Jan. 1, 2022, midnight | Michael Fairbank, Spyridon Samothrakis, Luca Citi

JMLR www.jmlr.org

Deep learning uses neural networks which are parameterised by their weights. The neural networks are usually trained by tuning the weights to directly minimise a given loss function. In this paper we propose to re-parameterise the weights into targets for the firing strengths of the individual nodes in the network. Given a set of targets, it is possible to calculate the weights which make the firing strengths best meet those targets. It is argued that using targets for training addresses …

deep learning learning space

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

Principal Machine Learning Engineer (AI, NLP, LLM, Generative AI)

@ Palo Alto Networks | Santa Clara, CA, United States

Consultant Senior Data Engineer F/H

@ Devoteam | Nantes, France