4 citations · 4 across the 3 of their papers we have counts for
3 papers
stat.ML2022
Dynamic mean field programming
George Stamatescu
A dynamic mean field theory is developed for finite state and action Bayesian reinforcement learning in the large state space limit. In an analogy with statistical physics, the Bel…
stat.ML2019★ 4 cited
Critical initialisation in continuous approximations of binary neural networks
George Stamatescu, Federica Gerace, Carlo Lucibello +2
The training of stochastic neural network models with binary () weights and activations via continuous surrogate networks is investigated. We derive new surrogates using a no…
cs.CV2016
Track Extraction with Hidden Reciprocal Chain Models
George Stamatescu, Langford B White, Riley Bruce-Doust
This paper develops Bayesian track extraction algorithms for targets modelled as hidden reciprocal chains (HRC). HRC are a class of finite-state random process models that generali…