Density Characterization of Human Poses In The Presence of Fisher Vectors and One-Class Classifiers


Density Characterization of Human Poses In The Presence of Fisher Vectors and One-Class Classifiers – Neural networks provide a powerful representation of abstract thought patterns and can be used to model biological systems, as has been observed by many other researchers. However, the network representation suffers from overfitting, which leads to the lack of discriminative representations given the input data. We propose a novel approach to perform neural network representation learning by leveraging sparse representations and a recently proposed learning algorithm to learn a sparse representation from a single input. Through a novel deep learning mechanism that explicitly incorporates the dimensionality of the input data, the network learns a classification objective to capture the learned model structure. Importantly, we demonstrate that the proposed approach outperforms some state-of-the-art classifiers in the task of human visual recognition.

Deep learning approaches have recently demonstrated great success in detecting various types of malware in web applications. In this work, we present a novel method, Deep Neural Networks (DNNs), for detecting malware in web applications. Deep-CNNs can efficiently process and summarize malicious actions in Web pages, while being more robust to the local changes of web page elements, e.g., images and text. We develop a Deep-CNN framework, based on a Convolutional Neural Network (CNN) and deep learning to automatically process Web pages, detect malicious actions and detect malicious entities. Our CNN has been trained and compared to a baseline CNN for malware detection and detection, with the aim of detecting malicious entities. In particular, we developed a two-stage CNN architecture, which contains a 3D CNN model with CNN layers, and a 2D CNN model with CNN layers. Our learned CNN system detects malicious entities with an accuracy of over 90% on a publicly available benchmark of malware detection data from the web applications. The detection accuracy is comparable to state-of-the-art detection in web applications of malware detection.

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Density Characterization of Human Poses In The Presence of Fisher Vectors and One-Class Classifiers

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    Fault Detection Techniques for Robotic Surgery Using SensorsDeep learning approaches have recently demonstrated great success in detecting various types of malware in web applications. In this work, we present a novel method, Deep Neural Networks (DNNs), for detecting malware in web applications. Deep-CNNs can efficiently process and summarize malicious actions in Web pages, while being more robust to the local changes of web page elements, e.g., images and text. We develop a Deep-CNN framework, based on a Convolutional Neural Network (CNN) and deep learning to automatically process Web pages, detect malicious actions and detect malicious entities. Our CNN has been trained and compared to a baseline CNN for malware detection and detection, with the aim of detecting malicious entities. In particular, we developed a two-stage CNN architecture, which contains a 3D CNN model with CNN layers, and a 2D CNN model with CNN layers. Our learned CNN system detects malicious entities with an accuracy of over 90% on a publicly available benchmark of malware detection data from the web applications. The detection accuracy is comparable to state-of-the-art detection in web applications of malware detection.


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