April 1, 2024, 2:38 a.m. | Synced

Synced syncedreview.com

A King’s College London research team delves into a theoretical exploration of the transformer architecture, employing the lens of topos theory. This innovative approach conjectures that the factorization through "choose" and "eval" morphisms can yield effective neural network architecture designs.


The post KCL Leverages Topos Theory to Decode Transformer Architectures first appeared on Synced.

ai architecture architectures artificial intelligence college decode deep-neural-networks designs exploration factorization king london machine learning machine learning & data science ml network network architecture neural network research research team team technology theory through topology transformer transformer architecture

More from syncedreview.com / Synced

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Principal, Product Strategy Operations, Cloud Data Analytics

@ Google | Sunnyvale, CA, USA; Austin, TX, USA

Data Scientist - HR BU

@ ServiceNow | Hyderabad, India