Multi-task Learning and Applications

Primary supervisor

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Other projects with the same supervisor

Funding

  • Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

In traditional machine learning, a learning system can be trained to deal with a specific single task, while human is able to complete multiple tasks with the same learning strategy. To overcome the limitations in traditional machine learning, multi-task learning techniques are demanded by for artificial general intelligence (AGI). For AGI, a learning system works for various tasks by sharing relevant knowledge between tasks so that learning a new task is done more efficiently and the learning system can generalise better on multiple tasks.

This project is going to develop novel learning systems and learning algorithms for multi-task learning in terms of different learning paradigms ranging from supervised, unsupervised to reinforcement learning and their applications to real world problems. The main research theme is how to share the generic knowledge and the representations applicable to different tasks without scarifying the previous learning outcome for a specific task. In a lifelong learning setting that a new task is learnt by a system already trained on other tasks, harmless knowledge transfer is also an unsolved issue and hard to carry out in the use of deep learning for multi-task learning due to catastrophic interference. Furthermore, this project also needs to address common issues in machine learning such as domain shift. Regarding applications, multi-model information processing, robotics and general video game playing are among the proper test beds for different learning paradigms. It is worth mentioning that this project description is generic and a specific yet well-defined project needs to be developed based on a self-motivated student's own input.

In order to take this project, it is essential to have excellent mathematics and machine learning background knowledge as well as good programming skills. If you are interested in this project, please first visit my research student page: http://staff.cs.manchester.ac.uk/~kechen/ for the required materials and information prior to contacting me.

Person specification

For information

Essential

Applicants will be required to evidence the following skills and qualifications.

  • This project requires mathematical engagement and ability substantially greater than for a typical Computer Science PhD. Give evidence for appropriate competence, as relevant to the project description.
  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.

Desirable

Applicants will be required to evidence the following skills and qualifications.

  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
  • You will have good time management.

General

Applicants will be required to address the following.

  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Why do you believe you are suitable for doing Postgraduate Research?
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