Deep Learning, Case-Based Reasoning, and AutoML:
Present and Future Synergies

In Conjuction with IJCAI 2021

Date TBA

Location: Online
https://ijcai-21.org

For more information, please contact deepcbr-2021@googlegroups.com

Call for Papers now available!
Abstract submission deadline May 7, 2021.
Paper submission deadline (long, short, and position papers) May 11, 2021 (Extended)

Workshop overview

Deep learning (DL) research has made dramatic progress in recent years, achieving high performance on supervised learning tasks for numerous problem domains. Simultaneously, there remain well-known challenges such as the need for large amounts of labeled training data, solving synthesis problems with structured solutions (e.g., designs, plans, or schedules), and explainability. Case-based reasoning is a knowledge-based methodology for reasoning from prior episodes, with complementary capabilities--such as to solve problems with small data sets or those requiring structured solutions, and to generate concrete explanations--and limitations. AutoML concerns processes for automatically generating end-to-end-machine learning (e.g., DL) pipelines, and could use techniques that build pipelines from prior cases (of successful pipeline components). This workshop will bring together researchers interested in DL, CBR, and AutoML to identify new opportunities and beneficial strategies for integrating these approaches to address current challenges.

Our goal for this workshop is to bring together members of the DL, CBR, and AutoML communities to identify new opportunities for leveraging the case-based reasoning methodology to advance deep learning and DL to advance CBR, to identify opportunities and challenges for leveraging CBR for AutoML, to examine related efforts from all three subareas, and to develop approaches for advancing such integrations. The workshop will include a substantial discussion component.

Potential subtopics include, but are not limited to:

For further discussion of some of these topics, see On Bringing Case-Based Reasoning Methodology to Deep Learning.


Call for Papers and Extended Abstracts

Submissions may be long papers (up to 6 pages body + up to 2 pages of references), short papers (up to 4 pages including references), or position papers (2-3 pages including references). All submissions will be in IJCAI format. Please download the IJCAI 2021 formatting guidelines here.

Important dates

How to submit

Submit at: https://cmt3.research.microsoft.com/DLCBR2021/Submission/Index

Organizers

David Aha
Naval Research Laboratory

David Crandall
Indiana University

David Leake
Indiana University


Program Committee

Klaus-Dieter Althoff
University of Hildesheim

Kerstin Bach
Norwegian University of Science and Techology

Chaofan Chen
University of Maine

Ralph Bergmann
University of Trier

Stelios Kapetanakis
University of Brighton

Mark Keane
University College Dublin

Kyle Martin
Robert Gordon University

Daniel Lopez-Sanchez
University of Salamanca

Sadiq Sani
British Telecommunications PLC

Swaroop Vattam
MIT Lincoln Laboratory

Rosina Weber
Drexel University

Nirmalie Wiratunga
Robert Gordon University