Edinburgh Deep Learning Workshop 2015


8 Jun 2015

 

EDINBURGH DEEP LEARNING WORKSHOP

DATE: 9th June 2015

LOCATION: Informatics Forum

 

Organisers: Amos StorkeyKrzysztof J. Geras

Please register for this event

Workshop

On the 9 June 2015 we will be running a second workshop on Deep Learning to bring together those who are working in Machine Learning, Statistics, and the wider community, who are using Deep Machine Learning Methods. We will look at different forms of deep models, and the forms of representations that are learnt, methodological extensions of these methods and the exciting developments of these methods in different areas.

The workshop follows up on the first Edinburgh Deep Learning Workshop in 2014, which had more than 150 attendees.

The workshop will be in the School of Informatics, University of Edinburgh. There will be a number of invited speakers, including Rich Caruana, Alex Graves, Phil Blunsom and Neil Lawrence, but also with room for contributions. The outline of the event is given below, but is subject to change.

  • 09.15 Introductory Talk: Amos Storkey.
  • 09.30 Invited Speaker: Neil Lawrence (University of Sheffield) on Deep Gaussian Processes.
  • 10:15 Pawel Swietojanski (Informatics, Edinburgh) Acoustic Model Adaptation.
  • 10.40 Coffee, Discussion
  • 11.15 Invited Speaker: Phil Blunsom (University of Oxford and Google Deepmind) on Deep Learning and Computational Linguistics.
  • 12:00 Iain Murray (Informatics, Edinburgh) Deep Density Estimation and Scientific Reasoning.
  • 12.25 Lunch.
  • 13:40 Invited Speaker: Rich Caruana (Microsoft Research) will be examining what we can know of Deep Learning Models.
  • 14:25 Michael Pfeiffer (Univ Zurich, ETH Zurich) Deep Spiking Networks.
  • 14:50 Coffee, Discussion Period and Posters.
  • 15:45 Invited Speaker: Alex Graves (Google Deepmind) on Neural Turing Machines.
  • 16:30 Panel Discussion.
  • 17:00 End of formal program. Evening pub trip for those who don't need to leave.
Posters:
  • Classifying Plankton Species with Deep Learning and Computer Vision. Matthew Graham, Gavin Gray, Scott Lowe, Finlay Maguire, Alina Selega and Dragos Stanciu
  • Learning Grounded Meaning Representations with Autoencoders. Carina Silberer and Mirella Lapata
  • Prosodically-enhanced Recurrent Neural Network Language Models. Siva Reddy Gangireddy, Steve Renals, Yoshihiko Nankaku and Akinobu Lee
  • Scheduled denoising autoencoders. Krzysztof Geras and Charles Sutton
  • A Comparison of Neural Network Methods for Unsupervised Representation Learning on the Zero Resource Speech Challenge. Daniel Renshaw, Herman Kamper, Aren Jansen and Sharon Goldwater
  • Bimodal Modelling of Source Code and Natural Language. Miltos Allamanis, Daniel Tarlow, Andrew D. Gordon and Yi Wei
  • Chinese Poetry Generation with Recurrent Neural Networks. Xingxing Zhang and Mirella Lapata
  • Convolution Neural Networks as controllers for autonomous driving vehicles. Elio Tuci and Aparajit Narayan
  • Deep Convolutional Networks for Playing Go. Chris Clark, Amos Storkey, Isaac Henrion