Relevant bibliographies by topics / Encoder-Decoder LSTM

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Encoder-Decoder LSTM'

Author: Grafiati
Published: 10 January 2023
Last updated: 28 January 2023

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Journal articles on the topic "Encoder-Decoder LSTM"

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Li, Mingfei, Jiajian Wu, Zhengpeng Chen, Jiangbo Dong, Zhiping Peng, Kai Xiong, Mumin Rao, Chuangting Chen, and Xi Li. "Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning." Energies 15, no. 17 (August 29, 2022): 6294. http://dx.doi.org/10.3390/en15176294.

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A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder–decoder LSTM, and encoder–decoder GRU. The results show that for the SOFC test set, the mean square error of encoder–decoder LSTM and encoder–decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder–decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.
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Subramanian, Sowkarthika, Yasoda Kailasa Gounder, and Sumathi Lingana. "Day-ahead solar irradiance forecast using sequence-to-sequence model with attention mechanism." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 900. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp900-909.

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<p>The increasing integration of distributed energy resources (DERs) into power grid makes it significant to forecast solar irradiance for power system planning. With the advent of deep learning techniques, it is possible to forecast solar irradiance accurately for a longer time. In this paper, day-ahead solar irradiance is forecasted using encoder-decoder sequence-to-sequence models with attention mechanism. This study formulates the problem as structured multivariate forecasting and comprehensive experiments are made with the data collected from National Solar Radiation Database (NSRDB). Two error metrics are adopted to measure the errors of encoder-decoder sequence-to-sequence model and compared with smart persistence (SP), back propagation neural network (BPNN), recurrent neural network (RNN), long short term memory (LSTM) and encoder-decoder sequence-to-sequence LSTM with attention mechanism (Enc-Dec-LSTM). Compared with SP, BPNN and RNN, Enc-Dec-LSTM is more accurate and has reduced forecast error of 31.1%, 19.3% and 8.5% respectively for day-ahead solar irradiance forecast with 31.07% as forecast skill.</p>
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Yolchuyeva, Sevinj, Géza Németh, and Bálint Gyires-Tóth. "Grapheme-to-Phoneme Conversion with Convolutional Neural Networks." Applied Sciences 9, no. 6 (March 18, 2019): 1143. http://dx.doi.org/10.3390/app9061143.

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Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form. It has a highly essential role for natural language processing, text-to-speech synthesis and automatic speech recognition systems. In this paper, we investigate convolutional neural networks (CNN) for G2P conversion. We propose a novel CNN-based sequence-to-sequence (seq2seq) architecture for G2P conversion. Our approach includes an end-to-end CNN G2P conversion with residual connections and, furthermore, a model that utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. We compare our approach with state-of-the-art methods, including Encoder-Decoder LSTM and Encoder-Decoder Bi-LSTM. Training and inference times, phoneme and word error rates were evaluated on the public CMUDict dataset for US English, and the best performing convolutional neural network-based architecture was also evaluated on the NetTalk dataset. Our method approaches the accuracy of previous state-of-the-art results in terms of phoneme error rate.
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Zhou, Shengwen, Shunsheng Guo, Baigang Du, Shuo Huang, and Jun Guo. "A Hybrid Framework for Multivariate Time Series Forecasting of Daily Urban Water Demand Using Attention-Based Convolutional Neural Network and Long Short-Term Memory Network." Sustainability 14, no. 17 (September 5, 2022): 11086. http://dx.doi.org/10.3390/su141711086.

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Urban water demand forecasting is beneficial for reducing the waste of water resources and enhancing environmental protection in sustainable water management. However, it is a challenging task to accurately predict water demand affected by a range of factors with nonlinear and uncertainty temporal patterns. This paper proposes a new hybrid framework for urban daily water demand with multiple variables, called the attention-based CNN-LSTM model, which combines convolutional neural network (CNN), long short-term memory (LSTM), attention mechanism (AM), and encoder-decoder network. CNN layers are used to learn the representation and correlation between multivariate variables. LSTM layers are utilized as the building blocks of the encoder-decoder network to capture temporal characteristics from the input sequence, while AM is introduced to the encoder-decoder network to assign corresponding attention according to the importance of water demand multivariable time series at different times. The new hybrid framework considers correlation between multiple variables and neglects irrelevant data points, which helps to improve the prediction accuracy of multivariable time series. The proposed model is contrasted with the LSTM model, the CNN-LSTM model, and the attention-based LSTM to predict the daily water demand time series in Suzhou, China. The results show that the hybrid model achieves higher prediction performance with the smallest mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), and largest correlation coefficient (R2).
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Geng, Yaogang, Hongyan Mei, Xiaorong Xue, and Xing Zhang. "Image-Caption Model Based on Fusion Feature." Applied Sciences 12, no. 19 (September 30, 2022): 9861. http://dx.doi.org/10.3390/app12199861.

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The encoder–decoder framework is the main frame of image captioning. The convolutional neural network (CNN) is usually used to extract grid-level features of the image, and the graph convolutional neural network (GCN) is used to extract the image’s region-level features. Grid-level features are poor in semantic information, such as the relationship and location of objects, while regional features lack fine-grained information about images. To address this problem, this paper proposes a fusion-features-based image-captioning model, which includes the fusion feature encoder and LSTM decoder. The fusion-feature encoder is divided into grid-level feature encoder and region-level feature encoder. The grid-level feature encoder is a convoluted neural network embedded in squeeze and excitation operations so that the model can focus on features that are highly correlated to the title. The region-level encoder employs node-embedding matrices to enable models to understand different node types and gain richer semantics. Then the features are weighted together by an attention mechanism to guide the decoder LSTM to generate an image caption. Our model was trained and tested in the MS COCO2014 dataset with the experimental evaluation standard Bleu-4 score and CIDEr score of 0.399 and 1.311, respectively. The experimental results indicate that the model can describe the image in detail.
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Zhang, Wei, Shangmin Luan, and Liqin Tian. "A Rapid Combined Model for Automatic Generating Web UI Codes." Wireless Communications and Mobile Computing 2022 (February 8, 2022): 1–10. http://dx.doi.org/10.1155/2022/4415479.

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Encoder-Decoder network is usually applied to image caption to automatically generate descriptive text for a picture. Web user interface (Web UI) is a special type of image and is usually described by HTML (hypertext marked language). Consequently, it becomes possible to use the encoder-decoder network to generate the corresponding code from a screenshot of Web UI. The basic structure of the decoder is RNN, LSTM, GRU, or other recurrent neural networks. However, this kind of decoder needs a long training time, so it increases the time complexity of training and prediction. The HTML language is a typically structured language to describe the Web UI, but it is hard to express the timing characteristics of the word sequence and the complex context. To resolve these problems efficiently, a rapid combined model RCM (rapid combined model) is designed in this paper. The basic structure of the RCM is an encoder-decoder network. The word embedding matrix and visual model are included in the encoder. The word embedding matrix uses fully connected units. Compared with LSTM, the accuracy of the word embedding matrix is basically unchanged, but the training and prediction speed have been significantly improved. In the visual model, the pretrained InceptionV3 network is used to generate the image vector, which not only improves the quality of the recognition of the Web UI interface image but also reduces the training time of the RCM significantly. In the decoder, the word embedding vector and the image vector are integrated together and input into the prediction model for word prediction.
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Luo, Tao, Xudong Cao, Jin Li, Kun Dong, Rui Zhang, and Xueliang Wei. "Multi-task prediction model based on ConvLSTM and encoder-decoder." Intelligent Data Analysis 25, no. 2 (March 4, 2021): 359–82. http://dx.doi.org/10.3233/ida-194969.

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The energy load data in the micro-energy network are a time series with sequential and nonlinear characteristics. This paper proposes a model based on the encode-decode architecture and ConvLSTM for multi-scale prediction of multi-energy loads in the micro-energy network. We apply ConvLSTM, LSTM, attention mechanism and multi-task learning concepts to construct a model specifically for processing the energy load forecasting of the micro-energy network. In this paper, ConvLSTM is used to encode the input time series. The attention mechanism is used to assign different weights to the features, which are subsequently decoded by the decoder LSTM layer. Finally, the fully connected layer interprets the output. This model is applied to forecast the multi-energy load data of the micro-energy network in a certain area of Northwest China. The test results prove that our model is convergent, and the evaluation index value of the model is better than that of the multi-task FC-LSTM and the single-task FC-LSTM. In particular, the application of the attention mechanism makes the model converge faster and with higher precision.
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Thakare, Abhijeet Ramesh, and Preeti Voditel. "Extractive Text Summarization Using LSTM-Based Encoder-Decoder Classification." ECS Transactions 107, no. 1 (April 24, 2022): 11665–72. http://dx.doi.org/10.1149/10701.11665ecst.

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Nowadays, text summarization is one of the important areas to be focused on. As the World Wide Web is growing, a huge amount of text articles (especially blogs, scientific articles) are also generated on the internet. Automatic text summarization is one of the important techniques to shorten the original text in such a way that shorten or summarized text covers incisive and meaningful sentences of original huge text. Extractive summarization extracts important sentences from original documents and then aggregates all these sentences to generate the summary. We have proposed a novel LSTM based encoder-decoder, which plays a vital role in the extractive text summarization process. CNN news article dataset is utilized for training our model. Our model is evaluated on standard metrics like Gold Standard, Recall Oriented Understudy for Gisting Evaluation (ROUGHE)-1, and ROUGHE-2. After evaluation, our model achieved an average F1-Score of 0.8353. Our model also outperformed other models available in the literature.
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Oveisi, Shahrzad, Ali Moeini, and sayeh Mirzaei. "LSTM Encoder-Decoder Dropout Model in Software Reliability Prediction." International Journal of Reliability, Risk and Safety: Theory and Application 4, no. 2 (December 1, 2021): 1–12. http://dx.doi.org/10.30699/ijrrs.4.2.1.

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Kapočiūtė-Dzikienė, Jurgita. "A Domain-Specific Generative Chatbot Trained from Little Data." Applied Sciences 10, no. 7 (March 25, 2020): 2221. http://dx.doi.org/10.3390/app10072221.

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Accurate generative chatbots are usually trained on large datasets of question–answer pairs. Despite such datasets not existing for some languages, it does not reduce the need for companies to have chatbot technology in their websites. However, companies usually own small domain-specific datasets (at least in the form of an FAQ) about their products, services, or used technologies. In this research, we seek effective solutions to create generative seq2seq-based chatbots from very small data. Since experiments are carried out in English and morphologically complex Lithuanian languages, we have an opportunity to compare results for languages with very different characteristics. We experimentally explore three encoder–decoder LSTM-based approaches (simple LSTM, stacked LSTM, and BiLSTM), three word embedding types (one-hot encoding, fastText, and BERT embeddings), and five encoder–decoder architectures based on different encoder and decoder vectorization units. Furthermore, all offered approaches are applied to the pre-processed datasets with removed and separated punctuation. The experimental investigation revealed the advantages of the stacked LSTM and BiLSTM encoder architectures and BERT embedding vectorization (especially for the encoder). The best achieved BLUE on English/Lithuanian datasets with removed and separated punctuation was ~0.513/~0.505 and ~0.488/~0.439, respectively. Better results were achieved with the English language, because generating different inflection forms for the morphologically complex Lithuanian is a harder task. The BLUE scores fell into the range defining the quality of the generated answers as good or very good for both languages. This research was performed with very small datasets having little variety in covered topics, which makes this research not only more difficult, but also more interesting. Moreover, to our knowledge, it is the first attempt to train generative chatbots for a morphologically complex language.
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Dissertations / Theses on the topic "Encoder-Decoder LSTM"

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Kumbala, Bharadwaj Reddy. "Predictive Maintenance of NOx Sensor using Deep Learning : Time series prediction with encoder-decoder LSTM." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18668.

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In automotive industry there is a growing need for predicting the failure of a component, to achieve the cost saving and customer satisfaction. As failure in a component leads to the work breakdown for the customer. This paper describes an effort in making a prediction failure monitoring model for NOx sensor in trucks. It is a component that used to measure the level of nitrogen oxide emission from the truck. The NOx sensor has chosen because its failure leads to the slowdown of engine efficiency and it is fragile and costly to replace. The data from a good and contaminated NOx sensor which is collated from the test rigs is used the input to the model. This work in this paper shows approach of complementing the Deep Learning models with Machine Learning algorithm to achieve the results. In this work LSTMs are used to detect the gain in NOx sensor and Encoder-Decoder LSTM is used to predict the variables. On top of it Multiple Linear Regression model is used to achieve the end results. The performance of the monitoring model is promising. The approach described in this paper is a general model and not specific to this component, but also can be used for other sensors too as it has a universal kind of approach.
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Nawaz, Sabeen. "Analysis of Transactional Data with Long Short-Term Memory Recurrent Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281282.

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An issue authorities and banks face is fraud related to payments and transactions where huge monetary losses occur to a party or where money laundering schemes are carried out. Previous work in the field of machine learning for fraud detection has addressed the issue as a supervised learning problem. In this thesis, we propose a model which can be used in a fraud detection system with transactions and payments that are unlabeled. The proposed modelis a Long Short-term Memory in an auto-encoder decoder network (LSTMAED)which is trained and tested on transformed data. The data is transformed by reducing it to Principal Components and clustering it with K-means. The model is trained to reconstruct the sequence with high accuracy. Our results indicate that the LSTM-AED performs better than a random sequence generating process in learning and reconstructing a sequence of payments. We also found that huge a loss of information occurs in the pre-processing stages.
Obehöriga transaktioner och bedrägerier i betalningar kan leda till stora ekonomiska förluster för banker och myndigheter. Inom maskininlärning har detta problem tidigare hanterats med hjälp av klassifierare via supervised learning. I detta examensarbete föreslår vi en modell som kan användas i ett system för att upptäcka bedrägerier. Modellen appliceras på omärkt data med många olika variabler. Modellen som används är en Long Short-term memory i en auto-encoder decoder nätverk. Datan transformeras med PCA och klustras med K-means. Modellen tränas till att rekonstruera en sekvens av betalningar med hög noggrannhet. Vår resultat visar att LSTM-AED presterar bättre än en modell som endast gissar nästa punkt i sekvensen. Resultatet visar också att mycket information i datan går förlorad när den förbehandlas och transformeras.
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Holcner, Jonáš. "Strojový překlad pomocí umělých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-386020.

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The goal of this thesis is to describe and build a system for neural machine translation. System is built with recurrent neural networks - encoder-decoder architecture in particular. The result is a nmt library used to conduct experiments with different model parameters. Results of the experiments are compared with system built with the statistical tool Moses.
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Nina, Oliver A. Nina. "A Multitask Learning Encoder-N-Decoder Framework for Movie and Video Description." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531996548147165.

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Zaroug, Abdelrahman. "Machine Learning Model for the Prediction of Human Movement Biomechanics." Thesis, 2021. https://vuir.vu.edu.au/42489/.

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An increasingly useful application of machine learning (ML) is in predicting features of human actions. If it can be shown that algorithm inputs related to actual movement mechanics can predict a limb or limb segment’s future trajectory, a range of apparently intractable problems in movement science could be solved. The forecasting of lower limb trajectories can anticipate movement characteristics that may predict the risk of tripping, slipping or balance loss. Particularly in the design of human augmentation technology such as the exoskeleton, human movement prediction will improve the synchronisation between the user and the device greatly enhancing its efficacy. Long Short Term Memory (LSTM) neural neworks are a subset of ML algoithms that proven a wide success in modelling the human movement data. The aim of this thesis was to examine four LSTM neural nework architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). This work also aims to investigate whether linear statistical methods such as the Linear Regression (LR) is enough to predict the trajectories of lower limb kinematics. Kinematics data (LA and AV) of foot, shank and thigh were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at 4 different walking speeds on a 0% gradient treadmill. Walking -1 -1 speeds included preferred walking speed (PWS 4.34 ± 0.43 km.h ), imposed speed (5km.h , 15.4% ± 7.6% faster), slower speed (-20% PWS 3.59 ± 0.47 km.h-1) and faster speed (+20% PWS 5.26 ± 0.53 km.h-1). The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 17,638 strides for all trials. The aim and findings of this work were carried out in 3 studies. Study 1 confirmed the possibility of predicting the future trajectories of human lower limb kinematics using LSTM autoencoders (ED-LSTM) and the LR during an imposed walking speed (5km.h-1). Both models achieved satisfactory predicted trajectories up to 0.06s. A prediction horizon of 0.06s can be used to compensate for delays in an exoskeleton’s feed-forward controller to better estimate the human motions and synchronise with intended movement trajectories. Study 2 (Chapter 4) indicated that the LR model is not suitable for the prediction of future lower limb kinematics at PWS. The LSTM perfromace results suggested that the ED-LSTM and the Stacked LSTM are more accurate to predict the future lower limb kinematics up to 0.1s at PWS and imposed walking speed (5km.h-1). The average duration for a gait cycle rages between 0.98-1.07s, and a prediction horizon of 0.1 accounts for about 10% of the gait cycle. Such a forecast may assist users in anticipating a low foot clearance to develop early countermeasures such as slowing down or stopping. Study 3 (Chapter 5) have shown that at +20% PWS the LSTM models’ performance obtained better predictions compared to all tested walking speed conditions (i.e. PWS, -20% PWS and 5km.h-1). While at -20% PWS, results indicated that at slower walking speeds all of the LSTM architectures obtained weaker predictions compared to all tested walking speeds (i.e. PWS, +20% PWS and 5km.h-1). In addition to the applications of a known future trajectories at the PWS mentioned in study 1 and 2, the prediction at fast and slow walking speeds familiarise the developed ML models with changes in human walking speed which are known to have large effects on lower limb kinematics. When intelligent ML methods are familiarised with the degree of kinematic changes due to speed variations, it could be used to improve human-machine interface in bionics design for various walking speeds The key finding of the three studies is that the ED-LSTM was found to be the most accurate -1 model to predict and adapt to the human motion kinematics at PWS, ±20% PWS and 5km.h up to 0.1s. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing better human-machine interface and mitigating the risk of tripping and balance loss.
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Book chapters on the topic "Encoder-Decoder LSTM"

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Jena, Om Prakash, Alok Ranjan Tripathy, Sudhansu Sekhar Patra, Manas Ranjan Chowdhury, and Rajesh Kumar Sahoo. "Automatic Text Simplification Using LSTM Encoder Decoder Model." In Lecture Notes in Networks and Systems, 235–49. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4807-6_23.

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Park, Dongju, and Chang Wook Ahn. "LSTM Encoder-Decoder with Adversarial Network for Text Generation from Keyword." In Communications in Computer and Information Science, 388–96. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2829-9_35.

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Li, Xiaoxu, Chensi Mao, Shiliang Huang, and Zhongfu Ye. "Chinese Sign Language Recognition Based on SHS Descriptor and Encoder-Decoder LSTM Model." In Biometric Recognition, 719–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_77.

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Cui, Shengmin, Xiaowa Yong, Sanghwan Kim, Seokjoon Hong, and Inwhee Joe. "An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries." In Intelligent Algorithms in Software Engineering, 178–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51965-0_15.

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Huang, Xiuqi, Yunlong Cheng, Xiaofeng Gao, and Guihai Chen. "TEALED: A Multi-Step Workload Forecasting Approach Using Time-Sensitive EMD and Auto LSTM Encoder-Decoder." In Database Systems for Advanced Applications, 706–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00126-0_55.

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Huang, Xiuqi, Yunlong Cheng, Xiaofeng Gao, and Guihai Chen. "TEALED: A Multi-Step Workload Forecasting Approach Using Time-Sensitive EMD and Auto LSTM Encoder-Decoder." In Database Systems for Advanced Applications, 706–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00126-0_55.

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Kundu, Ritwik, Shaurya Singh, Geraldine Amali, Mathew Mithra Noel, and Umadevi K. S. "Automatic Image Captioning Using Different Variants of the Long Short-Term Memory (LSTM) Deep Learning Model." In Deep Learning Research Applications for Natural Language Processing, 132–55. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6001-6.ch008.

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Today's world is full of digital images; however, the context is unavailable most of the time. Thus, image captioning is quintessential for providing the content of an image. Besides generating accurate captions, the image captioning model must also be scalable. In this chapter, two variants of long short-term memory (LSTM), namely stacked LSTM and BiLSTM along with convolutional neural networks (CNN) have been used to implement the Encoder-Decoder model for generating captions. Bilingual evaluation understudy (BLEU) score metric is used to evaluate the performance of these two bi-layered models. From the study, it was observed that both the models were on par when it came to performance. Some resulted in low BLEU scores suggesting that the predicted caption was dissimilar to the actual caption whereas some very high BLEU scores suggested that the model was able to predict captions almost similar to human. Furthermore, it was found that the bidirectional LSTM model is more computationally intensive and requires more time to train than the stacked LSTM model owing to its complex architecture.
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Conference papers on the topic "Encoder-Decoder LSTM"

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Fenghour, Souheil, Daqing Chen, and Perry Xiao. "Decoder-Encoder LSTM for Lip Reading." In ICSIE '19: 2019 8th International Conference on Software and Information Engineering. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3328833.3328845.

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Bennemann de Moura, Gustavo, and Valeria Delisandra Feltrim. "Using LSTM Encoder-Decoder for Rhetorical Structure Prediction." In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2018. http://dx.doi.org/10.1109/bracis.2018.00055.

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Li, Ye, and Hongxiang Ren. "Vessel Traffic Flow Prediction Using LSTM Encoder-Decoder." In SPML 2022: 2022 5th International Conference on Signal Processing and Machine Learning. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3556384.3556385.

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Xue, Peixin, Jianyi Liu, Shitao Chen, Zhuoli Zhou, Yongbo Huo, and Nanning Zheng. "Crossing-Road Pedestrian Trajectory Prediction via Encoder-Decoder LSTM." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917510.

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Du, Shengdong, Tianrui Li, Yan Yang, Xun Gong, and Shi-Jinn Horng. "An LSTM based Encoder-Decoder Model for MultiStep Traffic Flow Prediction." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851928.

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Kurata, Gakuto, Bing Xiang, and Bowen Zhou. "Labeled Data Generation with Encoder-Decoder LSTM for Semantic Slot Filling." In Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-727.

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Zeyer, Albert, Parnia Bahar, Kazuki Irie, Ralf Schluter, and Hermann Ney. "A Comparison of Transformer and LSTM Encoder Decoder Models for ASR." In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2019. http://dx.doi.org/10.1109/asru46091.2019.9004025.

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Zhao, Wei, Benyou Wang, Jianbo Ye, Min Yang, Zhou Zhao, Ruotian Luo, and Yu Qiao. "A Multi-task Learning Approach for Image Captioning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/168.

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In this paper, we propose a Multi-task Learning Approach for Image Captioning (MLAIC ), motivated by the fact that humans have no difficulty performing such task because they possess capabilities of multiple domains. Specifically, MLAIC consists of three key components: (i) A multi-object classification model that learns rich category-aware image representations using a CNN image encoder; (ii) A syntax generation model that learns better syntax-aware LSTM based decoder; (iii) An image captioning model that generates image descriptions in text, sharing its CNN encoder and LSTM decoder with the object classification task and the syntax generation task, respectively. In particular, the image captioning model can benefit from the additional object categorization and syntax knowledge. To verify the effectiveness of our approach, we conduct extensive experiments on MS-COCO dataset. The experimental results demonstrate that our model achieves impressive results compared to other strong competitors.
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Park, Seong Hyeon, ByeongDo Kim, Chang Mook Kang, Chung Choo Chung, and Jun Won Choi. "Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture." In 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2018. http://dx.doi.org/10.1109/ivs.2018.8500658.

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Li, Nisi, Lin Lin, and Fan Li. "ADS-B Anomaly Data Detection Using SVDD-based LSTM Encoder-Decoder Algorithm." In 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2021. http://dx.doi.org/10.1109/iccasit53235.2021.9633438.

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