- Cross-Domain MetaDL: Any-Way Any-Shot Learning Competition with Novel Datasets from Pratical Domains￼Meta-learning aims to leverage the experience from previous tasks to solve new tasks using only little training data, train faster and/or get better performance. The proposed challenge focuses on “cross-domain …
- Learning to Run a Power Network Challenge (L2RPN)The “Learning to run a power network challenge 2022” is concerned with AI for smart grids, and it has been built by RTE, the French Power Grid operator, and the TAU team, in collaboration with EPRI, CHA Learn, Google research, UCL and IQT labs.
During the course of the project, challenges (competitions, benchmarks, hackathons) will be proposed by TAILOR members. At least one academic challenge and one challenge related to an industrial use case should be run each year during TAILOR existence.
Before volunteering to organize a competition, you need to know what you will need to provide and to do by reading
* TAILOR Challenge guidelines (deliverable 2.2), that describes how to prepare your datasets, the metric to rank the contributions, the different phases of a competition, etc
* Codalab HowTo page, providing practical instructions as well as templates for starting your own competition