Preface: case-based reasoning and deep learning.
Martin, Kyle; Kapetanakis, Stelios; Wijekoon, Ajana; Amin, Kareem; Massie, Stewart
Doctor Stewart Massie email@example.com
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.
|Start Date||Sep 8, 2019|
|Publication Date||Mar 4, 2020|
|Publisher||CEUR Workshop Proceedings|
|Institution 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|
|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|
MARTIN 2019 Preface
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