Overview
The European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 2020 is set to host a special session focused on the theme of ‘Learning from Partially Labeled Data’. This session aims to bridge the gap between human and machine learning capabilities, particularly in the context of leveraging limited data effectively.
Background & Relevance
In recent years, the field of machine learning has made significant strides, yet it still struggles to replicate the efficiency of human learning. Humans can learn from a minimal number of examples and integrate knowledge across various domains seamlessly. This special session will explore innovative approaches that mimic human-like learning processes, such as domain adaptation, transfer learning, and few-shot learning. These methodologies are crucial for applications in areas like biomedical signal processing, medical imaging, and complex systems analysis, making this session highly relevant for researchers and practitioners alike.
Key Details
- Event: Special session on ‘Learning from Partially Labeled Data’
- Conference: ESANN 2020
- Dates: 22-24 April 2020
- Location: Bruges, Belgium
- Submission Deadline: 18 November 2019
- Notification of Acceptance: 31 January 2020
- Paper Format: Follow ESANN guidelines (see ESANN website)
Eligibility & Participation
This call for papers is open to researchers and practitioners interested in the latest advancements in learning from partially labeled data. Contributions are welcome from those working in both shallow and deep learning models, as well as related methodologies.
Submission or Application Guidelines
- Prepare a full paper (maximum 6 pages) following the ESANN paper format.
- Submit your paper by the deadline of 18 November 2019.
- Ensure adherence to the submission guidelines available on the ESANN website.
Additional Context / Real-World Relevance
The ability to learn from partially labeled data is increasingly important in various fields, particularly in scenarios where data is scarce or expensive to obtain. This session will highlight the significance of developing flexible learning strategies that can adapt to diverse application domains, thereby enhancing the practical impact of machine learning technologies.
Conclusion
Researchers are encouraged to contribute to this special session by submitting their papers and sharing insights on innovative learning strategies. This is a valuable opportunity to engage with the AI/ML community and contribute to the advancement of learning methodologies that can bridge the gap between human and machine learning capabilities.
Category: CFP & Deadlines
Tags: machine learning, deep learning, transfer learning, domain adaptation, few-shot learning, medical imaging, neural networks, ESANN
