Challenge

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 (including NVIDIA, RTE and Criteo) partners, organized these Data Challenges to promote the use of Machine Learning-based surrogate models to numerically solve physical problems, through […]

Machine Learning for Physical Simulations Read More »

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 the growing need for accessible, consumer-grade Brain Computer Interfaces (BCI) devices capable of providing reliable sleep monitoring and analysis. Electroencephalography (EEG) is a powerful, non-invasive

Sleep states Challenge Read More »

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 a university campus. The building is a so-called multi-tenant building, which means that it is used by different types of organizations. The Data Challenge addressed

Mind the Avatar’s Mind Read More »

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 in different human-vs-AI competitions includes experts for various languages (Italian, English, French), implementing clue database search, rule-based solvers, knowledge graph query, web search, candidate list

Automated Crossword Solving Read More »

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.

Brain Age Prediction Challenge Read More »

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 Mobility Data Challenge for a Greener Future on Codalab. The challenge has been developped by INRIA scientists Marc Schoenaeur and Sebastien Treguer in collaboration with

Smarter mobility data challenge Read More »

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 Isabelle Guyon (INRIA) in collaboration with TAILOR. The challenge is that of a portfolio of learning algorithms / hyperparameters: it is then possible to run

Meta Learning from Learning Curves 2 Read More »

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 meta-learning” for few-shot image classification using a novel “any-way” and “any-shot” setting. This challenge is part of TAILOR WP2 (see more information here). Goal The

Cross-Domain MetaDL: Any-Way Any-Shot Learning Competition with Novel Datasets from Pratical Domains Read More »