Researchers from MIT, Microsoft, and Google have introduced a “periodic table of machine learning” that stands to unify many different machine learning techniques using a single framework. Their framework, called Information Contrastive Learning (I-Con), shows that a variety of different algorithms including classification, regression, large language modeling, clustering, dimensionality reduction, and spectral graph theory, can all be viewed in a more general context.
A Unifying Framework for Representation Learning - Microsoft Research
the paper: I-CON: A UNIFYING FRAMEWORK FOR REPRESENTATION LEARNING
No comments:
Post a Comment