Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. is currently not grounded in explicit modelling assumptions which so far precluded its adoption Bayesian modelling. Using entropic reasoning we show that dropout can be interpreted optimal inference under constraints. We demonstrate this on an an...

Dynamic Bayesian networks are Bayesian networks which explicitly incorporating the dimension of time. They are distinguished into repetitive and non-repetitive networks. Repetitive networks have the same set of random (statistical) variables and independence relations at each time step, whereas in non-repetitive networks the set of random variables and the independence relations between these r...

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In this paper we study a probabilistic approach to characterize Interpersonal Behaviours (IBs) in a social concept by exploring the existent interrelation between body motion features. Human activities were explored in different level of complexities, such as social-based human activity. To bridge the existent big gap between human body motions and the IBs analysis, a set of proper dependencies...