Multi-task Learning and Applications
Primary supervisor
Contact admissions office
Other projects with the same supervisor
- Automatic Emotion Detection, Analysis and Recognition
- Biologically-Plausible Continual Learning
- Zero-Shot Learning and Applications
- Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
- Explainable and Interpretable Machine Learning
- Machine Learning and Cognitive Modelling Applied to Video Games
- Automatic Activity Analysis, Detection and Recognition
- Music Generation and Information Processing via Deep Learning
- Deep Learning for Temporal Information Processing
- Contextualised Multimedia Information Retrieval via Representation Learning
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
- Candidates must hold a minimum of an upper Second Class UK Honours degree or international equivalent in a relevant science or engineering discipline.
- Candidates must meet the School's minimum English Language requirement.
- Candidates will be expected to comply with the University's policies and practices of equality, diversity and inclusion.
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 have good time management.
- You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
General
Applicants will be required to address the following.
- Comment on your transcript/predicted degree marks, outlining both strong and weak points.
- 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?
- Why do you believe you are suitable for doing Postgraduate Research?