@inproceedings{a922300e31344296a0feebbb445f30df,
title = "A Retrospective of the Tutorial on Opportunities and Challenges of Online Deep Learning",
abstract = "Machine learning algorithms have become indispensable in today{\textquoteright}s world. They support and accelerate the way we make decisions based on the data at hand. This acceleration means that data structures that were valid at one moment could no longer be valid in the future. With these changing data structures, it is necessary to adapt machine learning (ML) systems incrementally to the new data. This is done with the use of online learning or continuous ML technologies. While deep learning technologies have shown exceptional performance on predefined datasets, they have not been widely applied to online, streaming, and continuous learning. In this retrospective of our tutorial titled Opportunities and Challenges of Online Deep Learning held at ECML PKDD 2023, we provide a brief overview of the opportunities but also the potential pitfalls for the application of neural networks in online learning environments using the frameworks River and Deep-River.",
keywords = "Concept Drift, Decision Support, Deep Learning, Neural Networks, Online Learning, Stream Learning",
author = "Cedric Kulbach and Lucas Cazzonelli and Ngo, \{Hoang Anh\} and Le-Nguyen, \{Minh Huong\} and Albert Bifet",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 23rd Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023 ; Conference date: 18-09-2023 Through 22-09-2023",
year = "2025",
month = jan,
day = "1",
doi = "10.1007/978-3-031-74630-7\_25",
language = "English",
isbn = "9783031746291",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "359--372",
editor = "Rosa Meo and Fabrizio Silvestri",
booktitle = "Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers",
}