Preface: case-based reasoning and deep learning.

Martin, Kyle; Kapetanakis, Stelios; Wijekoon, Ajana; Amin, Kareem; Massie, Stewart

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 adversarial networks; Transfer learning and domain adaptation; Similarity/metric learning models
• Public URL: https://rgu-repository.worktribe.com/output/891532
• Publisher URL: http://ceur-ws.org/Vol-2567/cbr_dl_preface.pdf