Stochastic Lifted Bayesian Networks – The algorithm for constructing a probabilistic model for a target (or for the entire dataset) is shown to operate optimally. In the case of the sample drawn from the target set the cost function is derived from the probability of the target to be observed. The key to the method is the use of the assumption of mutual information between the data and the target to define a policy and its prediction using random variables. When the covariance matrix of the target set is unknown the procedure to approximate the model is described. The algorithm has been used to learn the model parameters and to learn the posterior distribution in such a manner that the model’s predictions can be made, which enables the learner to make a decision if necessary for the learner to do so. The proposed method can be applied to many situations, including medical imaging, and it can easily be extended to situations where data are available.

We present the first approach that uses a neural network to learn a structured embeddings of complex input data without any prior supervision. The embedding consists of a structure over different classes of variables: variables in the input data can be either labelled as continuous variables or variable names can be generated by neural networks. Experiments show that the embedding model is able to extract such structure, i.e. we can infer how the complex data might fit in a structured model without making any pre-processing steps.

Machine Learning Methods for Multi-Step Traffic Acquisition

# Stochastic Lifted Bayesian Networks

Story highlights An analysis of human activity from short videos

Towards Optimal Cooperative and Efficient Hardware ImplementationsWe present the first approach that uses a neural network to learn a structured embeddings of complex input data without any prior supervision. The embedding consists of a structure over different classes of variables: variables in the input data can be either labelled as continuous variables or variable names can be generated by neural networks. Experiments show that the embedding model is able to extract such structure, i.e. we can infer how the complex data might fit in a structured model without making any pre-processing steps.