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68,838
Deep Learning Methods and Applications
"... Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) t ..."
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Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2
Leveraging Traffic Repetitions for High-Speed Deep Packet Inspection
"... Abstract — Deep Packet Inspection (DPI) plays a major role in contemporary networks, and specifically, in datacenters of content providers, scanned data may be highly repetitive. Most DPI engines are based on identifying signatures in packet payload. This pattern matching process is expensive both i ..."
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Abstract — Deep Packet Inspection (DPI) plays a major role in contemporary networks, and specifically, in datacenters of content providers, scanned data may be highly repetitive. Most DPI engines are based on identifying signatures in packet payload. This pattern matching process is expensive both
Spatio and temporal patterns in large-scale traffic speed prediction
- IEEE Transactions on Intelligent Transportation Systems
"... Abstract—The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful appli-cations, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy becaus ..."
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Abstract—The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful appli-cations, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy
Supervised Representation Learning: Transfer Learning with Deep Autoencoders
"... Transfer learning has attracted a lot of attention in the past decade. One crucial research issue in transfer learning is how to find a good representa-tion for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep learning ..."
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Transfer learning has attracted a lot of attention in the past decade. One crucial research issue in transfer learning is how to find a good representa-tion for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep
1 High-Speed Prediction of Air Traffic for Real-Time Decision
"... The ability to rapidly generate traffic predictions is expected to be central for implementing next-generation air traffic management functionality, both on the ground and aboard aircraft. While high-end computers can be used for this purpose, emerging capabilities of computational hardware such as ..."
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The ability to rapidly generate traffic predictions is expected to be central for implementing next-generation air traffic management functionality, both on the ground and aboard aircraft. While high-end computers can be used for this purpose, emerging capabilities of computational hardware
1 Deep Learning Background
"... What is Deep Learning? A family of methods that uses deep architectures to learn high-level feature representations (p.2) Example of Trainable Features [Lee et al., 2009] ..."
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What is Deep Learning? A family of methods that uses deep architectures to learn high-level feature representations (p.2) Example of Trainable Features [Lee et al., 2009]
Accelerated Deep Learning using Intel Xeon Phi
"... Deep learning, a sub-topic of machine learning inspired by biology, have achieved wide attention in the industry and research community recently. State-of-the-art applications in the area of computer vision and speech recognition (among others) are built using deep learning algorithms. In contrast t ..."
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parallelized implementation of a Convolutional Neural Network (CNN), a deep learning architecture, and our proposed parallelization scheme, CHAOS. Additionally a theoretical analysis and a performance model discuss the algo-rithm in detail and allow for predictions if even more threads are available in the fu-
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68,838
