Overview
The European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020) is hosting a special session focused on the topic of tensor decompositions in deep learning. This session aims to explore the growing significance of tensor methods in the analysis and processing of complex data within the machine learning community.
Background & Relevance
In recent years, tensor decompositions have emerged as a crucial tool for handling large-scale, intricate datasets, particularly in the context of big data. These methods not only facilitate efficient multiway data analysis but also enhance the theoretical understanding of deep neural networks. The application of tensor techniques spans various machine learning paradigms, including neural networks and probabilistic models, thereby enabling more efficient model parameter compression and improved computational performance.
Key Details
- Event: Special Session on Tensor Decompositions in Deep Learning
- Conference: ESANN 2020
- Dates: 22-24 April 2020
- Location: Bruges, Belgium
- Submission Deadline: 18 November 2019
- Notification of Acceptance: 31 January 2020
- Link: ESANN 2020
Eligibility & Participation
This call for papers is directed towards researchers and practitioners in the field of machine learning, particularly those with a focus on tensor decompositions and their applications in deep learning. Contributions are welcome from both established researchers and those presenting preliminary findings.
Submission or Application Guidelines
Interested authors should submit their papers through the ESANN portal, adhering to the submission guidelines available on the conference website. Additionally, authors are encouraged to notify the special session organizers via email with the tentative title of their contribution as soon as possible.
Additional Context / Real-World Relevance
The integration of tensor decompositions into machine learning frameworks is increasingly relevant as the complexity of data continues to grow. By leveraging these techniques, researchers can enhance the efficiency and effectiveness of various machine learning applications, including image processing, sensor data analysis, and more. This special session provides a platform for sharing innovative ideas and advancements in this vital area of research.
Conclusion
Researchers are encouraged to participate in this special session by submitting their work on tensor decompositions in deep learning. This is an excellent opportunity to contribute to a rapidly evolving field and engage with fellow experts. Interested parties should prepare their submissions and mark the important dates to ensure their participation in ESANN 2020.
Category: CFP & Deadlines
Tags: tensor decompositions, deep learning, machine learning, neural networks, signal processing, multivariate data, computational intelligence, ESANN
