Dr Kyle Martin k.martin3@rgu.ac.uk
Lecturer
Dr Kyle Martin k.martin3@rgu.ac.uk
Lecturer
Stelios Kapetanakis
Dr Anjana Wijekoon a.wijekoon1@rgu.ac.uk
Research Fellow B
Kareem Amin
Dr Stewart Massie s.massie@rgu.ac.uk
Associate Professor
Stelios Kapetanakis
Editor
Hayley Borck
Editor
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.
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
Conference Name | 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19) |
---|---|
Conference Location | Otzenhausen, Germany |
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 Workshop Proceedings |
Volume | 2567 |
Pages | 6-7 |
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 |
MARTIN 2019 Preface
(188 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
CBR driven interactive explainable AI.
(2023)
Conference Proceeding
AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics.
(2023)
Conference Proceeding
The current and future role of visual question answering in eXplainable artificial intelligence.
(2023)
Conference Proceeding
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Advanced Search