Variational Representation Learning

Lead Research Organisation: University of Oxford
Department Name: Computer Science


This project falls within the EPSRC Artificial Intelligence Technologies research area. Representation learning is defined as learning to map data to a different representation that has some preferential properties. These properties include a lower dimensionality, disentanglement, and meaning arithmetic. In turn these representations could enable generative modelling, and semi- supervised or unsupervised learning.

In practice, data is often presented in an inconvenient form, such as a graph, a high resolution MRI scan or a piece of text. To be able to process this data efficiently and correctly, it would be preferable to work with a different representation. This research project is aimed at converting the original data representation into a more convenient one, while preserving meaningful properties of the data. This representation may be a lower dimensional form of the same data type, or have some preferable properties such as removal of personally identifiable information.

There are currently several methods to obtain these more convenient representations, for example latent variable models [1] and metric learning [2]. Each of these methods has some downsides, such as no guarantees on the information contained in the representation or only optimizing a surrogate loss. In this project we aim to introduce novel, easy to use methods for creating representations with guarantees on information contained in the new representation. These representations can be used with confidence in other applications, such as medical imaging, language translation and compression. We plan to publish these methods for various types of data in various settings at top-tier machine learning conferences. We aim to develop algorithms and methods that improve upon the current solutions by optimizing for the true objective and allowing for quantifying the quality of the new found representation. This will be done by leveraging and developing variational algorithms that allow us to learn new types of models, probably with the use of deep neural networks. The project is tightly aligned with the research area Artificial Intelligence Technologies (AIT). Representation learning is currently in use by a wide variety of AIT, such as medical imaging, computer vision and reinforcement learning. Improvements in learning representations will therefore enable
better autonomous technologies, improve our ability to make fair and correct medical diagnoses, and enable privacy-minded applications built on top of data gathered by Internet of Things devices.

The project will be supervised by Professor Yarin Gal from the Department of Computer Science and co-supervised by Professor Yee Whye Teh from the Department of Statistics, both at the University of Oxford. Aside from the EPSRC grant, the project will be funded by the
Oxford-Google DeepMind fellowship.
[1] Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." ICLR, 2014.
[2] Hoffer, Elad, and Nir Ailon. "Deep metric learning using triplet network." International Workshop on
Similarity-Based Pattern Recognition . Springer, Cham, 2015.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509711/1 01/10/2016 30/09/2021
2056659 Studentship EP/N509711/1 01/10/2018 30/09/2022 Joost Van Amersfoort