In this section
@article{Poole,
author = {Poole, W and Ouldridge, T and Gopalkrishnan, M},
journal = {Journal of the Royal Society Interface},
title = {Autonomous learning of generative models with chemical reaction network ensembles},
url = {http://hdl.handle.net/10044/1/115278},
}
TY - JOUR
AB - Can a micron sized sack of interacting molecules autonomously learn an internalmodel of a complex and fluctuating environment? We draw insights from controltheory, machine learning theory, chemical reaction network theory, and statisticalphysics to develop a general architecture whereby a broad class of chemical systemscan autonomously learn complex distributions. Our construction takes the form ofa chemical implementation of machine learning’s optimization workhorse: gradientdescent on the relative entropy cost function which we demonstrate can be viewedas a form of integral feedback control. We show how this method can be applied tooptimize any detailed balanced chemical reaction network and that the constructionis capable of using hidden units to learn complex distributions.
AU - Poole,W
AU - Ouldridge,T
AU - Gopalkrishnan,M
SN - 1742-5662
TI - Autonomous learning of generative models with chemical reaction network ensembles
T2 - Journal of the Royal Society Interface
UR - http://hdl.handle.net/10044/1/115278
ER -
