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.

Most of the machine learning (ML) techniques for semantic segmentation have been used to classify large-scale object images, and to classify objects in large class collections. However, they are not yet a viable tool for large-scale object segmentation. We present a novel ML formulation to solve the ML problems involving large class collections of objects. Specifically, we propose a novel semantic segmentation strategy. The proposed strategy combines a semantic segmentation method, which simultaneously models object semantics in a large data set and an ML method, which predicts the segmentation probability in a large class by taking into consideration a large number of classes. The ML method is implemented as a supervised neural network and it takes a deep representation of the semantic segmentation probability within the ML system. To further reduce model complexity, we provide a novel ML analysis method based on the segmentation probability within the ML network. We propose a novel ML-LM algorithm to achieve the semantic segmentation probability within the ML system. Experimental results indicate that our ML ML-LM algorithm delivers significantly higher classification throughput than a conventional ML-SVM algorithm.

Learning Sparse Bayesian Networks with Hierarchical Matching Pursuit

Fast and easy control with dense convolutional neural networksMost of the machine learning (ML) techniques for semantic segmentation have been used to classify large-scale object images, and to classify objects in large class collections. However, they are not yet a viable tool for large-scale object segmentation. We present a novel ML formulation to solve the ML problems involving large class collections of objects. Specifically, we propose a novel semantic segmentation strategy. The proposed strategy combines a semantic segmentation method, which simultaneously models object semantics in a large data set and an ML method, which predicts the segmentation probability in a large class by taking into consideration a large number of classes. The ML method is implemented as a supervised neural network and it takes a deep representation of the semantic segmentation probability within the ML system. To further reduce model complexity, we provide a novel ML analysis method based on the segmentation probability within the ML network. We propose a novel ML-LM algorithm to achieve the semantic segmentation probability within the ML system. Experimental results indicate that our ML ML-LM algorithm delivers significantly higher classification throughput than a conventional ML-SVM algorithm.