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Multi-task Learning and Applications - developing novel learning systems and learning algorithms for multi-task learning

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

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 notorious in the use of deep learning for multi-task learning. 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.

Funding Notes

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.

How good is research at University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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