IJCNN 2019 Accepted Competitions







Challenge UP: Multimodal Fall Detection



Hiram Ponce, Lourdes Martínez-Villaseñor, León Palafox, Karina Pérez 

Falls are frequent especially among old people and it is a major health problem according to World Health Organization. Fall detectors can alleviate this problem and can reduce the time in which a person who suffered a fall receives assistance. Recently, there has been an increase in fall detection system development based mainly in sensor and/or context approaches; however, public datasets are difficult to access. In that sense, we provide a public multimodal dataset for fall detection in the benefit of researchers in the fields of wearable computing, ambient intelligence, and vision. In the best of our knowledge, no fall detection competition has been reported, and especially using a multimodal dataset. Contact: [email protected]



Start: Decembe 03, 2018

End: April 26, 2019

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L2RPN: Learning to run a power network. 

Isabelle Guyon, Antoine Marot, Balthazar Donon, Benjamin Donnot

The objective of this challenge is to test the potential of Reinforcement Learning (RL) to solve a real world problem of great practical importance: controlling electricity trans- portation in power grids while keeping people and equipment safe. This challenge is the ”gamification” of a serious problem. We work in collaboration with the French long dis- tance high voltage electricity transmission company Rseau de Transport dlectricit (RTE, France). Contact: [email protected]



Starts: May 6, 2019

End: April 17, 2019

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AutoML Rematch

Wei-Wei Tu, Yao Quanming, Wang Mengshuo,  Hugo Jair Escalante, Isabelle Guyon. 

The goal is to develop Automatic Machine Learning methods in a lifelong setting, and where the data presents the concept drift phenomenon. This challenge is a follow up of a series of AutoML challenges collocated with PAKDD2018,  NIPS2018, and PAKDD2019. Contact: [email protected]



Start: April 7-14, 2019

End: June 30, 2019

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AIML Contest 2019 

 Juyang Weng, Juan L. Castro-Garcia, Xiang Wu.  

The Artificial Intelligence Machine Learning (AIML) Contest aims to address major learning mechanisms for general purposes. It provides an opportunity for contestants to learn about brain-inspired models and algorithms. It is the first contest series that must use a task-independent and modality-independent learning engine. Contact: [email protected]



Start: March 20, 2019

Kickoff at IJCNN: July 14, 2019

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