Top PDF 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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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, ...
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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 ...
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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 ...
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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 ...
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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 ...
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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, ...
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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 ...
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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 ...
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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 ...
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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] ...
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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] ...
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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 ...
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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 ...
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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 ...
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