Using Tensor Decompositions to Learn Semantic Mappings from Data Streams – The problem of recovering a single vector of a given point from a tensor of vectors is commonly encountered in data mining. This has led to many opportunities for data processing in the form of learning matrix completion (MC) algorithms. While MC algorithms in the literature exploit a non-linearity in the learning procedure, they do not take into account temporal dependencies. Inspired by recent advances in data mining, we propose the efficient learning algorithm CMC that combines linear and non-linearity in an approximate model search over the tensor of vectors. Our algorithm is an extension of MC algorithm, CMC (Chang et al., 2016), which is based on a non-linearity constraint that is a covariance relation between the tensor of vectors and its matrix. CMC allows us to compute the exact point-to-point matrix by computing its rank. Experiments on real datasets demonstrate CMC algorithm outperforms MC algorithms on several benchmark datasets.

As the development of machine learning and neuroscience continues to increase, this paper presents a new learning approach for Bayesian networks. We first present a two-stream neural network, a Bayesian network (BN) and a deep neural network (DNN) model which use sparse Bayesian networks. We then develop a Bayesian network representation representation for the DNN and use this representation to compute the joint probabilities of the two DNN models. We demonstrate that our proposed representation provides a more accurate representation with a much higher success rate as compared to the classical Bayesian networks which are based on only a few parameters, which is beneficial when considering large data sets, as it can be used to represent nonlinear patterns.

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# Using Tensor Decompositions to Learn Semantic Mappings from Data Streams

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Clustering of Medical Records via Sparse Bayesian LearningAs the development of machine learning and neuroscience continues to increase, this paper presents a new learning approach for Bayesian networks. We first present a two-stream neural network, a Bayesian network (BN) and a deep neural network (DNN) model which use sparse Bayesian networks. We then develop a Bayesian network representation representation for the DNN and use this representation to compute the joint probabilities of the two DNN models. We demonstrate that our proposed representation provides a more accurate representation with a much higher success rate as compared to the classical Bayesian networks which are based on only a few parameters, which is beneficial when considering large data sets, as it can be used to represent nonlinear patterns.