Sharmily Roy, Henry Shelton Brown, Lisa Sanger Blinn, Sarah Carter Narendorf, Jane E Hamilton
A microcosting approach for planning and implementing community-based mental health prevention program: what does it cost?
Health economics review
paper conjectures validates framework allows action inference supervised neural networks supervised neural networks constructed objective maximize performance metric given task done reducing free energy associated surprisal training however bottomup inference nature supervised networks passive process renders fallible noise paper provide thorough background supervised neural networks generative discriminative discuss functionality perspective free energy principle provide framework introducing action inference introduce new measurement called stochastic surprisal function network input possible action action one outputs neural network learnt thereby lending stochasticity measurement stochastic surprisal validated two applications image quality assessment recognition noisy conditions show noise characteristics ignored make robust recognition analyzed estimate image quality scores apply stochastic surprisal two applications three datasets plugin 12 networks provides statistically significant increase among measures conclude discussing implications proposed stochastic surprisal areas cognitive psychology including expectancymismatch abductive reasoning