Challenges
Contact: Marc.Schoenauer@inria.fr or Sebastien.Treguer@inria.fr
Our challenges
- Machine Learning for Physical Simulations
IRT-SystemX is a public institute for industrial maturation and transfer, with a long collaboration history with TAILOR partner #3 Inria. IRT-SystemX, together with several academic (including Inria TAU) and industrial… Read more: Machine Learning for Physical Simulations - Sleep states Challenge
In this Data Challenge, participants are tasked with developing machine learning models to accurately predict sleep states using Electroencephalography (EEG) data collected from IDUN Guardian Earbuds. This Data Challenge addresses… Read more: Sleep states Challenge - Machine Learning for Physical Simulation Challenge
TAILOR is participating in the organisation of a new Challenge! The Challenge is organized by IRT SystemX and the RTE company, with Marc Schoenauer and Sébastien Treguer from Inria contributing… Read more: Machine Learning for Physical Simulation Challenge - Mind the Avatar’s Mind
This Data Challenge asks data science problems in the context of urban energy sustainability. More precisely, the focus of the challenge is on sensor data from a smart-building located on… Read more: Mind the Avatar’s Mind - Automated Crossword Solving
This Data Challenge consists of developing new modules designed to improve the crossword solving performances of the Webcrow 2.0 agent. The set of modules used to evaluate the base system… Read more: Automated Crossword Solving - Brain Age Prediction Challenge
The Brain Age Prediction Challenge, available on the Codalab platform, was launched as part of the NeurotechX Hackathon. In this challenge, participants are invited to use AI to predict the age of an individual from an electroencephalogram (EEG) recording time series. Such age predictions can be an important path to the development of computational psychiatry diagnosis methods. The brain age prediction challenge is running from November 4-22. - Smarter mobility data challenge
As data is at the heart of the industry 4.0, 11 large international groups and the TAILOR network challenge european students from Oct 3 to Dec 3, with the Smarter… Read more: Smarter mobility data challenge - Meta Learning from Learning Curves 2
The Meta Learning from Learning Curves challenge is an academic challenge in the 2022 part of the MetaLeran Series of data challenges run by Chalean. a non-for-profit organization lead by… Read more: Meta Learning from Learning Curves 2 - 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… Read more: Cross-Domain MetaDL: Any-Way Any-Shot Learning Competition with Novel Datasets from Pratical Domains - 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