Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
State-of-the-art deep learning: Evolving machine intelligence toward tomorrow's intelligent network traffic control systems
Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
A survey of the recent architectures of deep convolutional neural networks
Abstract Deep Convolutional Neural Network (CNN) is a special type of Neural Networks,
which has shown exemplary performance on several competitions related to Computer …
which has shown exemplary performance on several competitions related to Computer …
Routing or computing? The paradigm shift towards intelligent computer network packet transmission based on deep learning
Recent years, Software Defined Routers (SDRs)(programmable routers) have emerged as a
viable solution to provide a cost-effective packet processing platform with easy extensibility …
viable solution to provide a cost-effective packet processing platform with easy extensibility …
Learning deep hierarchical visual feature coding
In this paper, we propose a hybrid architecture that combines the image modeling strengths
of the bag of words framework with the representational power and adaptability of learning …
of the bag of words framework with the representational power and adaptability of learning …
Hybridnet: Classification and reconstruction cooperation for semi-supervised learning
In this paper, we introduce a new model for leveraging unlabeled data to improve
generalization performances of image classifiers: a two-branch encoder-decoder …
generalization performances of image classifiers: a two-branch encoder-decoder …
Deep representation learning with target coding
We consider the problem of learning deep representation when target labels are available.
In this paper, we show that there exists intrinsic relationship between target coding and …
In this paper, we show that there exists intrinsic relationship between target coding and …
Max-min convolutional neural networks for image classification
Convolutional neural networks (CNN) are widely used in computer vision, especially in
image classification. However, the way in which information and invariance properties are …
image classification. However, the way in which information and invariance properties are …
Regularization for unsupervised deep neural nets
B Wang, D Klabjan - Proceedings of the AAAI Conference on Artificial …, 2017 - ojs.aaai.org
Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep
belief networks (DBNs), are powerful tools for feature selection and pattern recognition …
belief networks (DBNs), are powerful tools for feature selection and pattern recognition …
Photo aesthetics analysis via DCNN feature encoding
We propose an automatic framework for quality assessment of a photograph as well as
analysis of its aesthetic attributes. In contrast to the previous methods that rely on manually …
analysis of its aesthetic attributes. In contrast to the previous methods that rely on manually …