Compression Theory Powers Interpretable Transformer Architectures
Paper proposes compressing data into low-dimensional Gaussian mixture using CRATE models, achieving competitive results with transformer-based models.
This is a Plain English Papers summary of a research paper called Compression Theory Powers Interpretable Transformer Architectures. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview The paper proposes that the natural objective of representation learning is to compress and transform the data distribution towards a low-dimensional Gaussian mixture. It introduces a measure called "sparse rate reduction" to evaluate the quality of such representations. It shows that popular deep network architectures like transformers can be viewed as o...