Representation Learning and Clustering (RLC)
RLC24 - PRICAI-Workshop
Learning Representation and Clustering (RLC)
In conjunction with PRICAI 2024 November. 18 - 24 2024,Kyoto, Japan
Workshop Overview
Data clustering and representation learning play an indispensable role in data science. They are very useful to explore massive data in many fields, including information retrieval, natural language processing, bioinformatics, recommender systems and computer vision. Despite their success, most existing clustering methods are severely challenged by the data generated by modern applications, which are typically high dimensional, noisy, heterogeneous and sparse or even collected from multiple sources or represented by multiple views where each describes a perspective of the data. This has driven many researchers to investigate new deep clustering models to overcome these difficulties. One promising category of such models relies on representation learning. Indeed learning a good data representation is crucial for clustering algorithms and combining the two tasks is a common way of exploring this type of data. The idea is to embed the original data into a low dimensional latent space and then perform clustering on this new space. Both tasks can be carried out sequentially or jointly; combining the two tasks is a common way of exploring this type of data.
Hence, one main goal of the workshop is to bring together the leading researchers who work on state-of-the-art deep unsupervised feature extraction and clustering models, and also the practitioners who seek novel applications. In summary, this workshop is an opportunity to:
Present the recent advances in representation learning and clustering methods including multi-view clustering and semi-supervised learning which are not explored well.
Outline potential applications that could inspire new deep approaches.
Explore benchmark data to better evaluate and study deep clustering models.
Evaluate the effectiveness of deep clustering models compared to classical approaches in terms of interpretability of clusters and Scalability .
The workshop is co-located with the PRICAI2024 Pacific Rim International Conjerence on Artificial Intelligence - Nov 18-24, 202).
Important Dates
Workshop paper submissions: August 31, 2024 September 15, 2024
Workshop paper notifications: September 25, 2024
Camera-ready deadline and copyright forms: September 30, 2024
Early bird registration finishes: TBA
Conference dates: November. 18 - 24 2024
**All deadlines are 11:59 pm anywhere on earth**
Workshop Chairs
General Chair
Prof. Mohamed Nadif
Centre Borelli, CNRS, Université de Paris Cité
45, rue des Saints Pères, 75270, Paris
Program Co-chair
Assoc. Prof. Lazhar Labiod
Centre Borelli, CNRS, Université de Paris Cité
45, rue des Saints Pères, 75270, Paris