The performance of many machine learning algorithms depends highly upon the quality and quantity of available data, and (hyper)-parameter settings. In particular, deep learning methods, including convolutional neural networks, are known to be ‘data-hungry,’ and require properly tuned hyper-parameters. Meta-Learning is a way to address both issues. Simple, but effective approaches reported recently include pre-training models on similar datasets. This way, a good model or good hyperparameters can be pre-determined or learned model parameters can be transferred to the new dataset. As such, higher performance can be achieved with the same amount of data, or similar performance with less data (few shot learning). This workshop, with a co-hosted competition, will focus on meta-learning and few shot learning.

Submission Guidelines

Papers must be formatted in AAAI two-column, camera-ready style. We welcome two types of submissions, regular papers (max. 7 pages, including references) and short papers (max. 4 pages, including references). All accepted papers will be hosted on the website of the workshop. Authors of accepted regular papers can opt-in to the formal proceedings. Submissions are due November 6, 2020.

List of Topics

We welcome all types of submissions that feature Meta-learning and few shot learning, but have a specific focus on the following topics:

• evaluation protocols and standardized benchmarks
• generalization of meta-learning techniques across diverse datasets
• papers that describe submissions to the co-hosted ChaLearn competition

Committees

Organizing committee

• Adrian El Baz (INRIA and Université Paris Saclay, France)
• Isabelle Guyon (INRIA and Université Paris Saclay, France, ChaLearn, USA)
• Zhengying Liu (INRIA and Université Paris Saclay, France)
• Jan N. van Rijn (LIACS, Leiden University, the Netherlands)
• Sebastien Treguer (INRIA and Université Paris Saclay, France, ChaLearn, USA)
• Joaquin Vanschoren (Eindhoven University of Technology, the Netherlands)

Invited Speakers

• Chelsea Finn (Stanford University, USA)
• Oriol Vinyals (Google DeepMind)
• Lilian Weng (OpenAI, USA)
• Richard Zemel (University of Toronto, Canada)

Publication

Metalearn2021 proceedings will be published in Proceedings of Machine Learning Research (PMLR).

Contact

All questions about submissions can be send to the following address: metalearningchallenge@googlegroups.com (actively maintained)