Dr Kyle Martin k.martin3@rgu.ac.uk
Lecturer
Preface: case-based reasoning and deep learning.
Martin, Kyle; Kapetanakis, Stelios; Wijekoon, Ajana; Amin, Kareem; Massie, Stewart
Authors
Stelios Kapetanakis
Ajana Wijekoon
Kareem Amin
Dr Stewart Massie s.massie@rgu.ac.uk
Associate Professor
Contributors
Stelios Kapetanakis
Editor
Hayley Borck
Editor
Abstract
Recent advances in deep learning (DL) have helped to usher in a new wave of confidence in the capability of artificial intelligence. Increasingly, we are seeing DL architectures out perform long established state-of-the-art algorithms in a number of diverse tasks. In fact, DL has reached a point where it currently rivals or has surpassed human performance in a number of challenges e.g. image classification, speech recognition and game play.
Citation
MARTIN, K., KAPETANAKIS, S., WIJEKOON, A., AMIN, K. and MASSIE, S. 2019. Preface: case-based reasoning and deep learning. In Kapetanakis, S. and Borck, H. (eds.) Proceedings of the 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19), 8-12 September 2019, Otzenhausen, Germany. CEUR workshop proceedings, 2567. Aachen: CEUR-WS [online], pages 6-7. Available from: http://ceur-ws.org/Vol-2567/cbr_dl_preface.pdf
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19) |
Start Date | Sep 8, 2019 |
End Date | Sep 12, 2019 |
Acceptance Date | Jul 23, 2019 |
Online Publication Date | Mar 4, 2020 |
Publication Date | Mar 4, 2020 |
Deposit Date | Apr 7, 2020 |
Publicly Available Date | Apr 7, 2020 |
Publisher | CEUR-WS |
Peer Reviewed | Peer Reviewed |
Pages | 6-7 |
Series Title | CEUR workshop proceedings |
Series Number | 2567 |
Series ISSN | 1613-0073 |
Keywords | Learning theory; Representation learning; Deep learning architectures; Hybrid systems; Deep reinforcement learning; Deep belief networks; Auto-encoders; Feed-forward neural networks; Convolutional neural networks; Recurrent neural networks; Generative adv |
Public URL | https://rgu-repository.worktribe.com/output/891532 |
Publisher URL | http://ceur-ws.org/Vol-2567/cbr_dl_preface.pdf |
Files
MARTIN 2019 Preface (VOR)
(189 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
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