Let’s try to make sense of data by mapping it into two dimensions.
Jan Niklas Böhm
Nik is a PhD student in the Department of Data Science at the Hertie Institute for AI in Brain Health at the University of Tübingen and the IMPRS-IS graduate school. He is interested in dimensionality reduction techniques for high-dimensional data. Learning good and compact representations in an unsupervised setting is a key part of that, some examples include contrastive learning in the form of t-SimCNE and t-SNE.
Let’s try to make sense of data by mapping it into two dimensions.