We construct three relationship graphs for hydrological gauges in the study area : the hydraulic distance graph , the Euclidean distance graph and the correlation graph . We fuse these graphs into one hydrological network graph , and propose a novel framework ST-Hydro which exploits Graph Convolutional Networks ( GCN ) for learning the spatial feature representations , and Recurrent Neural Networks with carefully designed activation functions for capturing temporal features simultaneously for hydrological prediction . Experimental results on real world data set demonstrate that the proposed framework can predict the river discharge effectively and at an early stage . Emergent is a software package that uses the AdEx neural dynamics model and LEABRA learning algorithm to simulate and train arbitrary recurrent neural network architectures in a biologically-realistic manner . We present Leabra7 , a complementary Python library that implements these same algorithms . We investigate recurrent neural network architectures for event-sequence processing . Event sequences , characterized by discrete observations stamped with continuous-valued times of occurrence , are challenging due to the potentially wide dynamic range of relevant time scales as well as interactions between time scales . This process is not parallelizable , and , in the case of neural MT models , it is particularly slow because a computationally intensive neural network is used to generate each token . While several recently proposed models avoid recurrence at train time by leveraging convolutions kalchbrenner2016neural , gehring2017convolutional , kaiser2017depthwise or self - attention vaswani2017attention as more - parallelizable alternatives to recurrent neural networks ( RNNs ) , use of autoregressive decoding makes it impossible to take full advantage of parallelism during inference . We introduce a non - autoregressive translation model based on the Transformer network vaswani2017attention . This leads to a convenient formulation where a language model has to predict the next token given a sequence of tokens as context . Recurrent neural networks are an effective way to compute distributed representations of the context by sequentially operating on the embeddings of the tokens . These representations can then be used to predict the next token as a probability distribution over a fixed vocabulary using a linear decoder followed by Softmax . The resulting network has two core properties : it runs in time that is linear in the length of the sequences and it sidesteps the need for excessive memorization . The ByteNet decoder attains state - of - the - art performance on character - level language modelling and outperforms the previous best results obtained with recurrent networks . The ByteNet also achieves state - of - the - art performance on character - to - character machine translation on the English - to - German WMT translation task , surpassing comparable neural translation models that are based on recurrent networks with attentional pooling and run in quadratic time . ResNet ’s building block uses the identity map as an anchor point and explicitly parameterizes an additive correction term ( the residual ) . Identity initialization also appears in the context of recurrent networks le2015simple . A tendency of ResNet and highway networks to fall - back to the identity map may make their effective depth much smaller than their nominal depth . We classify IoT traffic into four classes : Fixed-Bit Periodic ( FBP ) , Variable-Bit Periodic ( VBP ) , Fixed-Bit Aperiodic ( FBA ) , and Variable-Bit Aperiodic ( VBA ) . We show that LSTM outperforms all of the other models significantly in the symmetric Mean Absolute Percentage Error ( sMAPE ) measure for devices in the VBP class in our simulations . Furthermore , we show that LSTM has almost the same performance in this metric for the FBA class as MLP and 1-D CNN . We show that a progressive/sequential fusion framework based on long short term memory ( LSTM ) network aggregates the frame-wise human region representation at each time stamp and yields a sequence level human feature representation . Since LSTM nodes can remember and propagate previously accumulated good features and forget newly input inferior ones , even with simple hand-crafted features , the proposed recurrent feature aggregation network ( RFA-Net ) is effective in generating highly discriminative sequence level human representations . Extensive experimental results on two person re-identification benchmarks demonstrate that the proposed method performs favorably against state-of-the-art person re-identification methods . Employing the long short-term memory ( LSTM ) structure , we develop a discriminative model based on the hidden state and a generative model based on the cell state . The addition of the generative model constrains the network to learn functional communities represented by the LSTM nodes that are both consistent with the data generation as well as useful for the classification task . We apply our approach to the classification of subjects with autism vs. healthy controls using several datasets from the Autism Brain Imaging Data Exchange . The temporal component(i.e . temporal encoder/decoder ) uses stacked convolutional LSTM and conveys object movement . Specifically , we integrate CNN and the stacked convolutional LSTM to learn normal patterns from the training data , which contains only normal events . CNN network is employed for lateral control model , mapping a single image from forward camera directly to corresponding curvature . For longitudinal control , stacked convolutional LSTM is used to extract spatial and temporal features from a sequence of frames , and to map the features with longitudinal control commands . The demo and data are in http://roadhackers.baidu.com . Dense video captioning involves first localizing events in a video and then generating captions for the identified events . We present the Joint Event Detection and Description Network ( JEDDi-Net ) for solving this task in an end-to-end fashion , which encodes the input video stream with three-dimensional convolutional layers , proposes variable- length temporal events based on pooled features , and then uses a two-level hierarchical LSTM module with context modeling to transcribe the event proposals into captions . We show the effectiveness of our proposed JEDDi-Net on the large-scale ActivityNet Captions dataset . Unlike natural language , the innumerable identifiers lead to the vocabulary explosion and more difficult to predict . Therefore , we propose a new approach , the Induced Token based LSTM , to deal with the massive identifiers , thus decrease the vocabulary size . In order to induce the code tokens , we present two approaches , one is a constraint character-level LSTM and the other one is encoding identifiers with various preceding context before feeding them into a token-level LSTM . DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts . DeepTag extends multitask LSTM with an improved hierarchical objective that captures the semantic structures between diseases . To foster human-machine collaboration , DeepTag also learns to abstain in examples when it is uncertain and defers them to human experts , resulting in improved performance . Obtained LSTM-based language models were used for N-best list rescoring . As well we tested a linear interpolation of LSTM language model with the baseline 3-gram language model and achieved 22 % relative reduction of the word error rate with respect to the baseline 3-gram model . Our three experiments were conducted on BEST 2010 I2R , a standard Thai NLP corpus on F1 measurement , with the different percentage of noises having been synthesized . Firstly , for Tokenization , the result shows that Synthai , a jointed bidirectional LSTM , has the best performance . Additionally , for POS , bi-directional LSTM with CRF has obtained the best performance . Assembly level data from 50 Windows portable executable ( PE ) files are extracted , and basic blocks are constructed using the IDA Disassembler toolkit . The results show that longer primary block sequences result in richer LSTM hidden layer representations . The hidden states are fed as features into Max pooling layers or Attention layers , depending on the configuration being tested , and the final classification is performed using Logistic Regression with a single hidden layer . Our experiments indicate better statistics , such as correlation coefficient and root-mean-square error , for the CTBT forecasts from the proposed LSTM compared to the RNN , Persistency , and the Farneback method . The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN , Persistency , and Farneback projections in terms of Probability of Detection , False Alarm Ratio , Critical Success Index , correlation coefficient , and root-mean-square error , especially in predicting the convective rainfalls . The proposed method shows superior capabilities in short-term forecasting over compared methods , and has the potential to be implemented globally as an alternative short-term forecast product . If a body part is predicted to be visible , then , otherwise denotes occlusion . The visibility mask is computed using a softmax over the unnormalized log probabilities generated by the LSTM . Hence , our objective is to minimize the cross - entropy . For this task , in each question only one node is special , e.g. the B in eval B has_fear , and we assign a single value 1 to the annotation vector for this special node and 0 to all the other nodes . For RNN and LSTM the data is converted into token sequences like below : n6 e1 n1 eol In addition to a novel model architecture , we also propose a new type of hidden unit ( in Eq . ( [ reference ] ) ) that has been motivated by the LSTM unit but is much simpler to compute and implement . Fig . tang2015target developed target dependent Long Short - Term Memory Networks ( LSTM ) , where target information is automatically taken into account . tai2015improved generalized LSTM to Tree - LSTM where each LSTM unit gains information from its children units . zhou2016attention introduced BLSTM with attention mechanism to automatically select features that have a decisive effect on classification . subsubsection : LSTM based question model The essential structure of a LSTM unit is a memory cell which reserves the state of a sequence . At each step , the LSTM unit takes one input vector ( word vector in our case ) and updates the memory cell , then output a hidden state . Our baseline sentence embedding model simply sums the embeddings of the words in each sentence . In addition , we experiment with two simple sequence embedding models : a plain RNN and an LSTM RNN . The word embeddings for all of the models are initialized with the 300d reference GloVe vectors ( 840B token version , ? ) and fine - tuned as part of training . du2017learning present a state - of - the - art neural sequence - to - sequence model to generate questions from a given sentence / paragraph . The model used is an attention - based encoder - decoder network , where the encoder reads in a given text and the decoder is an LSTM RNN that produces the question by predicting one word at a time . du2017learning use the SQuAD question - answering dataset to develop a question generation dataset by pairing sentences from the text with the corresponding questions . Given the same data , under both the discriminative and generative settings RNNGs were found to parse with significantly higher accuracy than ( respectively ) the models of vinyals:2015 and choe:2016 that represent as a “ linearized ” sequence of symbols and parentheses without explicitly capturing the tree structure , or even constraining the to be a well - formed tree ( see Table [ reference ] ) . vinyals:2015 directly predict the sequence of nonterminals , “ shifts ” ( which consume a terminal symbol ) , and parentheses from left to right , conditional on the input terminal sequence , while choe:2016 used a sequential LSTM language model on the same linearized trees to create a generative variant of the vinyals:2015 model . The generative model is used to re - rank parse candidates . In detail , aESIM also consists of four main parts : encoding layer , local inference modeling layer , decoding layer and classification layer . The only difference between ESIM and aESIM is that we substitute the two Bi - LSTM layers ( LSTM1 and LSTM2 ) in ESIM with two Bi - aLSTM layers in aESIM . Therefore , as illustrated in Figure [ reference ] , the layers with red - dotted circles in ESIM will be replaced by the Bi - aLSTM layers shown in the right upper corner of the Figure [ reference ] and the details of Bi - aLSTM can be found in Figure [ reference ] . Thus the proposed stacked LSTM architecture modulates the amount of information to be delivered not only in horizontal recurrence but also in vertical connections , from which useful features extracted from lower layers are effectively conveyed to upper layers . We dub this architecture Cell - aware Stacked LSTM ( CAS - LSTM ) and show from experiments that our models achieve state - of - the - art results on benchmark datasets for natural language inference , paraphrase detection , and sentiment classification . section : Introduction [ reference ] . We train the base ESIM model without the tree - LSTM , as in [ reference ] , on the task of natural language inference with ADAM for 10 epochs on MultiNLI and also SNLI . Training Schedules : We use the following batch size schedules We evaluate several variants of our approach : Att - GT + LSTM models use ground - truth attributes as the input while Att - RegionCNN + LSTM uses the attributes vector predicted by the region based attributes prediction network in section [ reference ] . We also evaluate an approach Att - SVM + LSTM with linear SVM predicted attributes vector . We use the second fully connected layer of the fine - tuned VggNet to feed the SVM . The previous best performing model on the task performs phrase matching by using the attention mechanism . Our results show that NTI - SLSTM improved the performance of the sequential LSTM encoder by approximately 2 % . Not surprisingly , using LSTM as leaf node function helps in learning better representations . This operation is similar as the bottleneck structure in , but more efficient . After that , Conv - LSTM layers with 256 channels are inserted into the single - shot TLL network . We try to incrementally stack Conv - LSTM layers to the network , however , due to difficulties in training multiple RNNs , experiments show that stacking two or more Conv - LSTMs is not beneficial . The goal of the framework is to automatically recommend VNF descriptors based on a given description or/and to complete the descriptors with appropriate data . The framework is based on two neural network methods : Long short term memory ( LSTM ) and Convolutional Neural Networks ( CNN ) , which are both complementary in their abilities and could be combined to enhance the performance of the framework . The experiments show good and promising results . However , the amount of motion search related computation is significantly increased by the new approach as well . Based on the triangle inequality we investigate a modified motion search order especially suited for the long-term memory approach . The concept of half-pel refinement is incorporated into the new search ordering method . Principle component/independent component analysis to identify features of significant relationships with RTW outcomes or combine raw features into new features ; 3 . A machine learning model that combines Variational Autoencoder , Long and Short Term Memory , and Neural Turning Machines . Stage two will also include the development of an interactive dashboard and website to query the trained machine learning model . Service migration in pervasive cloud computing is important for leveraging cloud resources to execute mobile applications effectively and efficiently . This paper proposes a LSTM ( long and short-term memory model ) based service migration approach for pervasive cloud computing , i.e. , LSTM4PCC , which supports an accurate prediction of cloud resources . LSTM4PCC makes a prediction for cloud resource availability with a LSTM network and establishes a service migration mechanism in order to optimize service executions . Abstract : We applied two first-order , linear , time-varying , differential equations to the task of segmenting motion from sequences of images . The equations are modified Grossberg formulas for long-term and short-term memory models characterizing the neurotransmitter and cell-activity levels of a synapse and neuron . We described how a two layered , sensory , neural network can be built using the equations to simulate the amacrine neurons of the retina . This paper presents a method to enable a mobile robot working in non-stationary environments to plan its path and localize within multiple map hypotheses simultaneously . The maps are generated using a long-term and short-term memory mechanism that ensures only persistent configurations in the environment are selected to create the maps . In order to evaluate the proposed method , experimentation is conducted in an office environment . The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method , the LSTM ( long term short term memory ) . Other than existing prediction models , the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center . Due to its well-known capabilities in time-series forecasting , especially on a large dataset , the Recurrent Neural Network ( RNN ) method is employed in this study for modelling and prediction analysis . RNN has two different models , i.e. , Long-Term Short Memory ( LSTM ) and Gated Recurrent Unit ( GRU ) . GRU is implemented in this case study to predict and forecast future data . By examining the applications of deep learning in cyber security , it has been observed that solving a cyber security problem by analogy to image or text processing problems gives better results than direct application of deep learning techniques to the dataset . LSTM ( Long Term Term Memory ) and similar RNN ( Recurrent Neural Network ) models make it possible to evaluate a single network packet with previous and subsequent network packets , providing significant advantages over other machine learning algorithms . By examining the studies , it is pointed out that when the data set is of sufficient size , deep learning techniques give better results than other machine learning algorithms . The co-occurrence of language and mnestic deficits in the aphasic population affords the opportunity to examine their relationships . Here we review evidence from aphasia supporting the view that verbal STM processes are inextricably linked with the lexical processing system ( as opposed to drawing solely on an independent shortterm memory system ) . These studies are discussed as a foundation for the development of a new model of word processing , verbal STM and learning . Local addressing is realized by content encoding of the LSTM encoder and word level attention while global addressing is realized by field encoding of the field - gating LSTM variation and field level attention in our model . The structure - aware seq2seq architecture we proposed exploits encoder - decoder framework using long short - term memory ( LSTM ) [ reference ] units with local and global addressing on the structured table . In the encoding phase , our model first encodes the sets of fieldvalue records in the table by integrating field information and content representation . RNNs have proven to be useful in various natural language processing tasks including language modeling , speech recognition and machine translation . Long Short - Term Memory ( LSTM ) is one of the most frequently used variants of recurrent neural networks . Our model uses the LSTM architecture from Graves et al . . Biomedical named entity recognition ; CE : Character embedding ; CLWE : Character level word embedding ; CNN : convolution neural network ; CRF : Conditional random field ; LSTM : long short - term memory ; MTL : Multi - task learning ; MTM : Multi - task model ; NER : Named entity recognition ; NLP : Natural language processing ; PMC : PubMed Central ; STM : Single - task model ; RNN : It has obtained considerable attention due to its broad applications in natural language processing . Most existing studies set up sentiment classifiers using supervised machine learning approaches , such as support vector machine ( SVM ) , convolutional neural network ( CNN ) , long short - term memory ( LSTM ) , Tree - LSTM , and attention - based methods . Despite the remarkable progress made by the previous work , we argue that sentiment analysis still remains a challenge . 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 . The only attempt so far to build an end - to - end differentiable neural network for entailment failed to outperform such a simple similarity classifier . In this paper , we propose a neural model that reads two sentences to determine entailment using long short - term memory units . We extend this model with a word - by - word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases . Recurrent neural networks ( RNNs ) have been successful for modeling sequence data . RNNs equipped with gated hidden units and internal short - term memories , such as long short - term memories ( LSTM ) have achieved a notable success in several NLP tasks including named entity recognition , constituency parsing , textual entailment recognition , question answering , and machine translation . However , most LSTM models explored so far are sequential . In the machine learning community , the research of novel and powerful RNN models is a very active research topic . General - purpose RNNs such as Long Short Term Memories ( LSTMs ) have been the subject of several studies and modifications over the past years . This evolution has recently led to a novel architecture called Gated Recurrent Unit ( GRU ) , that simplifies the complex LSTM cell design . At each time step , the hidden state of the RNN is updated by where is a non - linear activation function . may be as simple as an element - wise logistic sigmoid function and as complex as a long short - term memory ( LSTM ) unit . An RNN can learn a probability distribution over a sequence by being trained to predict the next symbol in a sequence . The gated hidden unit is an alternative to the conventional simple units such as an element - wise . This gated unit is similar to a long short - term memory ( LSTM ) unit proposed earlier by , sharing with it the ability to better model and learn long - term dependencies . This is made possible by having computation paths in the unfolded RNN for which the product of derivatives is close to 1 . Relatedly , present excellent parsing results with a single left - to - right pass , but require a stack to explicitly delay making decisions and a parsing - specific transition strategy in order to achieve good parsing accuracies . The LSTM in contrast uses its short term memory to model the complex underlying structure that connects the input - output pairs . Recently , researchers have developed a number of neural network models that can be applied to general sequence - to - sequence problems . Also , we take the maximum over , so as to punish networks that have good length only for a few starting times , which can only well memorize information originating from those specific times . The proposed mean recurrent length has an interesting reciprocal relation with the short - term memory ( STM ) measure proposed in , but mean recurrent length emphasizes more on long - term memory capacity , which is more suitable for our intended applications . With this , we are ready to illustrate the memory advantage of DilatedRNN . In our case , we want to pass information of the convolutional feature maps to the corresponding deconvolutional layers . The very deep highway networks are essentially feedforward long short - term memory ( LSTMs ) with forget gates , and the CNN layers of deep residual network are feedforward LSTMs without gates . Note that our networks are in general not in the format of standard feedforward LSTMs . In general the encoder and decoder may be implemented by any recurrent neural network . In our experiments we use the Long Short - Term Memory architecture ( LSTM ; ) for both , in the extended form with forget gates . We favour LSTM due to its proven track record for handling long - range dependencies in real sequential data . We compile a novel dataset of teasers by systematically accumulating tweets and selecting those that conform to the teaser definition . We have compared a number of neural abstractive architectures on the task of teaser generation and the overall best performing system is See et al.(2017 ) 's seq2seq with pointer network . We encode tables as graphs using a graph neural network model based on the Transformer architecture . The answers are then selected from the encoded graph using a pointer network . This model is appropriate for processing conversations around structured data , where the attention mechanism that selects the answers to a question can also be used to resolve conversational references . This work provides the first neural network-based approach to argumentation mining , focusing on extracting links between argument components , with a secondary focus on classifying types of argument components . In order to solve this problem , we propose to use a modification of a Pointer Network architecture . A Pointer Network is appealing for this task for the following reasons : 1 ) It takes into account the sequential nature of argument components ; 2 ) By construction , it enforces certain properties of the tree structure present in argument relations ; 3 ) The hidden representations can be applied to auxiliary tasks . Each thread kernel was mapped onto an executing thread to advance simulation parallel . In addition , two algorithms were proposed to support high performance : ( 1 ) To improve the communication efficiency between threads , we proposed a pointer-based communication mechanism . By using buffers , synchronization between threads can be annihilated . Recent work has suggested a Memory Augmented Neural Network ( MANN ) for meta-learning . MANN is an implementation of a Neural Turing Machine ( NTM ) with the ability to rapidly assimilate new data in its memory , and use this data to make accurate predictions . In models such as MANN , the input data samples and their appropriate labels from previous step are bound together in the same memory locations . In this paper , P(#P ) and PF(#P ) are characterized in terms of a largely different computation structure , where P(#P ) ( resp . , PF(#P ) ) is the class of sets ( resp . , functions ) that are polynomial-time Turing reducible to # P functions . Let MidP be the class of functions that give the medians in the outputs of metric Turing machines , where a metric Turing machine is a polynomial-time bounded nondeterministic Turing machine such that each branch writes a binary number on an output tape . Then it is shown that every function in PF(#P ) is polynomial-time one-Turing reducible to a function in MidP and MidP @ ? In these tasks , the memory is pre - loaded using a learned embedding of the provided context , such as a paragraph of text , and then the controller , given an embedding of the question , repeatedly queries the memory by content - based reads to determine an answer . subsection : Neural Turing Machine The Neural Turing Machine is a recurrent neural network equipped with a content - addressable memory , similar to Memory Networks , but with the additional capability to write to memory over time .
We construct three relationship graphs for hydrological gauges in the study area : the hydraulic distance graph , the Euclidean distance graph and the correlation graph . We fuse these graphs into one hydrological network graph , and propose a novel framework ST-Hydro which exploits Graph Convolutional Networks ( GCN ) for learning the spatial feature representations , and Recurrent Neural Networks with carefully designed activation functions for capturing temporal features simultaneously for hydrological prediction . Experimental results on real world data set demonstrate that the proposed framework can predict the river discharge effectively and at an early stage .
Emergent is a software package that uses the AdEx neural dynamics model and LEABRA learning algorithm to simulate and train arbitrary recurrent neural network architectures in a biologically-realistic manner . We present Leabra7 , a complementary Python library that implements these same algorithms .
We investigate recurrent neural network architectures for event-sequence processing . Event sequences , characterized by discrete observations stamped with continuous-valued times of occurrence , are challenging due to the potentially wide dynamic range of relevant time scales as well as interactions between time scales .
This process is not parallelizable , and , in the case of neural MT models , it is particularly slow because a computationally intensive neural network is used to generate each token . While several recently proposed models avoid recurrence at train time by leveraging convolutions kalchbrenner2016neural , gehring2017convolutional , kaiser2017depthwise or self - attention vaswani2017attention as more - parallelizable alternatives to recurrent neural networks ( RNNs ) , use of autoregressive decoding makes it impossible to take full advantage of parallelism during inference . We introduce a non - autoregressive translation model based on the Transformer network vaswani2017attention .
This leads to a convenient formulation where a language model has to predict the next token given a sequence of tokens as context . Recurrent neural networks are an effective way to compute distributed representations of the context by sequentially operating on the embeddings of the tokens . These representations can then be used to predict the next token as a probability distribution over a fixed vocabulary using a linear decoder followed by Softmax .
The resulting network has two core properties : it runs in time that is linear in the length of the sequences and it sidesteps the need for excessive memorization . The ByteNet decoder attains state - of - the - art performance on character - level language modelling and outperforms the previous best results obtained with recurrent networks . The ByteNet also achieves state - of - the - art performance on character - to - character machine translation on the English - to - German WMT translation task , surpassing comparable neural translation models that are based on recurrent networks with attentional pooling and run in quadratic time .
ResNet ’s building block uses the identity map as an anchor point and explicitly parameterizes an additive correction term ( the residual ) . Identity initialization also appears in the context of recurrent networks le2015simple . A tendency of ResNet and highway networks to fall - back to the identity map may make their effective depth much smaller than their nominal depth .
We classify IoT traffic into four classes : Fixed-Bit Periodic ( FBP ) , Variable-Bit Periodic ( VBP ) , Fixed-Bit Aperiodic ( FBA ) , and Variable-Bit Aperiodic ( VBA ) . We show that LSTM outperforms all of the other models significantly in the symmetric Mean Absolute Percentage Error ( sMAPE ) measure for devices in the VBP class in our simulations . Furthermore , we show that LSTM has almost the same performance in this metric for the FBA class as MLP and 1-D CNN .
We show that a progressive/sequential fusion framework based on long short term memory ( LSTM ) network aggregates the frame-wise human region representation at each time stamp and yields a sequence level human feature representation . Since LSTM nodes can remember and propagate previously accumulated good features and forget newly input inferior ones , even with simple hand-crafted features , the proposed recurrent feature aggregation network ( RFA-Net ) is effective in generating highly discriminative sequence level human representations . Extensive experimental results on two person re-identification benchmarks demonstrate that the proposed method performs favorably against state-of-the-art person re-identification methods .
Employing the long short-term memory ( LSTM ) structure , we develop a discriminative model based on the hidden state and a generative model based on the cell state . The addition of the generative model constrains the network to learn functional communities represented by the LSTM nodes that are both consistent with the data generation as well as useful for the classification task . We apply our approach to the classification of subjects with autism vs. healthy controls using several datasets from the Autism Brain Imaging Data Exchange .
The temporal component(i.e . temporal encoder/decoder ) uses stacked convolutional LSTM and conveys object movement . Specifically , we integrate CNN and the stacked convolutional LSTM to learn normal patterns from the training data , which contains only normal events .
CNN network is employed for lateral control model , mapping a single image from forward camera directly to corresponding curvature . For longitudinal control , stacked convolutional LSTM is used to extract spatial and temporal features from a sequence of frames , and to map the features with longitudinal control commands . The demo and data are in http://roadhackers.baidu.com .
Dense video captioning involves first localizing events in a video and then generating captions for the identified events . We present the Joint Event Detection and Description Network ( JEDDi-Net ) for solving this task in an end-to-end fashion , which encodes the input video stream with three-dimensional convolutional layers , proposes variable- length temporal events based on pooled features , and then uses a two-level hierarchical LSTM module with context modeling to transcribe the event proposals into captions . We show the effectiveness of our proposed JEDDi-Net on the large-scale ActivityNet Captions dataset .
Unlike natural language , the innumerable identifiers lead to the vocabulary explosion and more difficult to predict . Therefore , we propose a new approach , the Induced Token based LSTM , to deal with the massive identifiers , thus decrease the vocabulary size . In order to induce the code tokens , we present two approaches , one is a constraint character-level LSTM and the other one is encoding identifiers with various preceding context before feeding them into a token-level LSTM .
DeepTag is trained on a newly curated dataset of 112,558 veterinary notes manually annotated by experts . DeepTag extends multitask LSTM with an improved hierarchical objective that captures the semantic structures between diseases . To foster human-machine collaboration , DeepTag also learns to abstain in examples when it is uncertain and defers them to human experts , resulting in improved performance .
Obtained LSTM-based language models were used for N-best list rescoring . As well we tested a linear interpolation of LSTM language model with the baseline 3-gram language model and achieved 22 % relative reduction of the word error rate with respect to the baseline 3-gram model .
Our three experiments were conducted on BEST 2010 I2R , a standard Thai NLP corpus on F1 measurement , with the different percentage of noises having been synthesized . Firstly , for Tokenization , the result shows that Synthai , a jointed bidirectional LSTM , has the best performance . Additionally , for POS , bi-directional LSTM with CRF has obtained the best performance .
Assembly level data from 50 Windows portable executable ( PE ) files are extracted , and basic blocks are constructed using the IDA Disassembler toolkit . The results show that longer primary block sequences result in richer LSTM hidden layer representations . The hidden states are fed as features into Max pooling layers or Attention layers , depending on the configuration being tested , and the final classification is performed using Logistic Regression with a single hidden layer .
Our experiments indicate better statistics , such as correlation coefficient and root-mean-square error , for the CTBT forecasts from the proposed LSTM compared to the RNN , Persistency , and the Farneback method . The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN , Persistency , and Farneback projections in terms of Probability of Detection , False Alarm Ratio , Critical Success Index , correlation coefficient , and root-mean-square error , especially in predicting the convective rainfalls . The proposed method shows superior capabilities in short-term forecasting over compared methods , and has the potential to be implemented globally as an alternative short-term forecast product .
If a body part is predicted to be visible , then , otherwise denotes occlusion . The visibility mask is computed using a softmax over the unnormalized log probabilities generated by the LSTM . Hence , our objective is to minimize the cross - entropy .
For this task , in each question only one node is special , e.g. the B in eval B has_fear , and we assign a single value 1 to the annotation vector for this special node and 0 to all the other nodes . For RNN and LSTM the data is converted into token sequences like below : n6 e1 n1 eol
In addition to a novel model architecture , we also propose a new type of hidden unit ( in Eq . ( [ reference ] ) ) that has been motivated by the LSTM unit but is much simpler to compute and implement . Fig .
tang2015target developed target dependent Long Short - Term Memory Networks ( LSTM ) , where target information is automatically taken into account . tai2015improved generalized LSTM to Tree - LSTM where each LSTM unit gains information from its children units . zhou2016attention introduced BLSTM with attention mechanism to automatically select features that have a decisive effect on classification .
subsubsection : LSTM based question model The essential structure of a LSTM unit is a memory cell which reserves the state of a sequence . At each step , the LSTM unit takes one input vector ( word vector in our case ) and updates the memory cell , then output a hidden state .
Our baseline sentence embedding model simply sums the embeddings of the words in each sentence . In addition , we experiment with two simple sequence embedding models : a plain RNN and an LSTM RNN . The word embeddings for all of the models are initialized with the 300d reference GloVe vectors ( 840B token version , ? ) and fine - tuned as part of training .
du2017learning present a state - of - the - art neural sequence - to - sequence model to generate questions from a given sentence / paragraph . The model used is an attention - based encoder - decoder network , where the encoder reads in a given text and the decoder is an LSTM RNN that produces the question by predicting one word at a time . du2017learning use the SQuAD question - answering dataset to develop a question generation dataset by pairing sentences from the text with the corresponding questions .
Given the same data , under both the discriminative and generative settings RNNGs were found to parse with significantly higher accuracy than ( respectively ) the models of vinyals:2015 and choe:2016 that represent as a “ linearized ” sequence of symbols and parentheses without explicitly capturing the tree structure , or even constraining the to be a well - formed tree ( see Table [ reference ] ) . vinyals:2015 directly predict the sequence of nonterminals , “ shifts ” ( which consume a terminal symbol ) , and parentheses from left to right , conditional on the input terminal sequence , while choe:2016 used a sequential LSTM language model on the same linearized trees to create a generative variant of the vinyals:2015 model . The generative model is used to re - rank parse candidates .
In detail , aESIM also consists of four main parts : encoding layer , local inference modeling layer , decoding layer and classification layer . The only difference between ESIM and aESIM is that we substitute the two Bi - LSTM layers ( LSTM1 and LSTM2 ) in ESIM with two Bi - aLSTM layers in aESIM . Therefore , as illustrated in Figure [ reference ] , the layers with red - dotted circles in ESIM will be replaced by the Bi - aLSTM layers shown in the right upper corner of the Figure [ reference ] and the details of Bi - aLSTM can be found in Figure [ reference ] .
Thus the proposed stacked LSTM architecture modulates the amount of information to be delivered not only in horizontal recurrence but also in vertical connections , from which useful features extracted from lower layers are effectively conveyed to upper layers . We dub this architecture Cell - aware Stacked LSTM ( CAS - LSTM ) and show from experiments that our models achieve state - of - the - art results on benchmark datasets for natural language inference , paraphrase detection , and sentiment classification . section : Introduction
[ reference ] . We train the base ESIM model without the tree - LSTM , as in [ reference ] , on the task of natural language inference with ADAM for 10 epochs on MultiNLI and also SNLI . Training Schedules : We use the following batch size schedules
We evaluate several variants of our approach : Att - GT + LSTM models use ground - truth attributes as the input while Att - RegionCNN + LSTM uses the attributes vector predicted by the region based attributes prediction network in section [ reference ] . We also evaluate an approach Att - SVM + LSTM with linear SVM predicted attributes vector . We use the second fully connected layer of the fine - tuned VggNet to feed the SVM .
The previous best performing model on the task performs phrase matching by using the attention mechanism . Our results show that NTI - SLSTM improved the performance of the sequential LSTM encoder by approximately 2 % . Not surprisingly , using LSTM as leaf node function helps in learning better representations .
This operation is similar as the bottleneck structure in , but more efficient . After that , Conv - LSTM layers with 256 channels are inserted into the single - shot TLL network . We try to incrementally stack Conv - LSTM layers to the network , however , due to difficulties in training multiple RNNs , experiments show that stacking two or more Conv - LSTMs is not beneficial .
The goal of the framework is to automatically recommend VNF descriptors based on a given description or/and to complete the descriptors with appropriate data . The framework is based on two neural network methods : Long short term memory ( LSTM ) and Convolutional Neural Networks ( CNN ) , which are both complementary in their abilities and could be combined to enhance the performance of the framework . The experiments show good and promising results .
However , the amount of motion search related computation is significantly increased by the new approach as well . Based on the triangle inequality we investigate a modified motion search order especially suited for the long-term memory approach . The concept of half-pel refinement is incorporated into the new search ordering method .
Principle component/independent component analysis to identify features of significant relationships with RTW outcomes or combine raw features into new features ; 3 . A machine learning model that combines Variational Autoencoder , Long and Short Term Memory , and Neural Turning Machines . Stage two will also include the development of an interactive dashboard and website to query the trained machine learning model .
Service migration in pervasive cloud computing is important for leveraging cloud resources to execute mobile applications effectively and efficiently . This paper proposes a LSTM ( long and short-term memory model ) based service migration approach for pervasive cloud computing , i.e. , LSTM4PCC , which supports an accurate prediction of cloud resources . LSTM4PCC makes a prediction for cloud resource availability with a LSTM network and establishes a service migration mechanism in order to optimize service executions .
Abstract : We applied two first-order , linear , time-varying , differential equations to the task of segmenting motion from sequences of images . The equations are modified Grossberg formulas for long-term and short-term memory models characterizing the neurotransmitter and cell-activity levels of a synapse and neuron . We described how a two layered , sensory , neural network can be built using the equations to simulate the amacrine neurons of the retina .
This paper presents a method to enable a mobile robot working in non-stationary environments to plan its path and localize within multiple map hypotheses simultaneously . The maps are generated using a long-term and short-term memory mechanism that ensures only persistent configurations in the environment are selected to create the maps . In order to evaluate the proposed method , experimentation is conducted in an office environment .
The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method , the LSTM ( long term short term memory ) . Other than existing prediction models , the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center .
Due to its well-known capabilities in time-series forecasting , especially on a large dataset , the Recurrent Neural Network ( RNN ) method is employed in this study for modelling and prediction analysis . RNN has two different models , i.e. , Long-Term Short Memory ( LSTM ) and Gated Recurrent Unit ( GRU ) . GRU is implemented in this case study to predict and forecast future data .
By examining the applications of deep learning in cyber security , it has been observed that solving a cyber security problem by analogy to image or text processing problems gives better results than direct application of deep learning techniques to the dataset . LSTM ( Long Term Term Memory ) and similar RNN ( Recurrent Neural Network ) models make it possible to evaluate a single network packet with previous and subsequent network packets , providing significant advantages over other machine learning algorithms . By examining the studies , it is pointed out that when the data set is of sufficient size , deep learning techniques give better results than other machine learning algorithms .
The co-occurrence of language and mnestic deficits in the aphasic population affords the opportunity to examine their relationships . Here we review evidence from aphasia supporting the view that verbal STM processes are inextricably linked with the lexical processing system ( as opposed to drawing solely on an independent shortterm memory system ) . These studies are discussed as a foundation for the development of a new model of word processing , verbal STM and learning .
Local addressing is realized by content encoding of the LSTM encoder and word level attention while global addressing is realized by field encoding of the field - gating LSTM variation and field level attention in our model . The structure - aware seq2seq architecture we proposed exploits encoder - decoder framework using long short - term memory ( LSTM ) [ reference ] units with local and global addressing on the structured table . In the encoding phase , our model first encodes the sets of fieldvalue records in the table by integrating field information and content representation .
RNNs have proven to be useful in various natural language processing tasks including language modeling , speech recognition and machine translation . Long Short - Term Memory ( LSTM ) is one of the most frequently used variants of recurrent neural networks . Our model uses the LSTM architecture from Graves et al . .
Biomedical named entity recognition ; CE : Character embedding ; CLWE : Character level word embedding ; CNN : convolution neural network ; CRF : Conditional random field ; LSTM : long short - term memory ; MTL : Multi - task learning ; MTM : Multi - task model ; NER : Named entity recognition ; NLP : Natural language processing ; PMC : PubMed Central ; STM : Single - task model ; RNN :
It has obtained considerable attention due to its broad applications in natural language processing . Most existing studies set up sentiment classifiers using supervised machine learning approaches , such as support vector machine ( SVM ) , convolutional neural network ( CNN ) , long short - term memory ( LSTM ) , Tree - LSTM , and attention - based methods . Despite the remarkable progress made by the previous work , we argue that sentiment analysis still remains a challenge .
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 .
The only attempt so far to build an end - to - end differentiable neural network for entailment failed to outperform such a simple similarity classifier . In this paper , we propose a neural model that reads two sentences to determine entailment using long short - term memory units . We extend this model with a word - by - word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases .
Recurrent neural networks ( RNNs ) have been successful for modeling sequence data . RNNs equipped with gated hidden units and internal short - term memories , such as long short - term memories ( LSTM ) have achieved a notable success in several NLP tasks including named entity recognition , constituency parsing , textual entailment recognition , question answering , and machine translation . However , most LSTM models explored so far are sequential .
In the machine learning community , the research of novel and powerful RNN models is a very active research topic . General - purpose RNNs such as Long Short Term Memories ( LSTMs ) have been the subject of several studies and modifications over the past years . This evolution has recently led to a novel architecture called Gated Recurrent Unit ( GRU ) , that simplifies the complex LSTM cell design .
At each time step , the hidden state of the RNN is updated by where is a non - linear activation function . may be as simple as an element - wise logistic sigmoid function and as complex as a long short - term memory ( LSTM ) unit . An RNN can learn a probability distribution over a sequence by being trained to predict the next symbol in a sequence .
The gated hidden unit is an alternative to the conventional simple units such as an element - wise . This gated unit is similar to a long short - term memory ( LSTM ) unit proposed earlier by , sharing with it the ability to better model and learn long - term dependencies . This is made possible by having computation paths in the unfolded RNN for which the product of derivatives is close to 1 .
Relatedly , present excellent parsing results with a single left - to - right pass , but require a stack to explicitly delay making decisions and a parsing - specific transition strategy in order to achieve good parsing accuracies . The LSTM in contrast uses its short term memory to model the complex underlying structure that connects the input - output pairs . Recently , researchers have developed a number of neural network models that can be applied to general sequence - to - sequence problems .
Also , we take the maximum over , so as to punish networks that have good length only for a few starting times , which can only well memorize information originating from those specific times . The proposed mean recurrent length has an interesting reciprocal relation with the short - term memory ( STM ) measure proposed in , but mean recurrent length emphasizes more on long - term memory capacity , which is more suitable for our intended applications . With this , we are ready to illustrate the memory advantage of DilatedRNN .
In our case , we want to pass information of the convolutional feature maps to the corresponding deconvolutional layers . The very deep highway networks are essentially feedforward long short - term memory ( LSTMs ) with forget gates , and the CNN layers of deep residual network are feedforward LSTMs without gates . Note that our networks are in general not in the format of standard feedforward LSTMs .
In general the encoder and decoder may be implemented by any recurrent neural network . In our experiments we use the Long Short - Term Memory architecture ( LSTM ; ) for both , in the extended form with forget gates . We favour LSTM due to its proven track record for handling long - range dependencies in real sequential data .
We compile a novel dataset of teasers by systematically accumulating tweets and selecting those that conform to the teaser definition . We have compared a number of neural abstractive architectures on the task of teaser generation and the overall best performing system is See et al.(2017 ) 's seq2seq with pointer network .
We encode tables as graphs using a graph neural network model based on the Transformer architecture . The answers are then selected from the encoded graph using a pointer network . This model is appropriate for processing conversations around structured data , where the attention mechanism that selects the answers to a question can also be used to resolve conversational references .
This work provides the first neural network-based approach to argumentation mining , focusing on extracting links between argument components , with a secondary focus on classifying types of argument components . In order to solve this problem , we propose to use a modification of a Pointer Network architecture . A Pointer Network is appealing for this task for the following reasons : 1 ) It takes into account the sequential nature of argument components ; 2 ) By construction , it enforces certain properties of the tree structure present in argument relations ; 3 ) The hidden representations can be applied to auxiliary tasks .
Each thread kernel was mapped onto an executing thread to advance simulation parallel . In addition , two algorithms were proposed to support high performance : ( 1 ) To improve the communication efficiency between threads , we proposed a pointer-based communication mechanism . By using buffers , synchronization between threads can be annihilated .
Recent work has suggested a Memory Augmented Neural Network ( MANN ) for meta-learning . MANN is an implementation of a Neural Turing Machine ( NTM ) with the ability to rapidly assimilate new data in its memory , and use this data to make accurate predictions . In models such as MANN , the input data samples and their appropriate labels from previous step are bound together in the same memory locations .
In this paper , P(#P ) and PF(#P ) are characterized in terms of a largely different computation structure , where P(#P ) ( resp . , PF(#P ) ) is the class of sets ( resp . , functions ) that are polynomial-time Turing reducible to # P functions . Let MidP be the class of functions that give the medians in the outputs of metric Turing machines , where a metric Turing machine is a polynomial-time bounded nondeterministic Turing machine such that each branch writes a binary number on an output tape . Then it is shown that every function in PF(#P ) is polynomial-time one-Turing reducible to a function in MidP and MidP @ ?
In these tasks , the memory is pre - loaded using a learned embedding of the provided context , such as a paragraph of text , and then the controller , given an embedding of the question , repeatedly queries the memory by content - based reads to determine an answer . subsection : Neural Turing Machine The Neural Turing Machine is a recurrent neural network equipped with a content - addressable memory , similar to Memory Networks , but with the additional capability to write to memory over time .
Joint Spatial and Temporal Modeling for Hydrological Prediction Leabra7: a Python package for modeling recurrent, biologically-realistic neural networks Discrete Event, Continuous Time RNNs Non-Autoregressive Neural Machine Translation Neural Machine Translation in Linear Time FractalNet: Ultra-Deep Neural Networks without Residuals Comparative Study of Forecasting Schemes for IoT Device Traffic in Machine-to-Machine Communication Person Re-identification via Recurrent Feature Aggregation Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI Detecting Video Anomaly with a Stacked Convolutional LSTM Framework Baidu driving dataset and end-to-end reactive control model Joint Event Detection and Description in Continuous Video Streams Deep-AutoCoder: Learning to Complete Code Precisely with Induced Code Tokens DeepTag: inferring diagnoses from veterinary clinical notes LSTM-Based Language Models for Very Large Vocabulary Continuous Russian Speech Recognition System A Comparative Study on Various Deep Learning Techniques for Thai NLP Lexical and Syntactic Tasks on Noisy Data Malware Detection for Forensic Memory Using Deep Recurrent Neural Networks Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks Towards Viewpoint Invariant 3D Human Pose Estimation Gated Graph Sequence Neural Networks Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling Stacked Attention Networks for Image Question Answering A large annotated corpus for learning natural language inference Identifying Well-formed Natural Language Questions What Do Recurrent Neural Network Grammars Learn About Syntax? Attention Boosted Sequential Inference Model Cell-aware Stacked LSTMs for Modeling Sentences Parameter Re-Initialization through Cyclical Batch Size Schedules Image Captioning and Visual Question Answering Based on Attributes and External Knowledge Neural Tree Indexers for Text Understanding Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation Virtual Network Function Descriptors Mining using Word Embeddings and Deep Neural Networks Fast search for long-term memory motion-compensated prediction Smart Work Injury Management (SWIM) System: Artificial Intelligence in Work Disability Management LSTM-Based Service Migration for Pervasive Cloud Computing Retinal Modeling: Segmenting Motion from Spatio-Temporal Inputs using Neural Networks Multiple map hypotheses for planning and navigating in non-stationary environments 건물 예측 제어용 LSTM 기반 일사 예측 모델 Recurrent Neural Networks - Gated Recurrent Unit for Behaviour Analysis and Prediction Modelling to Trigger High Energy-consumption Alert Applying Deep Learning to Cybersecurity Field and Its Advantages Neuropsychological and computational evidence for a model of lexical processing, verbal short-term memory and learning Table-to-text Generation by Structure-aware Seq2seq Learning CollaboNet: collaboration of deep neural networks for biomedical named entity recognition A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification Attention-Based Models for Speech Recognition Reasoning about Entailment with Neural Attention Neural Tree Indexers for Text Understanding Light Gated Recurrent Units for Speech Recognition Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Neural Machine Translation by Jointly Learning to Align and Translate Grammar as a Foreign Language Dilated Recurrent Neural Networks Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections DRAW: A Recurrent Neural Network For Image Generation News Article Teaser Tweets and How to Generate Them Answering Conversational Questions on Structured Data without Logical Forms Here's My Point: Argumentation Mining with Pointer Networks HSK: A Hierarchical Parallel Simulation Kernel for Multicore Platform Meta-Learning via Feature-Label Memory Network Simple Characterizations of P(#P) and Complete Problems Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
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