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Dynamic mean field programming
A dynamic mean field theory is developed for finite state and action Bayesian reinforcement learning in the large state space limit.
Critical initialisation in continuous approximations of binary neural networks
Moreover, we predict theoretically and confirm numerically, that common weight initialisation schemes used in standard continuous networks, when applied to the mean values of the stochastic binary weights, yield poor training performance.
Track Extraction with Hidden Reciprocal Chain Models
This paper develops Bayesian track extraction algorithms for targets modelled as hidden reciprocal chains (HRC).
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