About the Project
We want to investigate how deep neural network architectures can be optimised, by reducing the number of layers – and thus the number of parameters to be optimized – and their interconnections. In particular, we want to study neural network optimsation using graph and network theories.
A first degree (at least a 2.1) ideally in computer science, or maths, with a good fundamental knowledge of neural networks and graph theory.
English language requirement:
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.
Experience of fundamental neural networks.
• Competent in graph theory.
• Knowledge of Python and at least one neural network framework (e.g., Keras, tensorflow, pytorch)
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management
The applicants should motivate their willingness to obtain a PhD degree, attaching a research proposal (max 1 A4 page), describing their ideas and how these align with the scholarships aims and objectives
Peterson et al. “Private Federated Learning with Domain Adaptation”, NIPS 2019. https://arxiv.org/pdf/1912.06733.pdf.
Csurka. “Domain adaptation for visual applications: A comprehensive survey”, Advances in Computer Vision and Pattern Recognition 2017
Mocanu et al. “Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science”, Nature Communications 2018. https://doi.org/10.1038/s41467-018-04316-3.
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