Identification of relevant subtypes and their families through multivariate and cross-lingual data analysis


Identification of relevant subtypes and their families through multivariate and cross-lingual data analysis – We propose a simple way to use a single-dimensional manifold as a representation of the distribution of variables. This representation is of the nonlinear form of a linear function and contains many forms of arbitrary data. The non-linearity is demonstrated by numerical experiments on synthetic and real data. Results show that the proposed representation improves in the sense that it exhibits more accurate statistical analysis of multivariate distributions and more reasonable bounds on the distribution of unknown variables.

The Convolutional neural networks (CNN) are widely used for face recognition and pose estimation from video videos. The CNNs have a wide range of discriminant analysis capabilities and are able to accurately extract facial facial expressions from videos. CNNs have also achieved competitive performance in many tasks: semantic segmentation, object detection, object modeling, and facial pose estimation, which were considered in the literature. We propose a simple and effective framework for extracting facial expressions from videos (to the best of our knowledge) that achieves promising performance with the best of the three recognition rates by the authors. We also present some preliminary results on image retrieval tasks, as well as a recent work on action recognition. Our method was well trained on 486,000 videos of different domains (cameras) and achieved competitive success rates on the task of action recognition.

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Identification of relevant subtypes and their families through multivariate and cross-lingual data analysis

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  • Profit Driven Feature Selection for High Dimensional Regression via Determinantal Point Process Kernels

    Recurrent Convolutional Neural Network for Action DetectionThe Convolutional neural networks (CNN) are widely used for face recognition and pose estimation from video videos. The CNNs have a wide range of discriminant analysis capabilities and are able to accurately extract facial facial expressions from videos. CNNs have also achieved competitive performance in many tasks: semantic segmentation, object detection, object modeling, and facial pose estimation, which were considered in the literature. We propose a simple and effective framework for extracting facial expressions from videos (to the best of our knowledge) that achieves promising performance with the best of the three recognition rates by the authors. We also present some preliminary results on image retrieval tasks, as well as a recent work on action recognition. Our method was well trained on 486,000 videos of different domains (cameras) and achieved competitive success rates on the task of action recognition.


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