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[PDF] Top 20 Deep neural networks for natural language processing and its acceleration

Has 10000 "Deep neural networks for natural language processing and its acceleration" found on our website. Below are the top 20 most common "Deep neural networks for natural language processing and its acceleration".

Deep neural networks for natural language processing and its acceleration

Deep neural networks for natural language processing and its acceleration

... Recurrent neural networks (RNNs) have shown tremendous success in modeling sequen- tial data, such as natural language [119, ...dependencies and stimulating research on strategies to ... Voir le document complet

140

Unsupervised post-tuning of deep neural networks

Unsupervised post-tuning of deep neural networks

... ization, natural language processing ...challenge for deep learning classifiers is to move beyond traditional supervised training and exploit the large quantity of unlabeled data ... Voir le document complet

9

Natural image processing and synthesis using deep learning

Natural image processing and synthesis using deep learning

... composed deep feed-forward network, called domain-adversarial neural network (DANN) (il- lustrated by Figure ...layers and loss functions, and can be trained using standard backpropagation ... Voir le document complet

175

Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks

Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks

... EEG and audio are seen as a superposition of different ...another natural way of representing such ...signal and its structure is passed to the neural ...by its magnitude ... Voir le document complet

146

Energy Efficient Techniques using FFT for Deep Convolutional Neural Networks

Energy Efficient Techniques using FFT for Deep Convolutional Neural Networks

... convolutional neural networks (CNNs) has been developed for a wide range of applications such as image recognition, nature language processing, ...of deep CNNs in home and ... Voir le document complet

7

Natural language processing for music information retrieval : deep analysis of lyrics structure and content

Natural language processing for music information retrieval : deep analysis of lyrics structure and content

... precision and recall are close to each other, the overall performance is ...models, and that they do not need to consider the whole vocabulary, just the most offensive ...unigrams and bigrams is ... Voir le document complet

155

Learning Activation Functions in Deep Neural Networks

Learning Activation Functions in Deep Neural Networks

... of deep neural networks (deep learning) achieved considerable success in pattern recognition and text ...studies and practical applications of deep learning on images, ... Voir le document complet

171

Recurrent neural models and related problems in natural language processing

Recurrent neural models and related problems in natural language processing

... training/optimizing deep neu- ral networks (Hinton and Salakhutdinov, 2006; Bengio et ...theoretical and empirical work drew the community’s attention back to the buried treasure of ... Voir le document complet

161

Mean-field Langevin System, Optimal Control and Deep Neural Networks

Mean-field Langevin System, Optimal Control and Deep Neural Networks

... method for the deep neural ...a natural extension to the recent works [16, 23, 24] in which the authors endorse the solvability of the two-layer ...layer) neural networks using ... Voir le document complet

25

Applied natural language processing inspired by fundamental mathematics and physics

Applied natural language processing inspired by fundamental mathematics and physics

... NLP. For that purpose we follow the R-Transformer model of Wang et ...resentations for the multi-head attention are replaced with output representations from a local RNN module, which is an RNN that acts on ... Voir le document complet

45

Compression in Sequence to Sequence Learning for Natural Language Processing

Compression in Sequence to Sequence Learning for Natural Language Processing

... Wu, and Yong Zhao. Binarized Neural Networks on the ImageNet Classification ...Hughes, and Jeffrey Dean. Google’s Neural Machine Translation System : Bridging the Gap between Human ... Voir le document complet

62

Comparing learned representations of deep neural networks

Comparing learned representations of deep neural networks

... images, and have very high adversarial example ...true for the ResNeXt architecture, but not for other ...representations and whether it is a direct result of the architecture design, ... Voir le document complet

64

Multichannel Music Separation with Deep Neural Networks

Multichannel Music Separation with Deep Neural Networks

... DNNs, and the spatial covariance matrices, which are updated iteratively in an EM ...voice and other instruments from a mixture containing multiple musical ...evaluation, and estimating the optimal ... Voir le document complet

6

Compression of Deep Neural Networks for Image Instance Retrieval

Compression of Deep Neural Networks for Image Instance Retrieval

... as networks get larger, it is not feasible to train them on a single ...Large neural networks are trained across multiple machines, and one of the key bottlenecks in training is the ... Voir le document complet

11

Examining citations of natural language processing literature

Examining citations of natural language processing literature

... this and all further analyses, we do not include AA 0 papers published in 2017 or later (to allow for at least ...years for the papers to collect ...box and whisker plots for: all of ... Voir le document complet

12

Clinical event prediction and understanding with deep neural networks

Clinical event prediction and understanding with deep neural networks

... In addition, we compare these representations along with both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five i[r] ... Voir le document complet

56

De-noising and de-blurring of images using deep neural networks

De-noising and de-blurring of images using deep neural networks

... In this paper, we test using a Learning Synthesis Deep Neural Network (LS-DNN) [2] in combination with BM3D [3], an off the shelf de-noising tool, to generate images, att[r] ... Voir le document complet

12

Deep neural networks for automatic classification of anesthetic-induced unconsciousness

Deep neural networks for automatic classification of anesthetic-induced unconsciousness

... In terms of resource utilization, the cNN was also better than the MLP, as the latter had a significantly larger number of parameters to learn (e.g. 46,872,579 in MLP vs 2,921,219 in cNN, for the reference network ... Voir le document complet

11

Entropy and mutual information in models of deep neural networks

Entropy and mutual information in models of deep neural networks

... of deep learning models with a tractable method to compute information- theoretic ...entropies and mutual informations can be derived from heuristic statistical physics methods, under the assumption that ... Voir le document complet

66

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

... in deep learning framework training loops, the empirical loss of an epoch is computed as the averaged loss of each ...approximation for the empirical loss of the final mini-batch ...loss for each ... Voir le document complet

21

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