs pretty quickly on GPU.
To run the code, simply go to graphics_code/vae or graphics_code/cvae
THEANO_FLAGS="floatX=float32,device=gpu,mode=FAST_RUN" python vae.py
or
THEANO_FLAGS="floatX=float32,device=gpu,mode=FAST_RUN" python cvae.py
will start training the model. After training,
THEANO_FLAGS="floatX=float32,device=gpu,mode=FAST_RUN" python flying_vae.py serialized_vae.pkl
or
THEANO_FLAGS="floatX=float32,device=gpu,mode=FAST_RUN" python flying_cvae.py serialized_cvae.pkl
will generate plots for the saved model.
This variational autoencoder follows the general procedure described in Auto-Encoding Variational Bayes, Kingma and Welling
Another paper describes a similar concept, Stochastic Backpropagation and Approximate Inference in Deep Generative Models, Rezende, Mohamed, and Wierstra.
This conditional variational autoencoder follows a similar procedure to that described in Semi-supervised Learning with Deep Generative Models, Kingma, Rezende, Mohamed, and Welling.
sklearn-theano, a scikit-learn compatible library for using pretrained networks http://sklearn-theano.github.io/
My research code https://github.com/kastnerkyle/santa_barbaria
Neural network tutorial by @NewMu / Alec Radford https://github.com/Newmu/Theano-Tutorials
Theano Deep Learning Tutorials http://deeplearning.net/tutorial/
