Deep Neural Networks (DNNs)
TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding
... SNNs, deep neural networks (DNNs) have been able to perform the state-of-the-art results on many complex tasks such as image recognition (Krizhevsky, Sutskever, and Hinton 2012; Krizhevsky ...
8
Sensitivity Analysis of Deep Neural Networks
... Deep neural networks (DNNs) have exhibited impressive power in image classification and outperformed human de- tection in the ImageNet challenge (Russakovsky et ...state-of-the-art DNNs ...
8
Deep Neural Networks for Recommender Systems
... of Deep Neural Networks (DNN) approaches that can be used for recommender ...forward neural networks and its comparison with traditional ...
5
Completeness Problem of the Deep Neural Networks
... forward networks with enough hidden layers are universal ...the Deep Neural Networks implement an expansion and the expansion is ...a Deep Neural ...the Deep Neural ...
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Modeling Interestingness with Deep Neural Networks
... lated deep neural networks to computer vision (Krizhevshy et ...a deep neural network to map documents to feature vec- tors in a latent semantic ...the deep models used for ...
12
A Representer Theorem for Deep Neural Networks
... Our aim is to determine the optimal activation functions for a deep neural network in a task-dependent fashion. This problem is inherently ill-posed because activations are infinite- dimensional entities ...
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Predicting drug response of tumors from integrated genomic profiles by deep neural networks
... to DNNs. Also, by taking advan- tage of the transferability of neural networks, we used the huge volume of TCGA data to equip our model the ability of capturing representations of mutation and ex- ...
13
Deep Machine Learning In Neural Networks
... encoding with neural networks. There are three datasets are compared in this paper. For the CIFAR-10 experiments the encoder after VGG-style classifier with 11 convolutional layers and 3x3 filters. ACN ...
8
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 ...
13
CTR Prediction with Deep Neural Networks
... Since, marketers lack the technical know-how, choosing a solution wisely could help create value. While choosing solutions, it is important to understand the technical capabilities of the vendor. For some projects, a ...
9
Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning
... on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other ...use deep neural networks (DNNs) to obtain multi-pixel estimates ...
13
Automatic dysfluency detection in dysarthric speech using deep belief networks
... Dysarthria is a speech disorder caused by difficulties in control- ling muscles, such as the tongue and lips, that are needed to produce speech. These differences in motor skills cause speech to be slurred, mumbled, and ...
5
Predicting the daily return direction of the stock market using hybrid machine learning algorithms
... as deep neural networks (DNNs), to perform the ...analyses. DNNs employ various deep learning algorithms based on the combination of network structure, activation function, and ...
20
Private Model Compression via Knowledge Distillation
... powerful deep neural networks (DNNs) on mo- bile ...of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far ...
9
Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the ...Various deep learning architectures such as deep neural ...
5
Deep Learning as a Frontier of Machine Learning: A Review
... Deep neural network is a variant of multilayer feed-forward artificial neural ...the deep neural ...many neural network models and second, the issue of computation ...in ...
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Unified Framework For Deep Learning Based Text Classification
... artificial neural networks, which are inspired by biological brain model made of ...typical deep learning architecture has three components namely input variables, hidden layers and output ...in ...
5
Leveraging big data for fuel oil consumption modelling
... shallow neural networks, deep neural networks, support vector machines, and random forest regressors are presented and implemented, comparing ...
9
A general purpose intelligent surveillance system for mobile devices using deep learning
... The system presented in this paper is only capable of using a pre-trained model for its classification. The truly game- changing capability is to allow a mobile device to learn as it collects samples of images. This is ...
8
Development of a Data-Driven Analysis Framework for Boiling Problems with Multiphase-CFD Solver.
... using deep feedforward neural networks (DFNN). The networks are trained on data extracted from high fidelity pool boiling simulations with interface tracking method ...the networks is ...
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