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large scale neural networks

PubMedCentral-PMC4853295.pdf

PubMedCentral-PMC4853295.pdf

... four large-scale neural networks, namely, the dorsal attention network (DAN, (20)), the default-mode network (DMN, (21)), the salience network (SAL, (22)), and the executive control network ...

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Phone recognition with hierarchical convolutional deep maxout networks

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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Group Invariant Deep Representations for Image Instance Retrieval

Group Invariant Deep Representations for Image Instance Retrieval

... in large scale image classification, representations extracted from Convolutional Neural Networks (CNN) are quickly gaining ground on Fisher Vectors (FVs) as state-of-the-art global ...

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Forecasting of rainfall using different input selection methods on climate signals for neural network inputs

Forecasting of rainfall using different input selection methods on climate signals for neural network inputs

... used neural networks for monthly and seasonal predictions (from June to September) based on large scale satellite signals in Orissa, eastern ...the neural network ...

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FPGA Based Platform for Spiking Neural Network

FPGA Based Platform for Spiking Neural Network

... for large scale spiking neural networks, Understanding of mathematical model of Neuron and Hardware utilization versus number of ...reconfigurable neural layer, which is implemented ...

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Solving Large-Scale Multi-Objective Optimization Problems with Sparse Optimal Solutions via Unsupervised Neural Networks

Solving Large-Scale Multi-Objective Optimization Problems with Sparse Optimal Solutions via Unsupervised Neural Networks

... Two Neural Networks in MOEA/PSL The proposed MOEA/PSL learns the Pareto optimal subspace by both RBM and DAE, while a single neural network can also be used to achieve this ...

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Dynamics of large scale electrophysiological networks: a technical review

Dynamics of large scale electrophysiological networks: a technical review

... of neural activity? Second, are we assuming a linear relation between the time courses or an approach which can also account for non-linearity? Third, are we interested in directionality of information flow ...

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Simulation Infrastructure for Modeling Large Scale Neural Systems

Simulation Infrastructure for Modeling Large Scale Neural Systems

... Neural systems are highly complex, structured, and can be viewed from many levels of abstraction. For example, the human brain is composed of roughly 100 billion neurons interconnected by 100 trillion synapses. ...

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Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE

Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE

... from large-scale unlabelled ...as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the ...deep neural networks, ...

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Bayesian optimization of large scale biophysical networks

Bayesian optimization of large scale biophysical networks

... Although the nature of these interactions remains to be characterised, this hypothesis is consistent with more functionally-oriented views, in which the brain is seen as a network of spatially segregated units, ...

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LARGE-SCALE MALWARE CLASSIFICATION USING RANDOM PROJECTIONS AND NEURAL NETWORKS

LARGE-SCALE MALWARE CLASSIFICATION USING RANDOM PROJECTIONS AND NEURAL NETWORKS

... ral networks, but also include an additional pre-training stage often used in deep learning architectures which attempts to initialize the hidden layer weights before performing the standard neural network ...

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Large-Scale Image Segmentation with Convolutional Networks

Large-Scale Image Segmentation with Convolutional Networks

... sufficiently large input context patch, around each pixel to be ...convolutional neural network which considers a large input context while limiting the capacity of the ...

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Dynamical Nonlinear Neural Networks with Perturbations Modeling and Global Robust Stability Analysis

Dynamical Nonlinear Neural Networks with Perturbations Modeling and Global Robust Stability Analysis

... that neural networks are complex and large scale nonlinear dynamical ...the neural network field after publication of ...conceptualizing neural networks in terms of an ...

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Robust Large Margin Deep Neural Networks

Robust Large Margin Deep Neural Networks

... training networks with weight decay regularization, which is simply the 1 - or 2 -norm of all the weights in the net- ...that scale exponentially with the network ...

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Learning to Adaptively Scale Recurrent Neural Networks

Learning to Adaptively Scale Recurrent Neural Networks

... their scale variation ...the scale 0 and 1 are more related to noises while the scale 2 and 3 only appear in the region with square form ...the scale 2 is located is harder to identify as its ...

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On Deep Multiscale Recurrent Neural Networks

On Deep Multiscale Recurrent Neural Networks

... deep neural networks is learning a decomposable and hierarchical representation of ...convolutional neural networks can capture dif- ferent levels of spatial ...

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Large-scale Evaluation of Distributed Attack Detection

Large-scale Evaluation of Distributed Attack Detection

... networks does. Such traffic in most cases heavily influences behavior of the observed protocol or system. Thus, ReaSE extends INET by special client and server entities. These entities generate a reasonable mix of ...

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Bi level route guidance method for large scale urban road networks

Bi level route guidance method for large scale urban road networks

... smaller scale of the sub-region, which is inconsistent with the second principle mentioned above; moreover, the number of sub-regions will get too much after this division way, which does not match the third ...

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Statistical inference from large scale genomic data

Statistical inference from large scale genomic data

... This thesis covers a range of topics in the investigation of complex mul- tivariate genomic data. One focus involves using clustering as a method of inference and another is cluster validation to extract meaningful ...

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Link transmission centrality in large-scale social networks

Link transmission centrality in large-scale social networks

... very large-scale network, we propose a heuristic calcula- tion of transmission centrality, which is both computationally efficient and can be easily extended for weighted, directed, or temporal ...

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