A Retrospective of the Tutorial on Opportunities and Challenges of Online Deep Learning
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Abstract (arXiv)
Machine learning algorithms have become indispensable in today'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.
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11 of 47 references.
References · 11
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2020River: machine learning for streaming data in Pythoncited by 98
- 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
- 2018Scikit-Multiflow: A Multi-output Streaming Frameworkcited by 70
- 2019FAHT: An Adaptive Fairness-aware Decision Tree Classifiercited by 63
- 2020Challenges in Benchmarking Stream Learning Algorithms with Real-world Datacited by 59
- 2018FNNC: Achieving Fairness through Neural Networkscited by 43
- 2019Fairness-enhancing interventions in stream classificationcited by 15
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