As for prospective participants, we primarily target machine learning researchers and practitioners interested in the above questions. Specific target communities include but are not limited to compositional generalization, compositional reasoning, modular deep learning, transfer learning, continual learning, and foundation models. We also invite submissions from researchers who study neuroscience, to provide a broad perspective to the attendees. To summarize, the topics include but are not limited to:
- Empirical analysis of compositional generalization/reasoning capacity in various foundation models
- Mechanism understanding of compositional generalization/reasoning in foundation models
- Reliable and model-agnostic compositional generalization methods
- Modular and dynamic architectures
- Theoretical foundations and empirical findings of connections between modular structures and compositional generalization
- Continual/transfer learning through compositionality
- Compositional learning for various application domains, such as computer vision, natural language processing, reinforcement learning, and science.
Submission URL: https://openreview.net/group?id=NeurIPS.cc/2024/Workshop/Compositional_Learning
Format: All submissions must be in PDF format and anonymized. Submissions are limited to four content pages, including all figures and tables; unlimited additional pages containing references and supplementary materials are allowed. Reviewers may choose to read the supplementary materials but will not be required to. Camera-ready versions may go up to five content pages.
Style file: You must format your submission using the NeurIPS 2024 LaTeX style file. For your convenience, we have modified the main conference style file to refer to the compositional learning workshop: compositional_learning.sty. The maximum file size for submissions is 50MB. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review.
Dual-submission policy: We welcome ongoing and unpublished work. We will also accept papers that are under review at the time of submission, or that have been recently accepted without published proceedings.
Non-archival: The workshop is a non-archival venue and will not have official proceedings. Workshop submissions can be subsequently or concurrently submitted to other venues.
Visibility: Submissions and reviews will not be public. Only accepted papers will be made public.
Contact: For any questions, please contact us at compositional-learning-neurips2024@googlegroups.com.
If you would like to become a reviewer for this workshop, please let us know at https://forms.gle/nYwXVRhL6QK8eR2y6.