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|title=None
|pdfUrl=https://ceur-ws.org/Vol-2567/cbr_dl_preface.pdf
|volume=Vol-2567
|dblpUrl=https://dblp.org/rec/conf/iccbr/MartinKWAM19
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2567/cbr_dl_preface.pdf</pdf>
<pre>
                Case-Based Reasoning and Deep Learning
Kyle Martin1, Stelios Kapetanakis2, Ajana Wijekoon1, Kareem Amin3, Stewart Massie1

                          1
                          Robert Gordon University, Aberdeen, UK
                           2
                             University of Brighton, Brighton, UK
         3
           German Research Centre for Artificial Intelligence, Kaiserslautern, Germany



Preface
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 outperform
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.

These successes of DL call for novel methods and techniques that exploit DL for the benefit
of CBR systems. In particular, the potential of DL for CBR include improvement in knowledge
aggregation and feature extraction for case representation, efficient indexing and retrieval
architectures as well as assisting with case adaptation.

The goals of this workshop are to provide a forum to identify opportunities and challenges for
the use of deep learning techniques and architectures in the context of case-based reasoning
systems. Particular interests this workshop will explore include:
    • How DL can be used to improve knowledge aggregation strategies for case
       representation
    • The role of DL in making similarity computations easier and more efficient
    • Application of DL to help with solution adaptation
    • How DL architectures can be used to inspire more efficient indexing and retrieval
       architectures

This year the workshop has attracted researchers from a number of related areas including
Case-based Reasoning, Deep Learning and Machine Learning. This diversity allowed us to
address the challenges in the field and identify where our efforts, as a research community,
should focus.

Workshop Topics focus on:

     •   Learning Theory                              •   Feed-Forward Neural Networks
     •   Representation Learning                      •   Convolutional Neural Networks
     •   Deep Learning Architectures                  •   Recurrent Neural Networks
     •   Hybrid Systems                               •   Generative Adversarial Networks
     •   Deep Reinforcement Learning                  •   Transfer Learning and Domain
     •   Deep Belief Networks                             Adaptation
     •   Auto-encoders                                •   Similarity/Metric Learning Models
Program Chairs
Kyle Martin           Robert Gordon University, Aberdeen, UK
Stelios Kapetanakis   University of Brighton, Brighton, UK
Ajana Wijekoon        Robert Gordon University, Aberdeen, UK
Kareem Amin           German Research Centre for Artificial Intelligence, Kaiserslautern,
                      Germany
Stewart Massie        Robert Gordon University, Aberdeen, UK



Programme Committee
Juan Recio Garcia     University Complutense of Madrid, Spain
Jixin Ma              University of Greenwich, UK
Miltos Petridis       University of Middlesex UK
Nik Papadakis         Technological Educational Institute of Crete, GR
Nikolaos Polatidis    University of Brighton, UK
George Samakovitis    University of Greenwich, UK

</pre>