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Symbolic Autoencoder

Oryan Barta

Supervised by Alon Zvirin, Yaron Honen


Developing effective unsupervised learning techniques is an essential stepping stone towards next generation machine learning models. Such models would no longer be bottlenecked by their dependence on massive labeled datasets which are often difficult or impossible to obtain. We propose a novel architecture for deep feature extraction from unlabeled data and intelligent labeling of data using an implicitly defined and learned symbolic language. The model can then be used in a semi-supervised context to reduce the amount of labeled data necessary for training.


Please, see project papers.

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Project Report

Please, see project report.

Final Presentation

Please, see final presentation.

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