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model-based recurrent neural network

Factored Language Model based on Recurrent Neural Network

Factored Language Model based on Recurrent Neural Network

... various neural network language models (NNLMs), recurrent neural network-based lan- guage models (RNNLMs) are very competitive in many ...

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Recurrent Neural Network based Translation Quality Estimation

Recurrent Neural Network based Translation Quality Estimation

... QE model comes from the insufficiency of QE datasets to train the whole QE ...QE model is divided into two parts, and then different training data are used to train each of the separated parts: large-scale ...

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Recurrent Neural Network Based Loanwords Identification in Uyghur

Recurrent Neural Network Based Loanwords Identification in Uyghur

... recursive neural network to predict sentence senti- ...neural network. (Cho et al., 2014a) proposed a RNN encoder-decoder model to learn phrase representations in ...recursive ...

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Remote banking fraud detection framework using sequence learners

Remote banking fraud detection framework using sequence learners

... expert. Based on the proposed framework, we implement Long Short-Term Memory (LSTM) based Recurrent Neural Network model for detecting fraud in remote banking and evaluate its ...

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Emotion recognition from skeletal movements

Emotion recognition from skeletal movements

... new model (i.e., for RNN starting with a network containing 2 layers of 50 recurrent neurons and finishing with 4 layers containing 400 ...of neural network for the above mentioned ...a ...

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INTERACTING THROUGH DISCLOSING: PEER INTERACTION PATTERNS BASED ON 
SELF DISCLOSURE LEVELS VIA FACEBOOK

INTERACTING THROUGH DISCLOSING: PEER INTERACTION PATTERNS BASED ON SELF DISCLOSURE LEVELS VIA FACEBOOK

... DanQ model as a predicting model for the function of DNA sequences uses a combination of convolutional and recurrent neural network as the ...prediction model consists of the ...

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Recurrent Neural Network based Tuple Sequence Model for Machine Translation

Recurrent Neural Network based Tuple Sequence Model for Machine Translation

... translation model (Marino et al., 2006) is a Markov model over phrasal bilingual tuples and can improve the phrase-based translation system (Koehn et ...translation model, Crego and Yvon ...

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Enhancing recurrent neural network-based language models by word tokenization

Enhancing recurrent neural network-based language models by word tokenization

... modified recurrent neural network-based language model for language ...the network input into three ...basic recurrent neural network ...proposed ...

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High Resolution Range Profile Sequence Recognition Based on ARTRBM

High Resolution Range Profile Sequence Recognition Based on ARTRBM

... stochastic neural network model named Attention based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) is proposed for the poor performance of the traditional HRRP recognition ...

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NETWORK INTRUSION DETECTION USING DEEP NEURAL NETWORKS

NETWORK INTRUSION DETECTION USING DEEP NEURAL NETWORKS

... implemented based on the Long Short Term Memory Recurrent Neural ...the network behaviour is normal or affected based on the past ...

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Deep Learning Based Crime Investigation Framework

Deep Learning Based Crime Investigation Framework

... mathematical model that tells us the future data by using past ...Deep Neural Network we can use LSTM model as shown in fig 4 for ...Memory model or simply Recurrent ...

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Implementation of Recurrent Neural Network with Sequence to Sequence Model to Translate Language Based on TensorFlow

Implementation of Recurrent Neural Network with Sequence to Sequence Model to Translate Language Based on TensorFlow

... This model works for the formation of simple feed-forward neural networks, but fails for more advanced models, such as recurrent neural networks, which contain loops [8]; Adverse networks, in ...

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Sequence-to-sequence modeling for graph representation learning

Sequence-to-sequence modeling for graph representation learning

... not based solely on the graph ...Graph Neural Networks (GNNs) and rely on the idea of message propagation around the ...Convolutional Neural Network (DGCNN) (Zhang et al. 2018) is another ...

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Recurrent Neural Network Based Narrowband Channel Prediction

Recurrent Neural Network Based Narrowband Channel Prediction

... alternative, neural networks have also been proposed for the task of channel prediction [7], [8], [9], since they can be trained to learn from the past statistics, which can be exploited for predicting the ...

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Predicting Helpful Posts in Open Ended Discussion Forums: A Neural Architecture

Predicting Helpful Posts in Open Ended Discussion Forums: A Neural Architecture

... factoid based, we find that the threads in a forum are often open-ended ...a recurrent neural network based architecture to model (i) the relevance of a post regarding the ...

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Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model

Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model

... sis model that considers semantic plausi- bility of word sequences by using a re- current neural network language model ...els based on raw word sequences but use a semantically ...

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Joint Language and Translation Modeling with Recurrent Neural Networks

Joint Language and Translation Modeling with Recurrent Neural Networks

... tion model based on a recurrent neural net- work which predicts target words based on an unbounded history of both source and tar- get ...this model result in a vastly larger ...

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Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks

Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks

... Approaches based on RNNs: Recurrent Neural Networks (RNNs) with Long Short-Term Memory Network (LSTM) [13] are able to model the contextual information of the temporal sequences as ...

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Detecting hate speech on Twitter using a convolution-GRU based deep neural network

Detecting hate speech on Twitter using a convolution-GRU based deep neural network

... gies based on Natural Language Processing (NLP) and Machine Learning (ML) ...lutional neural network, gated recurrent unit network) neural network model optimised ...

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ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation

ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation

... In this paper, we aim at the efficient application of Re- current Neural Networks RNN to retrieve contextual infor- mation from images. We propose to extend the ReNet ar- chitecture [45], originally designed for ...

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