Deep neural networks
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 2009; ...
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Sentiment classification with deep neural networks
... The three deep neural networks were trained using SGD as the optimization algorithm. The setting of the model training hyper-parameters is listed in Table 4.1. Regarding the regularization technique ...
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A Representer Theorem for Deep Neural Networks
... a deep neural network by adding a corresponding functional regularization to the cost ...for deep neural networks that makes a direct connection with splines and ...existing ...
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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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CTR Prediction with Deep Neural Networks
... ad networks use algorithms, ad effectiveness as measured by CTRs is not well understood by marketing and sales ...with deep neural networks can improve ad ...that 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 ...
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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 ...
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Polymorphic Accelerators for Deep Neural Networks
... Abstract—Deep neural networks (DNNs) come with many forms, such as convolutional neural networks, multilayer perceptron and recurrent neural networks, to meet diverse ...
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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 ...
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Text Classification With Deep Neural Networks
... with Deep Neural Networks to achieve better text classification than the baseline method that does not use representation pre-training (ME-TFIDF) as shown in senti- ment and ADR ...introduced ...
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Automatic Designs in Deep Neural Networks
... of deep neural networks (DNNs) in vision, language, recommendation and many other Machine Learning (ML) fields, lots of efforts are put into designing deep neural network models that ...
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The resurgence of structure in deep neural networks
... invariances, deep neural net‐ works typically ...features. Deep neural networks al‐ low for automatic inference of useful features, purely through observing large quantities of training ...
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Concolic Testing for Deep Neural Networks
... Deep neural networks (DNNs) have been instrumental in solving a range of hard problems in AI, e.g., the ancient game of Go, image classification, and natural language processing. As a result, many ...
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Exploring Strategies for Training Deep Neural Networks
... multi-layer neural networks have many levels of non-linearities allowing them to compactly represent highly non-linear and highly-varying ...such deep networks, since gradient-based ...
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Deep Neural Networks for Coreference Resolution for Polish
... of deep neural networks aimed at the task of coreference resolution for ...best deep neural architecture with the sieve-based approach – the cascade of rule-based coreference resolvers ...
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Distributed deep neural networks
... optimism, deep learning seems on a similar trajectory to machine learning advances of ...adopting deep learning methods to achieve greater level of performance and ...with deep neural ...
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Whetstone Trained Spiking Deep Neural Networks to Spiking Neural Networks
... of neural networks that are currently under focus by the Artificial Intelligence ...community. Deep Neural Networks (DNNs), based on traditional, non- spiking, feed-forward ...
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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 ...
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Semantic Language models with deep neural Networks
... of deep autoencoders that encode the semantic context with a noisy representation, which would not be significantly affected from the noise introduced by the ASR or the semantic ...
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Deep Neural Networks Constrained by Decision Rules
... The important points of the results are threefold. First, RCN+A with RF performed better than the existing algo- rithms that can present decision rules supporting their deci- sions (RF, CART and SBRL). This supports the ...
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