Understanding what is happening inside the “black box” of large protein models could help researchers choose better models ...

Ziwei Zhu, Assistant Professor, Computer Science, College of Engineering and Computing (CEC), received funding for the project: “III: Small: Harnessing Interpretable Neuro-Symbolic Learning for ...

Their models are of the autoencoder type, that self-organises the information and finds interrelation patterns in the large amount of data.

The inner workings of LLMs are not easy to interpret, but within the past couple of years, researchers have begun using a type of algorithm known as a sparse autoencoder to help shed some light on how ...