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Professor Jonathon Hare

Professor Jonathon Hare

Professor

Research interests

  • My main research interests lie in the area of representation learning. The long-term goal of my research is to innovate techniques that can allow machines to learn from and understand the information conveyed by data and use that information to fulfil the information needs of humans.  Broadly speaking this can be broken down into the following areas:
  • Novel representation: I have worked on a number of different approaches to creating novel representations from data. These include:
  • New units for representing different data types: With Yan Zhang & Adam Prügel-Bennett, I’ve worked developing differentiable neural architectures for counting and working with unordered sets.

More research

Accepting applications from PhD students.

Connect with Jonathon

Email: jsh2@ecs.soton.ac.uk

Address: B32, East Highfield Campus, University Road, SO17 1BJ (View in Google Maps)

Research

Research groups

Research interests

  • My main research interests lie in the area of representation learning. The long-term goal of my research is to innovate techniques that can allow machines to learn from and understand the information conveyed by data and use that information to fulfil the information needs of humans.  Broadly speaking this can be broken down into the following areas:
  • Novel representation: I have worked on a number of different approaches to creating novel representations from data. These include:
  • New units for representing different data types: With Yan Zhang & Adam Prügel-Bennett, I’ve worked developing differentiable neural architectures for counting and working with unordered sets.
  • Embedding and Disentanglement: I’ve worked on a number of aspects of learning joint embeddings of different modalities of data. Recently with Matthew Painter, Adam Prügel-Bennett and I have looked at how underlying latent processes might be disentangled.
  • Learning architectures under constraints: In recent work with Sulaiman Sadiq, Geoff Merrett and I have started to look at how neural architectures for representation might be themselves learned to optimise against certain hardware constraints. Also related to this theme is joint work with Enrique Marquez and Mahesan Niranjan on Cascade Learning of deep networks, which allows a network to be grown from the bottom up.

Research projects

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