fully connected neural networks
EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks
... training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method includ- ing: 1) an approximation of the Hessian matrix and 2) a conju- gate gradient ...
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Phone recognition with hierarchical convolutional deep maxout networks
... convolutional neural networks (CNNs) have recently been shown to outperform fully connected deep neural networks (DNNs) both on low-resource and on large-scale speech ...
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Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks
... The FC-CRF model effectively removed the salt-and-pepper effects, which was employed in this work for post-processing. Compared with SVM-based classification methods, FC-CRF methods achieved higher accuracy. Gerke et al. ...
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Learning Fully Dense Neural Networks for Image Semantic Segmentation
... Decoder. As the encoder-decoder structure has much more layers than the original encoder network, how to boost gradi- ent backpropagation and information flow becomes another problem we have to deal with. The decoder ...
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Fight Detection in Hockey Videos using Deep Network
... Feed-forward Neural Networks with hard-limiting (signum) activation functions can adapt to complex decision ...are fully-connected and do not have a specific receptive ...the networks ...
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Stability Criteria Of Fully Connected Hopfield Artificial Neural Network
... biological neural networks in that they do not require the programming of tasks but generalize and learn from ...AN Networks comprise of a set of very simple processing elements that emulate the ...
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A Novel Method for Remotely Sensed Hyperspectral Image Classification Based on Convolutional Neural Network
... Convolutional Neural Networks (CNN) are gaining attention due to their capability to automatically discover relevant relative features in image classification ...two Fully Connected Network ...
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MoonGrad at SemEval 2019 Task 3: Ensemble BiRNNs for Contextual Emotion Detection in Dialogues
... convolution neural networks (Lakomkin et ...intermediate neural layers, ended with a fully connected layer to classify the contextual ...
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Stable Adaptive Neural Control of a Robot Arm
... Indirect Neural net- work Controller (IDNC) composed of two separate fully connected recurrent neural networks: the Neural Con- troller (NC) and the Adaptive instantaneous ...
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Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... biological neural networks, the connection strengths are sometimes also called synaptic ...in neural network ...vector. Neural networks are often classified with respect to their ...
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Intrusion Detection System Using Hybrid Approach by MLP and K-Means Clustering
... introduce Neural Network Committee Machine (NNCM), it consist Input Reduction System which is based on Intrusion Detection System and Principal Component Analysis (PCA) and these are represented by three level ...
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Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks
... were connected to the microgrid. At t=2sec, the battery at Bus 4was connected to the microgrid with full charging power, which increased the power supplied by the grid to the microgrid ...
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Network identification via multivariate correlation analysis
... At the beginning of this discussion, I highlighted as, for the same system, it is possi- ble to identify different network structures depending on what characteristic is ob- served and in which conditions. In this ...
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Improving Language Modeling using Densely Connected Recurrent Neural Networks
... is connected with every output of every other layer, we get a densely connected recurrent neural ...densely connected LSTMs need fewer parameters than stacked LSTMs to achieve similar ...
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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, in ...
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An Evolutionary Approach: Analysis of Artificial Neural Networks
... artificial neural network solutions have been trained with ...artificial neural network must be trained before it becomes ...a neural network is compared to the desired ...
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An Introduction to Neural Networks - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... The function of real neurons is extremely complex. However, the essential information processing attributes may be summarized as follows. A neuron receives input signals from many other (afferent) neurons. Each such ...
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Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks
... recurrent neural net- work which uses context ...is connected through a re- current link with all the nodes in the hidden layer as well as the typical feedforward connections from each input node ...
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A CNN Based Approach for Garments Texture Design Classification
... Identifying garments texture design automatically for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can ...
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Fully Connected Neural Network with Advance Preprocessor to Identify Aggression over Facebook and Twitter
... Burnap and Williams 2015 present ”Cyber hate speech on Twitter: An application of machine classification and statistical modeling for policy and decision making” and focuses on the use o[r] ...
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