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Herding as a Learning System with Edge-of-Chaos Dynamics
[article]
2016
arXiv
pre-print
Herding defines a deterministic dynamical system at the edge of chaos. It generates a sequence of model states and parameters by alternating parameter perturbations with state maximizations, where the sequence of states can be interpreted as "samples" from an associated MRF model. Herding differs from maximum likelihood estimation in that the sequence of parameters does not converge to a fixed point and differs from an MCMC posterior sampling approach in that the sequence of states is generated
arXiv:1602.03014v2
fatcat:pxweht2l7vgvjozwvd4re3qomi