Multimodal Representation Learning and Clustering(MuReC ‘25)
In conjunction with MMASIA 2025, December 9 – 12, 2025, Grand Millenium Hotel, Kuala Lumpur, Malaysia .
Technical areas
The workshop welcomes both high-quality academic (theoretical or empirical) and practical papers on unsupervised graph representation learning for clustering and related work. For reference, here is a non- exclusive list of topics of interest:
We welcome contributions related (but not limited) to the following topics:
Unsupervised feature learning from structured and unstructured data
Representation learning for recommender systems
Deep, spectral, and probabilistic methods for clustering
Factorization models: matrix/tensor, subspace learning, NMF/NMTF
Multi-view and multimodal clustering
Graph neural networks and attributed graph embedding
Self-supervised and semi-supervised learning paradigms
Large Language Models (LLMs) for representation and clustering
Anomaly and outlier detection in complex data
Representation learning under data sparsity and noise
Scalable learning algorithms for large-scale multimodal data
Application areas
In addition, to attract researchers from various communities, this workshop will encourage submissions on applications, especially those that motivate the development of deep clustering models or comparisons of classical approaches and deep approaches , such as
Recommender Systems
Graph mining
Computer Vision
Information Retrieval
Large Language Models
Bioinformatics
Web mining