Video Anomaly Detection Using Learned Convnet Features – This paper addresses the problem of learning a discriminative image of a person from two labeled images. Existing approaches address this problem by using latent representation learning and latent embedding. However, the underlying latent embedding structure often fails to capture the underlying person identity structure. In this paper, proposed approaches address this problem by learning deep representations of latent spaces. These representations are learned using the image features that have been captured from a shared space, thus providing a more robust discriminative model of the person. Extensive numerical experiments on two publicly available datasets demonstrate the effectiveness of our proposed approach. The results indicate that our approach can be used for person identification tasks in a non-convex problem with high dimensionality.

Our understanding of the function of a large set of variables is important for the analysis of complex data. In this work, we propose a new method for the extraction and interpretation of the parameters that is similar to the standard approach of learning function models.

On the Complexity of Learning the Semantics of Verbal Morphology

Selecting the Best Bases for Extractive Summarization

Video Anomaly Detection Using Learned Convnet Features

On the Generalizability of Kernelized Linear Regression and its Use as a Modeling Criterion

A Data based Approach for Liver and Bone Diseases PredictionOur understanding of the function of a large set of variables is important for the analysis of complex data. In this work, we propose a new method for the extraction and interpretation of the parameters that is similar to the standard approach of learning function models.