[PDF] Top 20 Accelerating Neural Transformer via an Average Attention Network
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Accelerating Neural Transformer via an Average Attention Network
... several attention formulations and distinguish local attention from global ...the attention to improve model’s capability in dealing with long-range ...the attention-generated context vectors ... See full document
10
Neural Speech Synthesis with Transformer Network
... end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-the- art performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) ... See full document
8
Survey on Attention Neural Network Models for Natural Language Processing
... [14]Graph neural network applies deep learning on the graphs where the input features vectors for every node is given and hidden representation for each node is ...Graph attention network ... See full document
5
DWT ANN Based Analysis of Inrush and Fault Currents in Power Transformers
... artificial neural networks to discriminate the fault has given a lot of attention ...a neural network, more properly referred to as an 'artificial' neural network (ANN), is ... See full document
7
Attending to Future Tokens for Bidirectional Sequence Generation
... sequence, neural net- work models typically produce one token at a ...the Transformer (Vaswani et al., 2017). The self-attention module of a Transformer network treats a sequence ... See full document
10
Dependency-Based Self-Attention for Transformer NMT
... the Transformer model (Vaswani et ...recurrent neural network (RNN)- based models (Sutskever et ...volutional neural network (CNN)-based models (Gehring et ...garnered attention ... See full document
8
Semantic Relation Classification via Hierarchical Recurrent Neural Network with Attention
... with average pool- ing, max-pooling and neural attention ...Bi-LSTMs via neural attention achieves the best result, which gives an improvement of ...the attention model ... See full document
10
A Cognition Based Attention Model for Sentiment Analysis
... Table 5 shows that among all three single atten- tion models, UPA outperforms both LA and CBA in the first three datasets. This is easier to under- stand as UPA already included LA and it has more explicit information ... See full document
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Artificial Neural Network Based Backup Differential Protection of Generator-Transformer Unit
... Many of the proposed algorithms produced good results in terms of accuracy. A better algorithm can always improve the reliability of the protection scheme. However, use of a backup protection system improves the ... See full document
6
A Dual Attention Hierarchical Recurrent Neural Network for Dialogue Act Classification
... convolutional neural net- work ...recurrent neural network was developed for jointly modelling sequences of words and discourse relations between adjacent sentences (Ji et ... See full document
10
Attention Based Convolutional Neural Network for Semantic Relation Extraction
... Recently, neural network models have been increasingly focused on for their ability to minimize the effort in feature engineering of NLP tasks (Collobert et ...paid attention to feature learning of ... See full document
11
Understanding the nature of oil fluctuations using 1 neutral network moving average: A study on the returns of crude oil futures
... configuration neural network model using January 2, 2004 to December 31, 2011 as the training ...trained neural network model that has the least normalized mean square error (NMSE) between the ... See full document
16
Long Short Term Memory Networks for Machine Reading
... render neural networks more structure aware have seen the incorporation of exter- nal memories in the context of recurrent neural net- works (Weston et ...and attention to empower a recurrent ... See full document
11
Analyzing the Structure of Attention in a Transformer Language Model
... how attention patterns manifest in other types of content, such as dialog scripts or song ...analyze attention patterns in text much longer than a single sentence, especially for new Trans- former variants ... See full document
14
Amrita School of Engineering CSE at SemEval 2019 Task 6: Manipulating Attention with Temporal Convolutional Neural Network for Offense Identification and Classification
... ployed neural network models which manip- ulate attention with Temporal Convolutional Neural Network for the three shared sub- tasks i) ATT-TCN (ATTention based Temporal ... See full document
7
The JHU Machine Translation Systems for WMT 2016
... include: neural probabilistic language models, bilingual neural network language models, morpho- logical segmentation, and the attention- based neural machine translation model as ... See full document
9
Artificial Intelligence Based Fault Diagnosis of Power Transformer-A Probabilistic Neural Network and Interval Type-2 Support Vector Machine Approach
... this network is that it doesn't require any iterative training and thus can learn quite ...the network starts with no nodes and nodes are added one at a time to the ... See full document
12
A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction
... neuro-fuzzy network (CNFN) model is validated by forecasting the daily closing indices of the DJIA, BSE, FTSE, TAIEX, and NASDAQ stock ...functional neural network (RBFNN), that are trained ... See full document
34
Global solar radiation forecasting based on meteorological data using artificial neural network
... Tamer Khatib et al (2012) presented a global solar energy estimation method using artificial neural networks (ANNs). This prediction was based on collected data from 28 sites in Malaysia. The clearness index is ... See full document
5
Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel
... sequences, Transformer is a feed-forward model that concurrently processes the entire se- ...the Transformer is its at- tention mechanism, which is proposed to integrate the dependencies between the ...of ... See full document
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