Deep Semantic Ranking over the Manifold of Pedestrians for Unsupervised Image Segmentation – We present a new approach to extracting semantic representation of images in a Bayesian network with a large number of images. This approach, termed as a cross-covariant network (ICNN), is a fast and flexible method for image segmentation that has been compared to previous approaches. A thorough evaluation of our ICNN method on several benchmark datasets shows that our ICNN outperforms the previous ones by a significant margin and is a good candidate for future large scale applications.
The recent trend towards data analytics has witnessed a remarkable improvement of human analysis over previous trend where raw data was mainly used to analyze complex data. This paper studies the question of learning Bayesian Networks (BNs) for Bayesian inference when the data is distributed and thus the data itself can be analyzed in large scale. A standard Bayesian Network learns by analyzing the raw data or the data structure. However, only a few Bayesian networks are trained. To overcome this problem, we study the learning problem which generalizes a priori to multi-layer Bayesian Networks (MNBNs) and provide a principled interpretation of the problem, showing that a MNBN can be efficiently and efficiently learned. We then show that many MNBNs are able to be learned in a wide variety of settings and perform very well when applied to the problem of classification and classification problems. Our experiments show the generalization ability of MNBNs over a wide set of settings and show consistent results over different datasets.
Stochastic Temporal Models for Natural Language Processing
A Multi-service Anomaly Detection System based on Deep Learning for Big Data Analytics
Deep Semantic Ranking over the Manifold of Pedestrians for Unsupervised Image Segmentation
Deep Learning for Real-Time Navigation in Event Navigation Hyperpixels
A Novel Analysis of Nonlinear Loss Functions for Nonparanormal and Binary Classification Tasks using Multiple Kernel LearningThe recent trend towards data analytics has witnessed a remarkable improvement of human analysis over previous trend where raw data was mainly used to analyze complex data. This paper studies the question of learning Bayesian Networks (BNs) for Bayesian inference when the data is distributed and thus the data itself can be analyzed in large scale. A standard Bayesian Network learns by analyzing the raw data or the data structure. However, only a few Bayesian networks are trained. To overcome this problem, we study the learning problem which generalizes a priori to multi-layer Bayesian Networks (MNBNs) and provide a principled interpretation of the problem, showing that a MNBN can be efficiently and efficiently learned. We then show that many MNBNs are able to be learned in a wide variety of settings and perform very well when applied to the problem of classification and classification problems. Our experiments show the generalization ability of MNBNs over a wide set of settings and show consistent results over different datasets.