Modules of Recurrent Neural Networks can be used as basic building blocks in models of human word recognition systems , if they exhibit spotting behavior . It is shown that such modules are forced to extract the relevant information from the signal and store it in a time-shift-independent way , if they are trained with a so-called “ brute-force-target-function ” and if the training data offered to the network is not time-aligned . The experimental results in this paper demonstrate that a simple pruning/retraining method effectively improves the generalization performance of recurrent neural networks trained to recognize regular languages . The technique also permits the extraction of symbolic knowledge in the form of deterministic finite-state automata ( DFA ) which are more consistent with the rules to be learned . Our investigation , performed within a detailed standard-based simulated environment , demonstrates competitive performance of DNN-based methods as compared to the conventional ones . In particular , convolutional neural network and recurrent neural network architectures applied in packet detection and CFO estimation , respectively , demonstrated robustness and accuracy that matched and even surpassed the conventional methods . This paper concerns our general recurrent neural network structures for nonlinear blind source separation , especially suited to polynomial mixtures . We here focus on linear-quadratic mixtures . This neural machine translation approach typically consists of two components , the first of which encodes a source sentence and the second decodes to a target sentence . For instance , two recurrent neural networks ( RNN ) were used by and to encode a variable - length source sentence into a fixed - length vector and to decode the vector into a variable - length target sentence . Despite being a quite new approach , neural machine translation has already shown promising results . In contrast , our approach solves target problems in an entirely feed - forward manner with no model updates required , making it more convenient for low - latency or low - power applications . RNN Memory Based Another category of approaches leverage recurrent neural networks with memories . Here the idea is typically that an RNN iterates over an examples of given problem and accumulates the knowledge required to solve that problem in its hidden activations , or external memory . Essentially the same model has been used by to successfully learn to generate image captions . Finally , already in 1990 experimented with applying recurrent neural networks to the problem of syntactic parsing . section : Conclusions While we have gone to significant lengths to expand our datasets ( c.f . Section [ reference ] ) , the recurrent networks we use are still adept at fitting the training data . In order to reduce variance further , we use several techniques . However there are three key differences . Firstly , both the encoder and decoder are recurrent networks in DRAW , so that a sequence of code samples is exchanged between them ; moreover the encoder is privy to the decoder ’s previous outputs , allowing it to tailor the codes it sends according to the decoder ’s behaviour so far . Neural machine translation ( NMT ) is a popular topic in Natural Language Processing which uses deep neural networks ( DNNs ) for translation from source to targeted languages . With the emerging technologies , such as bidirectional Gated Recurrent Units ( GRU ) , attention mechanisms , and beam-search algorithms , NMT can deliver improved translation quality compared to the conventional statistics-based methods , especially for translating long sentences . However , higher translation quality means more complicated models , higher computation/memory demands , and longer translation time , which causes difficulties for practical use . To enhance the accuracy among the temporal fluctuations of RSSI , a weighted average filter is proposed for both input RSSI data and sequential output locations . The results using different types of RNN , including vanilla RNN , long short-term memory ( LSTM ) , gated recurrent unit ( GRU ) , bidirectional RNN ( BiRNN ) , bidirectional LSTM ( BiLSTM ) , and bidirectional GRU ( BiGRU ) are presented . On-site experiments demonstrate that the proposed structure achieves an average localization error of 0.75 m with 80 % of the errors under one meter , which outperforms $ {K}$ -nearest neighbors algorithms and probabilistic algorithms by approximately 30 % under the same test environment . To accelerate convergence , two artificial features were added as input . The gated recurrent unit ( GRU ) network model achieves accurate rolling prediction from 15 minutes to 6 hours . Finally , comparing it with the long short-term memory ( LSTM ) network and the back-forward propagation network ( BPN ) , it was confirmed that the GRU network with a timestep of 1.5 hours is the best model for the long-term ( more than 3 hours ) traffic flow rolling prediction , while GRU with a timestep of 45 minutes has the best result for short-term rolling prediction . In this paper , we propose a healthcare expenditure prediction system for the crowd with co-existing medical conditions . We analyze the diagnoses and payment records , and leverage a gated recurrent unit ( GRU ) network model to learn the contextual features which would contribute to the prediction . Evaluations over diagnoses from 37,734 patients and the financial report from a real-world medical institution indicate that our system can provide dependable prediction of healthcare expenditure and it is practical to use our system to assist the medical budget decision . The GRU contains few gates ( reset and update gates ) but is based on gates grouping without taking into account the latent relations between short and long-term dependencies . The GRU term dependencies management through hidden units is therefore similar for all hidden neurons . Moreover , the learning of gated RNNs requires a large amount of data and , despite the advent of GPU cards that allow the model to be learned quicker , the processing time is quite costly . Despite the very different manner in which the two models consider time , their performance on eleven data sets we examined is essentially identical . Our surprising results point both to the robustness of GRU and LSTM architectures for handling continuous time , and to the potency of incorporating continuous dynamics into neural architectures . Deep neural networks are computational models abstracted from human brain cognitive processes , closer to humans . In this paper , we use GRU deep neural network to study the generative dialogue , and implement a seq2seq model manmachine conversation system . Experiments show that the confidence of the system can reach the range of human acceptance . The training set comprised data from 69 of the patients , while the test set contained data from the remaining 17 patients . RESULTS The accuracy of the best-performing model was 99.0 % , and the 1D CNN and GRU models had 99.0 % recall rates . CONCLUSIONS The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events , and they could distinguish between apnea and hypopnea events using an ECG signal . And then , we treat each k-mer as a word and pre-trained word representation model though word2vec algorithm . Thirdly , we construct a deep bidirectional GRU model for feature learning and classification . Experimental results have shown that our method has better performance compared with some state-of-the-art methods . This not only deals with the issue of mode mixing effectively , but also makes the reconstruction error of data close to zero . Multi-layer GRU has an excellent ability of nonlinear data-fitting . The experimental results of real WTI crude oil dataset show that the proposed approach perform better in crude oil prices forecasts than some state-of-the-art models . Intelligent Transportation System ( ITS ) is the fundamental requirement to an intelligent transport system . The proposed hybrid model Stacked Bidirectional LSTM and Attention-based GRU ( SBAG ) is used for predicting the large scale traffic speed . To capture bidirectional temporal dependencies and spatial features , BDLSTM and attention-based GRU are exploited . The paper evaluates three variants of the Gated Recurrent Unit ( GRU ) in recurrent neural networks ( RNNs ) by retaining the structure and systematically reducing parameters in the update and reset gates . We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense . In this comparative study , we simply refer to the three variants as , respectively , GRU1 , GRU2 , and GRU3 RNNs . ( [ reference ] ) . It is worth noticing that most of the RNN variants , such as the gated recurrent unit ( GRU ) and soft attention mechanism , can be used as a generator in SeqGAN . subsection : The Discriminative Model for Sequences Every timestep of the sequence is a feature vector . The second part is a Shorten RNN with GRU ( St - GRU ) . The structure of St - GRU is shown in Fig . For Spanish and Dutch , we use the 64 - dimensional Polyglot embeddings al2013polyglot . We set the hidden state dimensions to be 300 for the word - level GRU . The initial learning rate for AdaGrad is fixed at 0.01 . We used the matrix trained by word2vec to initialize the embedding layer , and the embedding size is 256 . We adopted GRU as the Encoder , and each GRU Cell has 1 , 024 hidden states . The accumulated MLP has 60 hidden units . The main purpose of this module is to ensure that the model is aware of the speaker of each utterance and handle it accordingly . subsubsection : Speaker Update ( Speaker GRU ) : Speaker usually frames the response based on the context , which is the preceding utterances in the conversation . subsubsection : Dependency on preceding utterances ( DialogueRNN ) One of the crucial components of DialogueRNN is its attention module over the outputs of global GRU ( ) . Figure [ reference ] shows the attention vector ( eq:7 ) over the history of a given test utterance compared with the attention vector from the CMN model . On WMT'16 English - Romanian translation we compare to [ reference ] , the winning single system entry for this language pair . Their model consists of a bi - directional GRU encoder , a GRU decoder and MLP - based attention . Table 2 : The word vector for the - th word is . A word sequence is processed using an RNN encoder with gated recurrent units ( GRU ) , which was proved to be effective in RC and neural machine translation tasks . For each position , GRU computes with input and previous state , as : where , , and are d - dimensional hidden state , reset gate , and update gate , respectively ; , and , are the parameters of the GRU ; is the sigmoid function , and denotes element - wise production . In , the authors generate image regions with object proposals and then select the regions relevant to the question and answer choice . Xiong augments dynamic memory network with a new input fusion module and retrieves an answer from an attention based GRU . In concurrent work , collected ‘ human attention maps ’ that are used to evaluate the attention maps generated by attention models for VQA . We use a GRU model with 256 - dimensional hidden units as the internal state controller . The initial state of the GRU controller is the last - word representation of the query bidirectional - GRU encoder . Termination Module : —In the digital world , inpainting is the algorithm used to replace or reconstruct lost , corrupted , or deteriorated parts of image data . Of the various proposed inpainting methods , convolutional methods are the simplest and most efficient . In this paper , an enhanced inpainting model based on convolution theorem is proposed for digital images that preserves the edge and effectively estimates the lost or damaged parts of an image . In this paper we propose using different deep-learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market , by making predictions 12 and 36 hours in advance . We develop and compare various estimators based on feedforward , convolutional , and recurrent neural networks . These estimators were trained and validated with data from a wind farm located on the island of Tenerife . Convolutional neural networks ( CNNs ) , in which several convolutional layers extract feature patterns from an input image , are one of the most popular network architectures used for image classification . The convolutional computation , however , requires a high computational cost , resulting in an increased power consumption and processing time . In this paper , we propose a novel algorithm that substitutes a single layer for a pair formed by a convolutional layer and the following average-pooling layer . This makes it more efficient and appropriate for embedded applications . Second , since the flow at each pyramid level is small ( pixel ) , a convolutional approach applied to pairs of warped images is appropriate . Third , unlike FlowNet , the learned convolution filters appear similar to classical spatio - temporal filters , giving insight into the method and how to improve it . In the illustrative experiments , we use a fully connected network with 3 ReLU layers of size 100 . For CelebA , we adapt the base convolutional architecture described in Finn et al . [ reference ] which we refer the readers to for more detail . In Appendix § C , we plot attention scores for a deep decoder and show that at different layers , different portions of the source are attended to . Our convolutional architecture also allows to batch the attention computation across all elements of a sequence compared to RNNs ( Figure 1 , middle ) . We batch the computations of each decoder layer individually . What the two operations have in common is that they both ignore the ground state : they replace the ground state with a zero vector to simplify the convolution operations . The difference between both operations is in how they handle active sites : instead of automatically making a site active if any of the inputs to its receptive field is active ( thereby dilating the set of active sites ) , our most efficient convolutional operation only considers the central input . As a result , the output set of active sites exactly mirrors that of the input set . The Rectified Linear Unit ( ReLU ) is not added after this convolution . Similar to conventional multi - class recognition approaches , we use the cross - entropy loss for identity prediction , which is Here denotes the convolutional operation . is a tensor , is the target class and denotes the parameters of the added convolutional layer . c × f 1 × f 1 , where c is the number of channels in the input image , f 1 is the spatial size of a filter . Intuitively , W 1 applies n 1 convolutions on the image , and each convolution has [ reference ] . Bicubic interpolation is also a convolutional operation , so it can be formulated as a convolutional layer . However , the output size of this layer is larger than the input size , so there is a fractional stride . Training is based on a two phase procedure , which we ’ ve found allows us to train CNNs efficiently when the distribution of labels is unbalanced . Thanks to the convolutional nature of the models and by using an efficient GPU implementation , the resulting segmentation system is very fast . The time needed to segment an entire brain with any of the these CNN architectures varies between seconds and minutes , making them practical segmentation methods . ( [ reference ] ) , theoretically allows to deal with a scaling factor in the range and rotations approximately in the range . Note also that DeepMatching is translation - invariant by construction , thanks to the convolutional nature of the processing . Given a patch of size located at level , Eq . Since the second operation extracts features by considering only non overlapping volume patches , the size of the resulting feature maps is halved . This strategy serves a similar purpose as pooling layers that , motivated by and other works discouraging the use of max - pooling operations in CNNs , have been replaced in our approach by convolutional ones . Moreover , since the number of feature channels doubles at each stage of the compression path of the V - Net , and due to the formulation of the model as a residual network , we resort to these convolution operations to double the number of feature maps as we reduce their resolution . Dense spatial score evaluation is instrumental in the success of our dense CNN feature extractor . As a first step to implement this , we convert the fully - connected layers of VGG - 16 into convolutional ones and run the network in a convolutional fashion on the image at its original resolution . However this is not enough as it yields very sparsely computed detection scores ( with a stride of 32 pixels ) . We add here that two other architectures , DeconvNet and U - Net share a similar architecture to SegNet but with some differences . DeconvNet has a much larger parameterization , needs more computational resources and is harder to train end - to - end ( Table [ reference ] ) , primarily due to the use of fully connected layers ( albeit in a convolutional manner ) We report several comparisons with DeconvNet later in the paper Sec . The goal of this paper is to conduct the study on the text classification for financial news based on partial information . By a fact that an indispensable step for the efficient use of topic information embedded in financial news is the text classification , a new neural network called “ All Dataset based on CharCNN ( Character Convolutional Neural Networks ) and GRU ( Gated Recurrent Unit ) ” ( in short , AD-CharCGNN ) which extracts a part of the financial article and incorporates both time domain and spatial domain to classify financial texts is proposed . In the study of this paper , we first build a character level vocabulary by reading all characters of the financial dataset , part of each financial text which will be classified is mapped to a high-dimensional spatial vector based on the vocabulary . Exchange rates Euro against US dollar , US dollar against Japanese yen and British Pound Sterling against US dollar are researched in this paper . Our experimental results show that , when compared with Artificial Neural Network , Support Vector Regression and Gated Recurrent Unit , the proposed model can effectively improve the accuracy of long-term forecasting . In this work , we proposed a probability matrix factorization method which combined deep textual features to predict metabolite-disease associations . The deep neural network combining convolutional neural network and gated recurrent unit network is used to extract the corresponding features from text annotations of metabolites and diseases . Then , associations between metabolites and diseases are predicted through the matrix factorization based on these textual features . Using these , we compare their predictive performances on data obtained during a human-robot interaction study . We find that our long short-term memory based model outperforms a gated recurrent unit based model and a feed-forward model . Further , we demonstrate how the created model can be used to create customized comfort zones that can help create a personalized experience for individual users . More importantly , we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles . Since our approach is generic , we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture , where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets . In addition , the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets . To achieve our goal , we created a dataset including 6 hand gestures recorded from 35 intact people by the usage of the Myo Armband device , which has 8 non-intrusive sEMG sensors . Using raw signals extracted from Myo armband , we have been able to train a gated recurrent unit-based network to perform gesture classification . Afterwards , we integrated our system with a live hand gesture recognition application , transmitting the commands to the robot for implementing a live teleoperation method . However , TMS-EEG poses a number of technical challenges , foremost of which is removal of stimulation-induced artifacts that are several orders of magnitude larger than the neural signals of interest and typically obscure critical early neural responses to the stimulation . Here we describe a non-linear non-causal neural network predictor , built using a Gated Recurrent Unit architecture , and demonstrate its use to remove TMS artifacts from EEG recorded on a phantom , as well as real EEG synthetically contaminated by artifacts from the phantom experiment . Our results indicate that this artifact removal algorithm may decontaminate EEG signals as early as 6ms following stimulation . Aiming at this problem , this paper sets up a gated recurrent unit based frequency-dependent hysteresis model and then proposes an end-to-end compensation method to correct image distortion . To be specific , a gated recurrent unit layer is designed to accurately describe the nonlinearity and memory of hysteresis , based on which , a modified back propagation neural network is constructed by introducing the frequency of input voltage to simulate the frequency-dependence characteristic , finally yielding a very accurate hysteresis model with strong generalization ability . Based on the constructed model , a novel piecewise Hermitian interpolation method is then proposed to deal with the uncompensated AFM images , obtained in both forward and backward scanning directions , so as to implement end-to-end compensation for hysteresis to generate a high-quality image . This helps the network to precisely and efficiently learn highly discriminative features for speech activities . What ’s more , we implement a recurrent neural network with the gated recurrent unit scheme to make use of the sequential information from video to produce the final decision . To give a comprehensive evaluation of the proposed method , we collect a large-scale dataset from unconstrained speech activities , which consists of a large number of speech/non-speech video sequences under various kinds of degradation . The appropriate network orthogonal initialization parameters further reduce the network training time and enhance the robustness of the acoustic model . Experimental results on the TIMIT and LibriSpeech datasets show that the improved gating recurrent unit model has a 2.8 % absolute phoneme error rate reduction compared to the baseline system , compared to the standard long short-term memory model , the average iteration period of neural network training is reduced by 16.6 % , which improves both recognition performance and computational efficiency . Using the terminology from , we call a glimpse . The step is completed by computing a new generator state : Long short - term memory units ( LSTM , ) and gated recurrent units ( GRU , ) are typically used as a recurrent activation , to which we refer as a recurrency . The process is graphically illustrated in Fig . [ ] Seo2016BidirectionalAF . It adds a self - attention layer after the bidirectional attention component , simplifies some of the pooling operations and substitutes the LSTMs for gated recurrent units [ GRUs ; ] [ ] GRU : Cho2014 . The input includes the source sentence and the fact descriptions . For each sequence , we employ the bidirectional Gated Recurrent Unit ( BiGRU ) encoder , to construct its semantic representation . Take the sentence as an example . Along with their intuitive design , RNNs have shown outstanding performance across various NLP tasks e.g. language modeling , machine translation , text classification , and parsing . Among several variants of the original RNN , gated recurrent architectures such as long short - term memory ( LSTM ) and gated recurrent unit ( GRU ) have been accepted as de - facto standard choices for RNNs due to their capability of addressing the vanishing and exploding gradient problem and considering long - term dependencies . Gated RNNs achieve these properties by introducing additional gating units that learn to control the amount of information to be transferred or forgotten , and are proven to work well without relying on complex optimization algorithms or careful initialization . The first row reports the results achieved with a simple RNN with ReLU activations ( no gating mechanisms are used here ) . Although this architecture has recently shown promising results in some machine learning tasks , our results confirm that gated recurrent networks ( rows 2 - 5 ) outperform traditional RNNs . We also observe that GRUs tend to slightly outperform the LSTM model . Sentence encoding models ( their main architecture is shown in Figure [ reference ] .a ) independently encode a pair of sentences , a premise and a hypothesis using pre - trained word embedding vectors , then learn semantic relation between two sentences with a multi - layer perceptron ( MLP ) . In these models , LSTM ( Long Short - Term Memory networks ) , its variants GRU ( Gated Recurrent Units ) and Bi - LSTM , are usually utilized to encode the sentences since they were capable of learning long - term dependencies inside sentences . For example , Conneau et al . proposed a generic NLI training scheme and compared several sentence encoding architectures : LSTM or GRU , Bi - LSTM with mean / max pooling , self - attention network and hierarchical convolutional networks . To this end we propose a deep learning approach to avoid heuristics and strong assumptions on the reflectance prior . We compare two network architectures : one classic ‘ U ’ shaped Convolutional Neural Network ( CNN ) and a Recurrent Neural Network ( RNN ) composed of Convolutional Gated Recurrent Units ( CGRU ) . We train our networks on a new dataset specifically designed for the task of intrinsic decomposition from sequences . DCN-SR applies a co-attention network to capture the dynamic interactions between the user 's long- and short-term interaction behavior and generates co-dependent representations of the user 's long- and short-term interests . For modeling a user 's short-term interaction behavior , we design a CGRU network to take actions like " click ' ' , " collect ' ' and " buy ' ' into account . Experiments on e-commerce datasets show significant improvements of DCN-SR over state-of-the-art session-based recommendation methods , with improvements of up to 2.58 % on the Tmall dataset and 3.08 % on the Tianchi dataset in terms of Recall@10 . We train the model using a dataset of 4,702 amateur karaoke performances selected for good intonation . We present a Convolutional Gated Recurrent Unit ( CGRU ) model to accomplish this task . This method can be extended into unsupervised pitch correction of a vocal performance , popularly referred to as autotuning . We introduce the pointer sentinel mixture architecture for neural sequence models which has the ability to either reproduce a word from the recent context or produce a word from a standard softmax classifier . Our pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank ( 70.9 perplexity ) while using far fewer parameters than a standard softmax LSTM . In order to evaluate how well language models can exploit longer contexts and deal with more realistic vocabularies and larger corpora we also introduce the freely available WikiText corpus .
Modules of Recurrent Neural Networks can be used as basic building blocks in models of human word recognition systems , if they exhibit spotting behavior . It is shown that such modules are forced to extract the relevant information from the signal and store it in a time-shift-independent way , if they are trained with a so-called “ brute-force-target-function ” and if the training data offered to the network is not time-aligned .
The experimental results in this paper demonstrate that a simple pruning/retraining method effectively improves the generalization performance of recurrent neural networks trained to recognize regular languages . The technique also permits the extraction of symbolic knowledge in the form of deterministic finite-state automata ( DFA ) which are more consistent with the rules to be learned .
Our investigation , performed within a detailed standard-based simulated environment , demonstrates competitive performance of DNN-based methods as compared to the conventional ones . In particular , convolutional neural network and recurrent neural network architectures applied in packet detection and CFO estimation , respectively , demonstrated robustness and accuracy that matched and even surpassed the conventional methods .
This paper concerns our general recurrent neural network structures for nonlinear blind source separation , especially suited to polynomial mixtures . We here focus on linear-quadratic mixtures .
This neural machine translation approach typically consists of two components , the first of which encodes a source sentence and the second decodes to a target sentence . For instance , two recurrent neural networks ( RNN ) were used by and to encode a variable - length source sentence into a fixed - length vector and to decode the vector into a variable - length target sentence . Despite being a quite new approach , neural machine translation has already shown promising results .
In contrast , our approach solves target problems in an entirely feed - forward manner with no model updates required , making it more convenient for low - latency or low - power applications . RNN Memory Based Another category of approaches leverage recurrent neural networks with memories . Here the idea is typically that an RNN iterates over an examples of given problem and accumulates the knowledge required to solve that problem in its hidden activations , or external memory .
Essentially the same model has been used by to successfully learn to generate image captions . Finally , already in 1990 experimented with applying recurrent neural networks to the problem of syntactic parsing . section : Conclusions
While we have gone to significant lengths to expand our datasets ( c.f . Section [ reference ] ) , the recurrent networks we use are still adept at fitting the training data . In order to reduce variance further , we use several techniques .
However there are three key differences . Firstly , both the encoder and decoder are recurrent networks in DRAW , so that a sequence of code samples is exchanged between them ; moreover the encoder is privy to the decoder ’s previous outputs , allowing it to tailor the codes it sends according to the decoder ’s behaviour so far .
Neural machine translation ( NMT ) is a popular topic in Natural Language Processing which uses deep neural networks ( DNNs ) for translation from source to targeted languages . With the emerging technologies , such as bidirectional Gated Recurrent Units ( GRU ) , attention mechanisms , and beam-search algorithms , NMT can deliver improved translation quality compared to the conventional statistics-based methods , especially for translating long sentences . However , higher translation quality means more complicated models , higher computation/memory demands , and longer translation time , which causes difficulties for practical use .
To enhance the accuracy among the temporal fluctuations of RSSI , a weighted average filter is proposed for both input RSSI data and sequential output locations . The results using different types of RNN , including vanilla RNN , long short-term memory ( LSTM ) , gated recurrent unit ( GRU ) , bidirectional RNN ( BiRNN ) , bidirectional LSTM ( BiLSTM ) , and bidirectional GRU ( BiGRU ) are presented . On-site experiments demonstrate that the proposed structure achieves an average localization error of 0.75 m with 80 % of the errors under one meter , which outperforms $ {K}$ -nearest neighbors algorithms and probabilistic algorithms by approximately 30 % under the same test environment .
To accelerate convergence , two artificial features were added as input . The gated recurrent unit ( GRU ) network model achieves accurate rolling prediction from 15 minutes to 6 hours . Finally , comparing it with the long short-term memory ( LSTM ) network and the back-forward propagation network ( BPN ) , it was confirmed that the GRU network with a timestep of 1.5 hours is the best model for the long-term ( more than 3 hours ) traffic flow rolling prediction , while GRU with a timestep of 45 minutes has the best result for short-term rolling prediction .
In this paper , we propose a healthcare expenditure prediction system for the crowd with co-existing medical conditions . We analyze the diagnoses and payment records , and leverage a gated recurrent unit ( GRU ) network model to learn the contextual features which would contribute to the prediction . Evaluations over diagnoses from 37,734 patients and the financial report from a real-world medical institution indicate that our system can provide dependable prediction of healthcare expenditure and it is practical to use our system to assist the medical budget decision .
The GRU contains few gates ( reset and update gates ) but is based on gates grouping without taking into account the latent relations between short and long-term dependencies . The GRU term dependencies management through hidden units is therefore similar for all hidden neurons . Moreover , the learning of gated RNNs requires a large amount of data and , despite the advent of GPU cards that allow the model to be learned quicker , the processing time is quite costly .
Despite the very different manner in which the two models consider time , their performance on eleven data sets we examined is essentially identical . Our surprising results point both to the robustness of GRU and LSTM architectures for handling continuous time , and to the potency of incorporating continuous dynamics into neural architectures .
Deep neural networks are computational models abstracted from human brain cognitive processes , closer to humans . In this paper , we use GRU deep neural network to study the generative dialogue , and implement a seq2seq model manmachine conversation system . Experiments show that the confidence of the system can reach the range of human acceptance .
The training set comprised data from 69 of the patients , while the test set contained data from the remaining 17 patients . RESULTS The accuracy of the best-performing model was 99.0 % , and the 1D CNN and GRU models had 99.0 % recall rates . CONCLUSIONS The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events , and they could distinguish between apnea and hypopnea events using an ECG signal .
And then , we treat each k-mer as a word and pre-trained word representation model though word2vec algorithm . Thirdly , we construct a deep bidirectional GRU model for feature learning and classification . Experimental results have shown that our method has better performance compared with some state-of-the-art methods .
This not only deals with the issue of mode mixing effectively , but also makes the reconstruction error of data close to zero . Multi-layer GRU has an excellent ability of nonlinear data-fitting . The experimental results of real WTI crude oil dataset show that the proposed approach perform better in crude oil prices forecasts than some state-of-the-art models .
Intelligent Transportation System ( ITS ) is the fundamental requirement to an intelligent transport system . The proposed hybrid model Stacked Bidirectional LSTM and Attention-based GRU ( SBAG ) is used for predicting the large scale traffic speed . To capture bidirectional temporal dependencies and spatial features , BDLSTM and attention-based GRU are exploited .
The paper evaluates three variants of the Gated Recurrent Unit ( GRU ) in recurrent neural networks ( RNNs ) by retaining the structure and systematically reducing parameters in the update and reset gates . We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense . In this comparative study , we simply refer to the three variants as , respectively , GRU1 , GRU2 , and GRU3 RNNs .
( [ reference ] ) . It is worth noticing that most of the RNN variants , such as the gated recurrent unit ( GRU ) and soft attention mechanism , can be used as a generator in SeqGAN . subsection : The Discriminative Model for Sequences
Every timestep of the sequence is a feature vector . The second part is a Shorten RNN with GRU ( St - GRU ) . The structure of St - GRU is shown in Fig .
For Spanish and Dutch , we use the 64 - dimensional Polyglot embeddings al2013polyglot . We set the hidden state dimensions to be 300 for the word - level GRU . The initial learning rate for AdaGrad is fixed at 0.01 .
We used the matrix trained by word2vec to initialize the embedding layer , and the embedding size is 256 . We adopted GRU as the Encoder , and each GRU Cell has 1 , 024 hidden states . The accumulated MLP has 60 hidden units .
The main purpose of this module is to ensure that the model is aware of the speaker of each utterance and handle it accordingly . subsubsection : Speaker Update ( Speaker GRU ) : Speaker usually frames the response based on the context , which is the preceding utterances in the conversation .
subsubsection : Dependency on preceding utterances ( DialogueRNN ) One of the crucial components of DialogueRNN is its attention module over the outputs of global GRU ( ) . Figure [ reference ] shows the attention vector ( eq:7 ) over the history of a given test utterance compared with the attention vector from the CMN model .
On WMT'16 English - Romanian translation we compare to [ reference ] , the winning single system entry for this language pair . Their model consists of a bi - directional GRU encoder , a GRU decoder and MLP - based attention . Table 2 :
The word vector for the - th word is . A word sequence is processed using an RNN encoder with gated recurrent units ( GRU ) , which was proved to be effective in RC and neural machine translation tasks . For each position , GRU computes with input and previous state , as : where , , and are d - dimensional hidden state , reset gate , and update gate , respectively ; , and , are the parameters of the GRU ; is the sigmoid function , and denotes element - wise production .
In , the authors generate image regions with object proposals and then select the regions relevant to the question and answer choice . Xiong augments dynamic memory network with a new input fusion module and retrieves an answer from an attention based GRU . In concurrent work , collected ‘ human attention maps ’ that are used to evaluate the attention maps generated by attention models for VQA .
We use a GRU model with 256 - dimensional hidden units as the internal state controller . The initial state of the GRU controller is the last - word representation of the query bidirectional - GRU encoder . Termination Module :
—In the digital world , inpainting is the algorithm used to replace or reconstruct lost , corrupted , or deteriorated parts of image data . Of the various proposed inpainting methods , convolutional methods are the simplest and most efficient . In this paper , an enhanced inpainting model based on convolution theorem is proposed for digital images that preserves the edge and effectively estimates the lost or damaged parts of an image .
In this paper we propose using different deep-learning techniques and architectures to solve the problem of predicting wind generation in order to participate in the daily market , by making predictions 12 and 36 hours in advance . We develop and compare various estimators based on feedforward , convolutional , and recurrent neural networks . These estimators were trained and validated with data from a wind farm located on the island of Tenerife .
Convolutional neural networks ( CNNs ) , in which several convolutional layers extract feature patterns from an input image , are one of the most popular network architectures used for image classification . The convolutional computation , however , requires a high computational cost , resulting in an increased power consumption and processing time . In this paper , we propose a novel algorithm that substitutes a single layer for a pair formed by a convolutional layer and the following average-pooling layer .
This makes it more efficient and appropriate for embedded applications . Second , since the flow at each pyramid level is small ( pixel ) , a convolutional approach applied to pairs of warped images is appropriate . Third , unlike FlowNet , the learned convolution filters appear similar to classical spatio - temporal filters , giving insight into the method and how to improve it .
In the illustrative experiments , we use a fully connected network with 3 ReLU layers of size 100 . For CelebA , we adapt the base convolutional architecture described in Finn et al . [ reference ] which we refer the readers to for more detail .
In Appendix § C , we plot attention scores for a deep decoder and show that at different layers , different portions of the source are attended to . Our convolutional architecture also allows to batch the attention computation across all elements of a sequence compared to RNNs ( Figure 1 , middle ) . We batch the computations of each decoder layer individually .
What the two operations have in common is that they both ignore the ground state : they replace the ground state with a zero vector to simplify the convolution operations . The difference between both operations is in how they handle active sites : instead of automatically making a site active if any of the inputs to its receptive field is active ( thereby dilating the set of active sites ) , our most efficient convolutional operation only considers the central input . As a result , the output set of active sites exactly mirrors that of the input set .
The Rectified Linear Unit ( ReLU ) is not added after this convolution . Similar to conventional multi - class recognition approaches , we use the cross - entropy loss for identity prediction , which is Here denotes the convolutional operation . is a tensor , is the target class and denotes the parameters of the added convolutional layer .
c × f 1 × f 1 , where c is the number of channels in the input image , f 1 is the spatial size of a filter . Intuitively , W 1 applies n 1 convolutions on the image , and each convolution has [ reference ] . Bicubic interpolation is also a convolutional operation , so it can be formulated as a convolutional layer . However , the output size of this layer is larger than the input size , so there is a fractional stride .
Training is based on a two phase procedure , which we ’ ve found allows us to train CNNs efficiently when the distribution of labels is unbalanced . Thanks to the convolutional nature of the models and by using an efficient GPU implementation , the resulting segmentation system is very fast . The time needed to segment an entire brain with any of the these CNN architectures varies between seconds and minutes , making them practical segmentation methods .
( [ reference ] ) , theoretically allows to deal with a scaling factor in the range and rotations approximately in the range . Note also that DeepMatching is translation - invariant by construction , thanks to the convolutional nature of the processing . Given a patch of size located at level , Eq .
Since the second operation extracts features by considering only non overlapping volume patches , the size of the resulting feature maps is halved . This strategy serves a similar purpose as pooling layers that , motivated by and other works discouraging the use of max - pooling operations in CNNs , have been replaced in our approach by convolutional ones . Moreover , since the number of feature channels doubles at each stage of the compression path of the V - Net , and due to the formulation of the model as a residual network , we resort to these convolution operations to double the number of feature maps as we reduce their resolution .
Dense spatial score evaluation is instrumental in the success of our dense CNN feature extractor . As a first step to implement this , we convert the fully - connected layers of VGG - 16 into convolutional ones and run the network in a convolutional fashion on the image at its original resolution . However this is not enough as it yields very sparsely computed detection scores ( with a stride of 32 pixels ) .
We add here that two other architectures , DeconvNet and U - Net share a similar architecture to SegNet but with some differences . DeconvNet has a much larger parameterization , needs more computational resources and is harder to train end - to - end ( Table [ reference ] ) , primarily due to the use of fully connected layers ( albeit in a convolutional manner ) We report several comparisons with DeconvNet later in the paper Sec .
The goal of this paper is to conduct the study on the text classification for financial news based on partial information . By a fact that an indispensable step for the efficient use of topic information embedded in financial news is the text classification , a new neural network called “ All Dataset based on CharCNN ( Character Convolutional Neural Networks ) and GRU ( Gated Recurrent Unit ) ” ( in short , AD-CharCGNN ) which extracts a part of the financial article and incorporates both time domain and spatial domain to classify financial texts is proposed . In the study of this paper , we first build a character level vocabulary by reading all characters of the financial dataset , part of each financial text which will be classified is mapped to a high-dimensional spatial vector based on the vocabulary .
Exchange rates Euro against US dollar , US dollar against Japanese yen and British Pound Sterling against US dollar are researched in this paper . Our experimental results show that , when compared with Artificial Neural Network , Support Vector Regression and Gated Recurrent Unit , the proposed model can effectively improve the accuracy of long-term forecasting .
In this work , we proposed a probability matrix factorization method which combined deep textual features to predict metabolite-disease associations . The deep neural network combining convolutional neural network and gated recurrent unit network is used to extract the corresponding features from text annotations of metabolites and diseases . Then , associations between metabolites and diseases are predicted through the matrix factorization based on these textual features .
Using these , we compare their predictive performances on data obtained during a human-robot interaction study . We find that our long short-term memory based model outperforms a gated recurrent unit based model and a feed-forward model . Further , we demonstrate how the created model can be used to create customized comfort zones that can help create a personalized experience for individual users .
More importantly , we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles . Since our approach is generic , we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture , where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets . In addition , the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets .
To achieve our goal , we created a dataset including 6 hand gestures recorded from 35 intact people by the usage of the Myo Armband device , which has 8 non-intrusive sEMG sensors . Using raw signals extracted from Myo armband , we have been able to train a gated recurrent unit-based network to perform gesture classification . Afterwards , we integrated our system with a live hand gesture recognition application , transmitting the commands to the robot for implementing a live teleoperation method .
However , TMS-EEG poses a number of technical challenges , foremost of which is removal of stimulation-induced artifacts that are several orders of magnitude larger than the neural signals of interest and typically obscure critical early neural responses to the stimulation . Here we describe a non-linear non-causal neural network predictor , built using a Gated Recurrent Unit architecture , and demonstrate its use to remove TMS artifacts from EEG recorded on a phantom , as well as real EEG synthetically contaminated by artifacts from the phantom experiment . Our results indicate that this artifact removal algorithm may decontaminate EEG signals as early as 6ms following stimulation .
Aiming at this problem , this paper sets up a gated recurrent unit based frequency-dependent hysteresis model and then proposes an end-to-end compensation method to correct image distortion . To be specific , a gated recurrent unit layer is designed to accurately describe the nonlinearity and memory of hysteresis , based on which , a modified back propagation neural network is constructed by introducing the frequency of input voltage to simulate the frequency-dependence characteristic , finally yielding a very accurate hysteresis model with strong generalization ability . Based on the constructed model , a novel piecewise Hermitian interpolation method is then proposed to deal with the uncompensated AFM images , obtained in both forward and backward scanning directions , so as to implement end-to-end compensation for hysteresis to generate a high-quality image .
This helps the network to precisely and efficiently learn highly discriminative features for speech activities . What ’s more , we implement a recurrent neural network with the gated recurrent unit scheme to make use of the sequential information from video to produce the final decision . To give a comprehensive evaluation of the proposed method , we collect a large-scale dataset from unconstrained speech activities , which consists of a large number of speech/non-speech video sequences under various kinds of degradation .
The appropriate network orthogonal initialization parameters further reduce the network training time and enhance the robustness of the acoustic model . Experimental results on the TIMIT and LibriSpeech datasets show that the improved gating recurrent unit model has a 2.8 % absolute phoneme error rate reduction compared to the baseline system , compared to the standard long short-term memory model , the average iteration period of neural network training is reduced by 16.6 % , which improves both recognition performance and computational efficiency .
Using the terminology from , we call a glimpse . The step is completed by computing a new generator state : Long short - term memory units ( LSTM , ) and gated recurrent units ( GRU , ) are typically used as a recurrent activation , to which we refer as a recurrency . The process is graphically illustrated in Fig .
[ ] Seo2016BidirectionalAF . It adds a self - attention layer after the bidirectional attention component , simplifies some of the pooling operations and substitutes the LSTMs for gated recurrent units [ GRUs ; ] [ ] GRU : Cho2014 .
The input includes the source sentence and the fact descriptions . For each sequence , we employ the bidirectional Gated Recurrent Unit ( BiGRU ) encoder , to construct its semantic representation . Take the sentence as an example .
Along with their intuitive design , RNNs have shown outstanding performance across various NLP tasks e.g. language modeling , machine translation , text classification , and parsing . Among several variants of the original RNN , gated recurrent architectures such as long short - term memory ( LSTM ) and gated recurrent unit ( GRU ) have been accepted as de - facto standard choices for RNNs due to their capability of addressing the vanishing and exploding gradient problem and considering long - term dependencies . Gated RNNs achieve these properties by introducing additional gating units that learn to control the amount of information to be transferred or forgotten , and are proven to work well without relying on complex optimization algorithms or careful initialization .
The first row reports the results achieved with a simple RNN with ReLU activations ( no gating mechanisms are used here ) . Although this architecture has recently shown promising results in some machine learning tasks , our results confirm that gated recurrent networks ( rows 2 - 5 ) outperform traditional RNNs . We also observe that GRUs tend to slightly outperform the LSTM model .
Sentence encoding models ( their main architecture is shown in Figure [ reference ] .a ) independently encode a pair of sentences , a premise and a hypothesis using pre - trained word embedding vectors , then learn semantic relation between two sentences with a multi - layer perceptron ( MLP ) . In these models , LSTM ( Long Short - Term Memory networks ) , its variants GRU ( Gated Recurrent Units ) and Bi - LSTM , are usually utilized to encode the sentences since they were capable of learning long - term dependencies inside sentences . For example , Conneau et al . proposed a generic NLI training scheme and compared several sentence encoding architectures : LSTM or GRU , Bi - LSTM with mean / max pooling , self - attention network and hierarchical convolutional networks .
To this end we propose a deep learning approach to avoid heuristics and strong assumptions on the reflectance prior . We compare two network architectures : one classic ‘ U ’ shaped Convolutional Neural Network ( CNN ) and a Recurrent Neural Network ( RNN ) composed of Convolutional Gated Recurrent Units ( CGRU ) . We train our networks on a new dataset specifically designed for the task of intrinsic decomposition from sequences .
DCN-SR applies a co-attention network to capture the dynamic interactions between the user 's long- and short-term interaction behavior and generates co-dependent representations of the user 's long- and short-term interests . For modeling a user 's short-term interaction behavior , we design a CGRU network to take actions like " click ' ' , " collect ' ' and " buy ' ' into account . Experiments on e-commerce datasets show significant improvements of DCN-SR over state-of-the-art session-based recommendation methods , with improvements of up to 2.58 % on the Tmall dataset and 3.08 % on the Tianchi dataset in terms of Recall@10 .
We train the model using a dataset of 4,702 amateur karaoke performances selected for good intonation . We present a Convolutional Gated Recurrent Unit ( CGRU ) model to accomplish this task . This method can be extended into unsupervised pitch correction of a vocal performance , popularly referred to as autotuning .
We introduce the pointer sentinel mixture architecture for neural sequence models which has the ability to either reproduce a word from the recent context or produce a word from a standard softmax classifier . Our pointer sentinel-LSTM model achieves state of the art language modeling performance on the Penn Treebank ( 70.9 perplexity ) while using far fewer parameters than a standard softmax LSTM . In order to evaluate how well language models can exploit longer contexts and deal with more realistic vocabularies and larger corpora we also introduce the freely available WikiText corpus .
RECURRENT NEURAL NETWORKS AS PHONEME SPOTTERS Pruning recurrent neural networks for improved generalization performance Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah Stable Higher-Order Recurrent Neural Network Structures for Nonlinear Blind Source Separation Neural Machine Translation by Jointly Learning to Align and Translate Learning to Compare: Relation Network for Few-Shot Learning Grammar as a Foreign Language Deep Speech: Scaling up end-to-end speech recognition DRAW: A Recurrent Neural Network For Image Generation Implementing neural machine translation with bi-directional GRU and attention mechanism on FPGAs using HLS Recurrent Neural Networks for Accurate RSSI Indoor Localization RNN-Based Subway Passenger Flow Rolling Prediction Healthcare Expenditure Prediction for Crowd with Co-existing Medical Conditions Parsimonious memory unit for recurrent neural networks with application to natural language processing Discrete Event, Continuous Time RNNs Research on Man-Machine Conversation System Based on GRU seq2seq Model Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram Recurrent Neural Network for Predicting Transcription Factor Binding Sites Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks SBAG: A Hybrid Deep Learning Model for Large Scale Traffic Speed Prediction Gate-variants of Gated Recurrent Unit (GRU) neural networks SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks Explicit Interaction Model towards Text Classification DialogueRNN: An Attentive RNN for Emotion Detection in Conversations A Convolutional Encoder Model for Neural Machine Translation End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension Hierarchical Question-Image Co-Attention for Visual Question Answering ReasoNet: Learning to Stop Reading in Machine Comprehension An Enhanced Model for Inpainting on Digital Images Using Dynamic Masking Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation Fast algorithm using summed area tables with unified layer performing convolution and average pooling Optical Flow Estimation Using a Spatial Pyramid Network Probabilistic Model-Agnostic Meta-Learning Convolutional Sequence to Sequence Learning Submanifold Sparse Convolutional Networks A Discriminatively Learned CNN Embedding for Person Reidentification Image Super-Resolution Using Deep Convolutional Networks Brain tumor segmentation with Deep Neural Networks DeepMatching: Hierarchical Deformable Dense Matching V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation The Study on the Text Classification for Financial News Based on Partial Information Foreign Exchange Rates Forecasting with Convolutional Neural Network Integrating Deep Textual Features to Probability Matrix Factorization for Metabolite-disease Association Prediction Investigating Deep Learning Approaches for Human-Robot Proxemics Efficient Online Learning Algorithms Based on LSTM Neural Networks Using Inferred Gestures from sEMG Signal to Teleoperate a Domestic Robot for the Disabled Removing TMS Artifacts from EEG Recordings Using a Deep Gated Recurrent Unit Gated recurrent unit based frequency-dependent hysteresis modeling and end-to-end compensation Are You Speaking: Real-Time Speech Activity Detection via Landmark Pooling Network Research on Acoustic Model of Speech Recognition Based on Neural Network with Improved Gating Unit Attention-Based Models for Speech Recognition Deep contextualized word representations Faithful to the Original: Fact Aware Neural Abstractive Summarization Cell-aware Stacked LSTMs for Modeling Sentences Light Gated Recurrent Units for Speech Recognition Attention Boosted Sequential Inference Model Intrinsic Decomposition by Learning from Varying Lighting Conditions A Dynamic Co-attention Network for Session-based Recommendation Deep Autotuner: A Data-Driven Approach to Natural-Sounding Pitch Correction for Singing Voice in Karaoke Performances Pointer Sentinel Mixture Models
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