Connectionists: CFP special session on "Green Machine Learning" at ESANN 2023

[Brais Cancela Barizo]

[Apologies if you receive multiple copies of this CFP]



Call for papers:  special session on " Green Machine Learning " at ESANN 2023



European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2023) 

4-6 October 2023, Bruges (Belgium) - <http://www.esann.org/> http://www.esann.org<http://www.esann.org/>


IMPORTANT DATES:



Paper submission deadline: 2 May 2023

Notification of acceptance: 16 June 2023

ESANN conference: 4-6 October 2023




Green Machine Learning


Homepage: https://www.esann.org/special-sessions#session3



Organized by Verónica Bolón-Canedo, Laura Morán-Fernández, Brais Cancela and Amparo Alonso-Betanzos (Universidade da Coruña, Spain)

Emails: veronica.bolon at udc.es<mailto:veronica.bolon at udc.es>, laura.moranf at udc.es<mailto:laura.moranf at udc.es>, brais.cancela at udc.es<mailto:brais.cancela at udc.es> , ciamparo at udc.es<mailto:ciamparo at udc.es>



In the last years we have witnessed the most impressive advances achieved by Artificial Intelligence (AI), in most cases by using deep learning models. However, it is undeniable that deep learning has a huge carbon footprint (a paper from 2019 stated that training a language model could emit nearly five times the lifetime emissions of an average car).



The term Green AI refers to AI research that is more environmentally friendly and inclusive, not only by producing novel results without increasing the computational cost, but also by ensuring that any researcher with a laptop has the opportunity to perform high-quality research without the need to use expensive cloud servers. The typical AI research (sometimes referred as Red AI) aims to obtain state-of-the-art results at the expense of using massive computational power, usually through an enormous quantity of training data and numerous experiments. Efficient machine learning approaches (especially deep learning) are starting to receive some attention in the research community. However, the problem is that, most of the time, these works are not motivated by being green. Therefore, it is necessary to encourage the AI community to recognize the value of work by researchers who take a different path, optimizing efficiency rather than only accuracy. Topics such as low-resolution algorithms, edge computing, efficient platforms, and in general scalable and sustainable algorithms and their applications are of interest to complete a holistic view of Green AI.



In this special session, we invite papers on both practical and theoretical issues about developing new machine learning that are sustainable and green, as well as review papers with the state-of-art techniques and the open challenges encountered in this field. In particular, topics of interest include, but are not limited to:



Developing energy-efficient algorithms for training and/or inference.

Investigating sustainable data management and storage techniques.

Exploring the use of renewable energy sources for machine learning.

Examining the ethical and social implications of green machine learning.

Investigating methods for reducing the carbon footprint of machine learning systems.

Studying the impact of green machine learning on various industries and applications.



Submitted papers will be reviewed according to the ESANN reviewing process and will be evaluated on their scientific value: originality, correctness, and writing style.
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