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

In Conjuction with IJCAI 2021

Date TBA

Location: Online

For more information, please contact

Call for Papers now available!

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), short papers (up to 4 pages), or position papers (2-3 pages). All submissions will be in IJCAI format.

Important dates

How to submit

Submission details will be posted soon.


David Aha
Naval Research Laboratory

David Crandall
Indiana University

David Leake
Indiana University

Program Committee