Learning Sparse Bayesian Networks with Hierarchical Matching Pursuit – We present a novel deep neural network architecture for predicting and ranking items by means of hierarchical matching pursuit. We propose a novel approach for ranking items by means of Hierarchical Matching Pursuit, which aims to approximate item ranking. Moreover, we extend our technique to the use of multi-task learning on a deep learning model. A Bayesian network is trained to predict the item ranking, using the rank correlation between items, using a discriminant model in order to obtain the rank correlation between items. We also propose a new method to rank items by means of hierarchical matching pursuit that aims for minimizing the gap between the items’ rank correlation. Experiments have shown that our method outperforms state-of-the-art ranking model prediction on two different datasets.

This paper presents an efficient algorithm, SDA, for the purpose of optimizing optimization decisions involving discrete and continuous variables. The algorithm uses a convex optimization algorithm that optimizes a matrix-valued objective function on the input matrix. Its performance is evaluated on the benchmark dataset of KJB, a commercial online K-Nearest Neighbor search algorithm. In the benchmark case, the algorithm gives a linear convergence rate compared to the best algorithms. The paper also presents a method for evaluating the optimal distribution for solving the optimal algorithm.

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Learning Sparse Bayesian Networks with Hierarchical Matching Pursuit

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An Efficient Algorithm for Stochastic OptimizationThis paper presents an efficient algorithm, SDA, for the purpose of optimizing optimization decisions involving discrete and continuous variables. The algorithm uses a convex optimization algorithm that optimizes a matrix-valued objective function on the input matrix. Its performance is evaluated on the benchmark dataset of KJB, a commercial online K-Nearest Neighbor search algorithm. In the benchmark case, the algorithm gives a linear convergence rate compared to the best algorithms. The paper also presents a method for evaluating the optimal distribution for solving the optimal algorithm.