deep neural network architectures
Deep Learning as a Frontier of Machine Learning: A Review
... the deep learning methods avoid feature engineering in supervised learning ...data, deep learning algorithms can be applied to such kind of ...The deep belief networks are the example of deep ...
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Image Description using Deep Neural Networks
... Convolutional Neural Networks (CNNs) are a specific form of FNNs that explicitly assume the inputs to the network be structured samples, such as audio signals or image pixels which can be ...These ...
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Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals
... signals. Deep neural networks, by contrast, can automatically extract person specific features - an important ...However, deep neural networks typically have the drawback of large numbers of ...
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Deep architectures for Neural Machine Translation
... recurrent neural network architec- tures with multiple layers allow different connec- tion schemes (Pascanu et ...be deep in dif- ferent ways, giving rise to a large number of pos- sible ...
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Learning Deep Architectures for AI - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... a Deep Belief Net, both distributions share essentially the same parameters, so the parameters involved in esti- mating P (Y | X) benefit from a form of data-dependent regularization: they have to agree to some ...
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Understanding deep learning via backtracking and deconvolution
... Convolutional neural networks have been widely adopted in solving problems in com- puter ...new network architectures, we decide to take a closer look at the miss-classified instances in order to ...
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The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Di erent Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review
... of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) ...
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Neural Network Architectures for Arabic Dialect Identification
... ASR-Deep Neural Network as described in (Ali et ...submitting neural networks, deepCybErNetRun, competed with a bidirectional Long Short Term Memory network (biLSTM) on words (F1: ...
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Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... Our network contains no pooling layer or full-connected layer, thus it is sensitive to the initialization parameters and learning ...the network may fall into a bad local minimum, and the learned filters ...
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Automatic Brain Tumor Segmentation by Deep Convolutional Networks and Graph Cuts
... the deep convolutional neural network architectures that were previously successful for semantic segmentation and medical image segmentation, such as fully convolutional neural networks ...
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A STUDY & REVIEW ON TECHNIQUES, APPLICATIONS AND CHALLENGES USED IN DEEP LEARNING Khushboo Mandhaniya*1 & Bhushit Chandra Nema2
... Recursive Neural Network: Recursive neural networks comprise a class of architecture that can operate on structured ...these architectures are deep in structure, they lack the capacity ...
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A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images
... on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted ...the network remains the most ...
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Face recognition with Bayesian convolutional networks for robust surveillance systems
... 2.2 Deep learning-based face recognition approaches Although machine learning techniques for facial recogni- tion have provided decent results, these techniques do not perform well under unconstrained ...hand, ...
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Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... feedforward neural network, the depth of the CAPs (thus of the network) is the number of hidden layers plus one (as the output layer is also parameterized), but for recurrent neural networks, ...
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Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks
... convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) ...convolutional neural network ...proposed deep ...
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Intrusion Detection using Artificial Neural Network and Swarm Intelligence Algorithm
... real-time network, carefully acquired for the applications which include web browsing, ...labeled network traces, incorporating full packet payloads in pcap organize, which along with the important profiles ...
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Image Description Using Deep Neural Network
... in deep learning have inspired works which discuss a deep learning based approach inspired by recent advances in the applications of Convolutional deep neural networks and recurrent ...
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Deep Machine Learning In Neural Networks
... In deep learning technique the compression and efficiency acts as two ...In network pruning, the unnecessary connections are removed and larger network is used for smaller network ...
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DEVELOPMENT AND APPLICATION OF A STAGE GATE PROCESS TO REDUCE THE UNERLYING RISKS OF IT SERVICE PROJECTS
... The scholars in [10] endeavored in [12] to economize the effort spent in computing but with accuracy through decreasing the size of outlines from 20×480 to 10×480. This work was later developed by the same scholars to ...
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DETECTION AND CLASSIFICATION OF DIABETIC RETINOPATHY USING ADAPTIVE BOOSTING AND ARTIFICIAL NEURAL NETWORK
... our network for training purpose. Multiple layered convolutional neural networks are designed and trained with different hyper- parameter values, changing behavior of the training curves are analyzed and ...
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