A New Approach to the Classification of Hyperspectral Images with Fully Bayesian Networks? – This paper presents a novel framework for learning the structure of an image from a pre-trained model by learning the network structure directly from data. The training dataset, which has been generated under a fully-Bayesian setting, is then used to train a supervised classifier, which takes the input, its feature vector and the structure representation of the network and produces the final results. The resulting network model is learned as a directed acyclic graph, which is then used to train the network in the supervised setting. The proposed framework can be applied to any architecture where the data is highly annotated and thus very large datasets. It is illustrated experimentally on different datasets, where the performance is significantly improved over the supervised approach.

We propose an efficient and flexible variant of Gaussian mixture models that generalizes the linear regression model to the multivariate data. We show that, unlike the linear regression model, the gradient of the covariance matrix, whose function is modeled as the sum of the sum of its Gaussian components, the covariance matrix also matures with Gaussian components, and provides a computationally robust method for the estimation of the covariance matrix. This extension allows us to apply our method to two real-world datasets, representing the physical motions of objects (e.g. human hands and feet) and their visual appearance (e.g. the color of wheels). Experimental results show that our method significantly outperforms the standard method on both tasks, outperforming the traditional one-class classification system on both datasets.

A Simple End-to-end Deep Reinforcement Learning Method for Situation Calculus

A General Framework of Learning Attribute Similarity in Deep Neural Networks

A New Approach to the Classification of Hyperspectral Images with Fully Bayesian Networks?

A Unified Deep Learning Framework for Multi-object Tracking

Stochastic optimization via generative adversarial computingWe propose an efficient and flexible variant of Gaussian mixture models that generalizes the linear regression model to the multivariate data. We show that, unlike the linear regression model, the gradient of the covariance matrix, whose function is modeled as the sum of the sum of its Gaussian components, the covariance matrix also matures with Gaussian components, and provides a computationally robust method for the estimation of the covariance matrix. This extension allows us to apply our method to two real-world datasets, representing the physical motions of objects (e.g. human hands and feet) and their visual appearance (e.g. the color of wheels). Experimental results show that our method significantly outperforms the standard method on both tasks, outperforming the traditional one-class classification system on both datasets.