About the event
Date and Venue 23 – 25 November, School of Informatics, Edinburgh
Main organisers: Amos Storkey, Krzysztof Geras, Nando De Freitas, Ben Graham, Zoubin Ghahramani, Thore Graepel Deep Learning is the learning and use of highly flexible multilayer models for machine learning. Deep learning has proven itself, both in terms of academic research and capability, and in terms of commercial interest. The Deep Learning Scoping Workshop will work on establishing areas of joint research and furthering UK capacity and capability building in this field. The workshop will focus on four aspects of deep learning. First, we will consider the further generalisations of deep learning methods beyond classification and feature generation, to areas of generative modelling, transfer learning, multimodal integration, reinforcement learning and other important problems. Second, we will ask what other practical problems/scenarios can benefit from the application of deep learning methods beyond current domains, and how models can be distilled and made accessible for use in those domains. Third, we will propose potential approaches for reducing or distributing the computational costs associated with deep learning. Fourth, we will examine the theoretical aspects of deep learning, and ask why deep learning has proven so effective and what the limitations are. The discussion will also include:
- Facilitating trusted early communication of approaches and results to enable the UK to maintain its position in the fast and competitive research in this field
- The potential to maintain joint data repositories that will provide benefits to all parties
- The steep learning curve for doctoral students and postdocs. The working environment is quickly changing. What coordination will helps ensure researchers are quickly up to speed?
- What interaction is required with business research labs and startups to enable them to take up deep learning methods, where otherwise they would not have the opportunity
- What the best practice is, in training the next generation in deep learning