Mixed Space

Mixed space research focuses on developing methods to effectively represent and analyze data residing in multiple, potentially disparate spaces, aiming to bridge the gap between different data modalities and improve downstream tasks. Current research emphasizes the development of novel embedding techniques, often leveraging graph neural networks, variational autoencoders, and optimal transport methods, to create unified representations that capture relationships across diverse data types. This work has significant implications for various fields, including machine learning, natural language processing, and cultural heritage studies, by enabling more robust and interpretable analyses of complex, multi-modal datasets.