Ph.D. position in deep learning, deadline: Friday 17th March, 2017, School of Computer Science, University of Manchester, U. K.
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Tingting Mu
Feb 9, 2017, 7:22:48 AM2/9/17
to Machine Learning News
This project is eligible for the James Elson Studentship of School of Computer Science at the University of Manchester, U. K., for September 2017 entry, for students from the UK and EU who are eligible to pay 'Home' fees.
Recent advances in deep neural networks have enabled the successful training of computers to solve supervised tasks, such as speech recognition, intelligent game playing, and image scene/object classification. How to enable A.I. to perform at levels of competence more similar to humans on creative tasks, such as image and text generation, is still a very open question. This has been of great interest to both computer scientists, and A.I./machine learning companies, but so far, although there has been reasonably good progress in language generation (e.g., machine translation, image caption generation), image generation has been of very limited success.
This adventurous Ph.D. project lies in the intersection of the computer vision and natural language processing fields, and targets the advancement of A.I. on the creative tasks of writing and drawing. One objective is to deepen our understanding on how neural networks can perceive and encode image/text information. The principal outcome will be an A.I. system, which, given a topic described in the format of either an image or text, will be capable of producing a story or generating a drawing of a picture, that are semantically relevant. The comprising neural networks will need to encode knowledge jointly learned from subsets of existing text (e.g., literature) and from existing artworks (e.g., sketches), and generate the aforementioned creative output (the new text and image) based on this encoded knowledge. Building upon current deep learning designs, this project will explore new neural network architectures, training objective functions and optimization algorithms that are suitable for solving the intended learning tasks.
Candidates are expected to have an excellent undergraduate degree in Computer Science or Mathematics or related discipline, and preferably, a relevant M.Sc. degree. Very good experience with computer programming of mathematical models and algorithms (in Matlab, R, or Python) is necessary; also, strong report writing and presentation skills, as well as communication skills and ability to work well with fellow students and colleagues. This is a novel project for dedicated students, with strong personal drive towards scientific innovation who have interest in the fields of machine learning, computer vision and natural language processing.
The deadline for applications for this studentship is Friday 17th March.
Qualified applicants are strongly encouraged to informally contact the supervising academic Dr. Tingting Mu (Email: tingt...@manchester.ac.uk, Homepage: http://personalpages.manchester.ac.uk/staff/tingting.mu) to discuss the application.
Further information about the James Elson studentship can be found at:
http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/funding/james-elson/.
Recent advances in deep neural networks have enabled the successful training of computers to solve supervised tasks, such as speech recognition, intelligent game playing, and image scene/object classification. How to enable A.I. to perform at levels of competence more similar to humans on creative tasks, such as image and text generation, is still a very open question. This has been of great interest to both computer scientists, and A.I./machine learning companies, but so far, although there has been reasonably good progress in language generation (e.g., machine translation, image caption generation), image generation has been of very limited success.
This adventurous Ph.D. project lies in the intersection of the computer vision and natural language processing fields, and targets the advancement of A.I. on the creative tasks of writing and drawing. One objective is to deepen our understanding on how neural networks can perceive and encode image/text information. The principal outcome will be an A.I. system, which, given a topic described in the format of either an image or text, will be capable of producing a story or generating a drawing of a picture, that are semantically relevant. The comprising neural networks will need to encode knowledge jointly learned from subsets of existing text (e.g., literature) and from existing artworks (e.g., sketches), and generate the aforementioned creative output (the new text and image) based on this encoded knowledge. Building upon current deep learning designs, this project will explore new neural network architectures, training objective functions and optimization algorithms that are suitable for solving the intended learning tasks.
Candidates are expected to have an excellent undergraduate degree in Computer Science or Mathematics or related discipline, and preferably, a relevant M.Sc. degree. Very good experience with computer programming of mathematical models and algorithms (in Matlab, R, or Python) is necessary; also, strong report writing and presentation skills, as well as communication skills and ability to work well with fellow students and colleagues. This is a novel project for dedicated students, with strong personal drive towards scientific innovation who have interest in the fields of machine learning, computer vision and natural language processing.
The deadline for applications for this studentship is Friday 17th March.
Qualified applicants are strongly encouraged to informally contact the supervising academic Dr. Tingting Mu (Email: tingt...@manchester.ac.uk, Homepage: http://personalpages.manchester.ac.uk/staff/tingting.mu) to discuss the application.
Further information about the James Elson studentship can be found at:
http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/funding/james-elson/.
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