Low Dimensional Representation

Low-dimensional representation aims to reduce the complexity of high-dimensional data while preserving essential information, facilitating efficient analysis and improved performance in machine learning tasks. Current research focuses on developing novel algorithms and model architectures, such as autoencoders, variational autoencoders, and graph neural networks, often incorporating techniques like optimal transport and contrastive learning to achieve effective dimensionality reduction. These advancements are significant for diverse applications, including improved classification, clustering, and uncertainty quantification in fields ranging from medical imaging and genomics to knowledge graph representation and subsurface modeling.