Relevant bibliographies by topics / Attention LSTM

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

Academic literature on the topic 'Attention LSTM'

Author: Grafiati
Published: 8 July 2021
Last updated: 17 February 2022

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Journal articles on the topic "Attention LSTM"

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Li, Youru, Zhenfeng Zhu, Deqiang Kong, Hua Han, and Yao Zhao. "EA-LSTM: Evolutionary attention-based LSTM for time series prediction." Knowledge-Based Systems 181 (October 2019): 104785. http://dx.doi.org/10.1016/j.knosys.2019.05.028.

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Huang, Zhongzhan, Senwei Liang, Mingfu Liang, and Haizhao Yang. "DIANet: Dense-and-Implicit Attention Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4206–14. http://dx.doi.org/10.1609/aaai.v34i04.5842.

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Attention networks have successfully boosted the performance in various vision problems. Previous works lay emphasis on designing a new attention module and individually plug them into the networks. Our paper proposes a novel-and-simple framework that shares an attention module throughout different network layers to encourage the integration of layer-wise information and this parameter-sharing module is referred to as Dense-and-Implicit-Attention (DIA) unit. Many choices of modules can be used in the DIA unit. Since Long Short Term Memory (LSTM) has a capacity of capturing long-distance dependency, we focus on the case when the DIA unit is the modified LSTM (called DIA-LSTM). Experiments on benchmark datasets show that the DIA-LSTM unit is capable of emphasizing layer-wise feature interrelation and leads to significant improvement of image classification accuracy. We further empirically show that the DIA-LSTM has a strong regularization ability on stabilizing the training of deep networks by the experiments with the removal of skip connections (He et al. 2016a) or Batch Normalization (Ioffe and Szegedy 2015) in the whole residual network.
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Wang, Hao, Xiaofang Zhang, Bin Liang, Qian Zhou, and Baowen Xu. "Gated Hierarchical LSTMs for Target-Based Sentiment Analysis." International Journal of Software Engineering and Knowledge Engineering 28, no. 11n12 (November 2018): 1719–37. http://dx.doi.org/10.1142/s0218194018400259.

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In the field of target-based sentiment analysis, the deep neural model combining attention mechanism is a remarkable success. In current research, it is commonly seen that attention mechanism is combined with Long Short-Term Memory (LSTM) networks. However, such neural network-based architectures generally rely on complex computation and only focus on single target. In this paper, we propose a gated hierarchical LSTM (GH-LSTMs) model which combines regional LSTM and sentence-level LSTM via a gated operation for the task of target-based sentiment analysis. This approach can distinguish different polarities of sentiment of different targets in the same sentence through a regional LSTM. Furthermore, it is able to concentrate on the long-distance dependency of target in the whole sentence via a sentence-level LSTM. The final results of our experiments on multi-domain datasets of two languages from SemEval 2016 indicate that our approach yields better performance than Support Vector Machine (SVM) and several typical neural network models. A case study of some typical examples also makes a supplement to this conclusion.
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Feng, Kaicheng, and Xiaobing Liu. "Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction." Complexity 2020 (December 7, 2020): 1–9. http://dx.doi.org/10.1155/2020/6689304.

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To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM (Long Short-Term Memory) box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment.
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Jo, Youngki, and Hyunsoo Lee. "Electricity Demand Forecasting Framework using Modified Attention-based LSTM." Journal of Korean Institute of Intelligent Systems 30, no. 3 (June 30, 2020): 242–50. http://dx.doi.org/10.5391/jkiis.2020.30.3.242.

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Gallardo-Antolín, Ascensión, and Juan M. Montero. "Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework." Applied Sciences 11, no. 14 (July 11, 2021): 6393. http://dx.doi.org/10.3390/app11146393.

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The automatic detection of deceptive behaviors has recently attracted the attention of the research community due to the variety of areas where it can play a crucial role, such as security or criminology. This work is focused on the development of an automatic deception detection system based on gaze and speech features. The first contribution of our research on this topic is the use of attention Long Short-Term Memory (LSTM) networks for single-modal systems with frame-level features as input. In the second contribution, we propose a multimodal system that combines the gaze and speech modalities into the LSTM architecture using two different combination strategies: Late Fusion and Attention-Pooling Fusion. The proposed models are evaluated over the Bag-of-Lies dataset, a multimodal database recorded in real conditions. On the one hand, results show that attentional LSTM networks are able to adequately model the gaze and speech feature sequences, outperforming a reference Support Vector Machine (SVM)-based system with compact features. On the other hand, both combination strategies produce better results than the single-modal systems and the multimodal reference system, suggesting that gaze and speech modalities carry complementary information for the task of deception detection that can be effectively exploited by using LSTMs.
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Zhang, Xuan, Xun Liang, Aakas Zhiyuli, Shusen Zhang, Rui Xu, and Bo Wu. "AT-LSTM: An Attention-based LSTM Model for Financial Time Series Prediction." IOP Conference Series: Materials Science and Engineering 569 (August 9, 2019): 052037. http://dx.doi.org/10.1088/1757-899x/569/5/052037.

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Yin, Helin, Dong Jin, Yeong Hyeon Gu, Chang Jin Park, Sang Keun Han, and Seong Joon Yoo. "STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM." Agriculture 10, no. 12 (December 8, 2020): 612. http://dx.doi.org/10.3390/agriculture10120612.

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It is difficult to forecast vegetable prices because they are affected by numerous factors, such as weather and crop production, and the time-series data have strong non-linear and non-stationary characteristics. To address these issues, we propose the STL-ATTLSTM (STL-Attention-based LSTM) model, which integrates the seasonal trend decomposition using the Loess (STL) preprocessing method and attention mechanism based on long short-term memory (LSTM). The proposed STL-ATTLSTM forecasts monthly vegetable prices using various types of information, such as vegetable prices, weather information of the main production areas, and market trading volumes. The STL method decomposes time-series vegetable price data into trend, seasonality, and remainder components. It uses the remainder component by removing the trend and seasonality components. In the model training process, attention weights are assigned to all input variables; thus, the model’s prediction performance is improved by focusing on the variables that affect the prediction results. The proposed STL-ATTLSTM was applied to five crops, namely cabbage, radish, onion, hot pepper, and garlic, and its performance was compared to three benchmark models (i.e., LSTM, attention LSTM, and STL-LSTM). The performance results show that the LSTM model combined with the STL method (STL-LSTM) achieved a 12% higher prediction accuracy than the attention LSTM model that did not use the STL method and solved the prediction lag arising from high seasonality. The attention LSTM model improved the prediction accuracy by approximately 4% to 5% compared to the LSTM model. The STL-ATTLSTM model achieved the best performance, with an average root mean square error (RMSE) of 380, and an average mean absolute percentage error (MAPE) of 7%.
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Yang, Zhan, Chengliang Li, Zhongying Zhao, and Chao Li. "Sentiment classification based on dependency-relationship embedding and attention mechanism." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 867–77. http://dx.doi.org/10.3233/jifs-202747.

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Aspect-based sentiment classification, a fine-grained sentiment analysis task, aims to predict the sentiment polarity for a specified aspect. However, the existing aspect-based sentiment classification approaches cannot fully model the dependency-relationship between words and are easily disturbed by irrelevant aspects. To address this problem, we propose a novel approach named Dependency-Relationship Embedding and Attention Mechanism-based LSTM. DA-LSTM first merges the word hidden vector output by LSTM with the dependency-relationship embedding to form a combined vector. This vector is then fed into the attention mechanism together with the aspect information which can avoid interference to calculate the final word representation for sentiment classification. Our extensive experiments on benchmark data sets clearly show the effectiveness of DA-LSTM.
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Kim, Hong-In, and Rae-Hong Park. "Residual LSTM Attention Network for Object Tracking." IEEE Signal Processing Letters 25, no. 7 (July 2018): 1029–33. http://dx.doi.org/10.1109/lsp.2018.2835768.

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Dissertations / Theses on the topic "Attention LSTM"

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Singh, J. P., A. Kumar, Nripendra P. Rana, and Y. K. Dwivedi. "Attention-based LSTM network for rumor veracity estimation of tweets." Springer, 2020. http://hdl.handle.net/10454/17942.

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Yes
Twitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM model is compared with several conventional machine and deep learning models. The proposed Attention-based LSTM model achieved an F1-score of 0.88 in classifying rumor and non-rumor tweets, which is better than the state-of-the-art results. The proposed system can reduce the impact of rumors on society and weaken the loss of life, money, and build the firm trust of users with social media platforms.
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Kindbom, Hannes. "Investigating the Attribution Quality of LSTM with Attention and SHAP : Going Beyond Predictive Performance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302412.

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Estimating each marketing channel’s impact on conversion can help advertisers develop strategies and spend their marketing budgets optimally. This problem is often referred to as attribution modelling, and it is gaining increasing attention in both the industry and academia as access to online tracking data improves. Focusing on achieving higher predictive performance, the Long Short- Term Memory (LSTM) architecture is currently trending as a data-driven solution to attribution modelling. However, such deep neural networks have been criticised for being difficult to interpret. Interpretability is critical, since channel attributions are generally obtained by studying how a model makes a binary conversion prediction given a sequence of clicks or views of ads in different channels. Therefore, this degree project studies and compares the quality of LSTM attributions, calculated with SHapleyAdditive exPlanations (SHAP), attention and fractional scores to three baseline models. The fractional score is the mean difference in a model’s predicted conversion probability with and without a channel. Furthermore, a synthetic data generator based on a Poisson process is developed and validated against real data to measure attribution quality as the Mean Absolute Error (MAE) between calculated attributions and the true causal relationships between channel clicks and conversions. The experimental results demonstrate that the quality of attributions is not unambiguously reflected by the predictive performance of LSTMs. In general, it is not possible to assume a high attribution quality solely based on high predictive performance. For example, all models achieve ~82% accuracy on real data, whereas LSTM Fractional and SHAP produce the lowest attribution quality of 0:0566 and 0:0311 MAE respectively. This can be compared to an improved MAE of 0:0058, which is obtained with a Last-Touch Attribution (LTA) model. The attribution quality also varies significantly depending on which attribution calculation method is used for the LSTM. This suggests that the ongoing quest for improved accuracy may be questioned and that it is not always justified to use an LSTM when aiming for high quality attributions.
Genom att estimera påverkan varje marknadsföringskanal har på konverteringar, kan annonsörer utveckla strategier och spendera sina marknadsföringsbudgetar optimalt. Det här kallas ofta attributionsmodellering och det får alltmer uppmärksamhet i både näringslivet och akademin när tillgången till spårningsinformation ökar online. Med fokus på att uppnå högre prediktiv prestanda är Long Short-Term Memory (LSTM) för närvarande en populär datadriven lösning inom attributionsmodellering. Sådana djupa neurala nätverk har dock kritiserats för att vara svårtolkade. Tolkningsbarhet är viktigt, då kanalattributioner generellt fås genom att studera hur en modell gör en binär konverteringsprediktering givet en sekvens av klick eller visningar av annonser i olika kanaler. Det här examensarbetet studerar och jämför därför kvaliteten av en LSTMs attributioner, beräknade med SHapley Additive exPlanations (SHAP), attention och fractional scores mot tre grundmodeller. Fractional scores beräknas som medelvärdesdifferensen av en modells predikterade konverteringssannolikhet med och utan en viss kanal. Därutöver utvecklas en syntetisk datagenerator baserad på en Poissonprocess, vilken valideras mot verklig data. Generatorn används för att kunna mäta attributionskvalitet som Mean Absolute Error (MAE) mellan beräknade attributioner och de verkliga kausala sambanden mellan kanalklick och konverteringar. De experimentella resultaten visar att attributionskvaliteten inte entydigt avspeglas av en LSTMs prediktiva prestanda. Det är generellt inte möjligt att anta en hög attributionskvalitet enbart baserat på en hög prediktiv prestanda. Alla modeller uppnår exempelvis ~82% prediktiv träffsäkerhet på verklig data, medan LSTM Fractional och SHAP ger den lägsta attributionskvaliteten på 0:0566 respektive 0:0311 MAE. Det här kan jämföras mot en förbättrad MAE på 0:0058, som erhålls med en Last-touch-modell. Kvaliteten på attributioner varierar också signifikant beroende på vilket metod för attributionsberäkning som används för LSTM. Det här antyder att den pågående strävan efter högre prediktiv träffsäkerhet kan ifrågasättas och att det inte alltid är berättigat att använda en LSTM när attributioner av hög kvalitet eftersträvas.
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Forch, Valentin, Julien Vitay, and Fred H. Hamker. "Recurrent Spatial Attention for Facial Emotion Recognition." Technische Universität Chemnitz, 2020. https://monarch.qucosa.de/id/qucosa%3A72453.

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Automatic processing of emotion information through deep neural networks (DNN) can have great benefits (e.g., for human-machine interaction). Vice versa, machine learning can profit from concepts known from human information processing (e.g., visual attention). We employed a recurrent DNN incorporating a spatial attention mechanism for facial emotion recognition (FER) and compared the output of the network with results from human experiments. The attention mechanism enabled the network to select relevant face regions to achieve state-of-the-art performance on a FER database containing images from realistic settings. A visual search strategy showing some similarities with human saccading behavior emerged when the model’s perceptive capabilities were restricted. However, the model then failed to form a useful scene representation.
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Bopaiah, Jeevith. "A recurrent neural network architecture for biomedical event trigger classification." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/73.

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A “biomedical event” is a broad term used to describe the roles and interactions between entities (such as proteins, genes and cells) in a biological system. The task of biomedical event extraction aims at identifying and extracting these events from unstructured texts. An important component in the early stage of the task is biomedical trigger classification which involves identifying and classifying words/phrases that indicate an event. In this thesis, we present our work on biomedical trigger classification developed using the multi-level event extraction dataset. We restrict the scope of our classification to 19 biomedical event types grouped under four broad categories - Anatomical, Molecular, General and Planned. While most of the existing approaches are based on traditional machine learning algorithms which require extensive feature engineering, our model relies on neural networks to implicitly learn important features directly from the text. We use natural language processing techniques to transform the text into vectorized inputs that can be used in a neural network architecture. As per our knowledge, this is the first time neural attention strategies are being explored in the area of biomedical trigger classification. Our best results were obtained from an ensemble of 50 models which produced a micro F-score of 79.82%, an improvement of 1.3% over the previous best score.
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Soncini, Filippo. "Classificazione di documenti tramite reti neurali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20509/.

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Questo elaborato è stato proposto con l’obbiettivo di affrontare il problema della classificazione di documenti utilizzando sia contenuti visivi che testuali, cercando di analizzare diverse reti e diverse combinazioni di esse per poi sviluppare un modello personalizzato.
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Näslund, Per. "Artificial Neural Networks in Swedish Speech Synthesis." Thesis, KTH, Tal-kommunikation, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239350.

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Text-to-speech (TTS) systems have entered our daily lives in the form of smart assistants and many other applications. Contemporary re- search applies machine learning and artificial neural networks (ANNs) to synthesize speech. It has been shown that these systems outperform the older concatenative and parametric methods. In this paper, ANN-based methods for speech synthesis are ex- plored and one of the methods is implemented for the Swedish lan- guage. The implemented method is dubbed “Tacotron” and is a first step towards end-to-end ANN-based TTS which puts many differ- ent ANN-techniques to work. The resulting system is compared to a parametric TTS through a strength-of-preference test that is carried out with 20 Swedish speaking subjects. A statistically significant pref- erence for the ANN-based TTS is found. Test subjects indicate that the ANN-based TTS performs better than the parametric TTS when it comes to audio quality and naturalness but sometimes lacks in intelli- gibility.
Talsynteser, också kallat TTS (text-to-speech) används i stor utsträckning inom smarta assistenter och många andra applikationer. Samtida forskning applicerar maskininlärning och artificiella neurala nätverk (ANN) för att utföra talsyntes. Det har visats i studier att dessa system presterar bättre än de äldre konkatenativa och parametriska metoderna. I den här rapporten utforskas ANN-baserade TTS-metoder och en av metoderna implementeras för det svenska språket. Den använda metoden kallas “Tacotron” och är ett första steg mot end-to-end TTS baserat på neurala nätverk. Metoden binder samman flertalet olika ANN-tekniker. Det resulterande systemet jämförs med en parametriskt TTS genom ett graderat preferens-test som innefattar 20 svensktalande försökspersoner. En statistiskt säkerställd preferens för det ANN- baserade TTS-systemet fastställs. Försökspersonerna indikerar att det ANN-baserade TTS-systemet presterar bättre än det parametriska när det kommer till ljudkvalitet och naturlighet men visar brister inom tydlighet.
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Carman, Benjamin Andrew. "Translating LaTeX to Coq: A Recurrent Neural Network Approach to Formalizing Natural Language Proofs." Ohio University Honors Tutorial College / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors161919616626269.

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Ujihara, Rintaro. "Multi-objective optimization for model selection in music classification." Thesis, KTH, Optimeringslära och systemteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298370.

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With the breakthrough of machine learning techniques, the research concerning music emotion classification has been getting notable progress combining various audio features and state-of-the-art machine learning models. Still, it is known that the way to preprocess music samples and to choose which machine classification algorithm to use depends on data sets and the objective of each project work. The collaborating company of this thesis, Ichigoichie AB, is currently developing a system to categorize music data into positive/negative classes. To enhance the accuracy of the existing system, this project aims to figure out the best model through experiments with six audio features (Mel spectrogram, MFCC, HPSS, Onset, CENS, Tonnetz) and several machine learning models including deep neural network models for the classification task. For each model, hyperparameter tuning is performed and the model evaluation is carried out according to pareto optimality with regard to accuracy and execution time. The results show that the most promising model accomplished 95% correct classification with an execution time of less than 15 seconds.
I och med genombrottet av maskininlärningstekniker har forskning kring känsloklassificering i musik sett betydande framsteg genom att kombinera olikamusikanalysverktyg med nya maskinlärningsmodeller. Trots detta är hur man förbehandlar ljuddatat och valet av vilken maskinklassificeringsalgoritm som ska tillämpas beroende på vilken typ av data man arbetar med samt målet med projektet. Denna uppsats samarbetspartner, Ichigoichie AB, utvecklar för närvarande ett system för att kategorisera musikdata enligt positiva och negativa känslor. För att höja systemets noggrannhet är målet med denna uppsats att experimentellt hitta bästa modellen baserat på sex musik-egenskaper (Mel-spektrogram, MFCC, HPSS, Onset, CENS samt Tonnetz) och ett antal olika maskininlärningsmodeller, inklusive Deep Learning-modeller. Varje modell hyperparameteroptimeras och utvärderas enligt paretooptimalitet med hänsyn till noggrannhet och beräkningstid. Resultaten visar att den mest lovande modellen uppnådde 95% korrekt klassificering med en beräkningstid på mindre än 15 sekunder.
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GAO, SHAO-EN, and 高紹恩. "Share Price Trend Prediction Using Attention with LSTM Structure." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/n57t99.

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碩士
國立勤益科技大學
資訊工程系
107
Stock market has a considerable impact in the whole financial market.Among researches on prediction, stock price movements prediction is a quite hot topic. In this paper, stock price movements were predicted by utilizing various stock information by technical means of deep learning.The architecture based on LSTM using Attention proposed in this paper was proven through experiment to be able to effectively improve prediction accuracy． This paper uses deep learning to predict the trend of stock prices.Since the price increase of stocks is usually related to the stock price in the past, a long term short term memory LSTM based architecture is proposed. LSTM improves the long term dependence of traditional RNN, effectively improves the accuracy and stability of prediction,and improves the accuracy and stability of the network by adding Attention.
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Tseng, Po-Yen, and 曾博彥. "Android Malware Analysis Based on System Call sequences and Attention-LSTM." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/gdrth9.

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碩士
國立中央大學
資訊管理學系
107
With the popularity of Android mobile devices, detecting and protecting malicious software has become an important issue. Although there have been studies proposed that dynamic analysis can overcome the shortcomings of avoidance detection problems such as code obfuscated. However, how to learn more detail of correlation between the sequence-type features extracted by dynamic analysis to improve the resolution accuracy of the classification model is the direction of many research efforts. This study extracts the system call sequence as a feature, and extracts the system call correlation through the Long Short-Term Memory (LSTM) deep learning model. In addition, in order to avoid the increase of the length of the system call sequence and reduce the accuracy of the model classification, the attention mechanism is added to the classification model. The experimental results show that through the two-layer of Bi- LSTM architecture and the deep neural network of the Attention mechanism, the resolution of benign and malicious programs is 93.5%, and the classification of benign programs and two other malicious types is detailed. The result is an accuracy of 93.1%, showing excellent classification ability.
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Book chapters on the topic "Attention LSTM"

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Grósz, Tamás, and Mikko Kurimo. "LSTM-XL: Attention Enhanced Long-Term Memory for LSTM Cells." In Text, Speech, and Dialogue, 382–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83527-9_32.

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Yuan, Fangfang, Yanmin Shang, Yanbing Liu, Yanan Cao, and Jianlong Tan. "Attention-Based LSTM for Insider Threat Detection." In Applications and Techniques in Information Security, 192–201. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0871-4_15.

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Lebron Casas, Luis, and Eugenia Koblents. "Video Summarization with LSTM and Deep Attention Models." In MultiMedia Modeling, 67–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05716-9_6.

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Zheng, Zengwei, Lifei Shi, Chi Wang, Lin Sun, and Gang Pan. "LSTM with Uniqueness Attention for Human Activity Recognition." In Lecture Notes in Computer Science, 498–509. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30508-6_40.

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Liu, Suyuan, Wenming Zheng, Tengfei Song, and Yuan Zong. "Sparse Graphic Attention LSTM for EEG Emotion Recognition." In Communications in Computer and Information Science, 690–97. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36808-1_75.

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Weytjens, Hans, and Jochen De Weerdt. "Process Outcome Prediction: CNN vs. LSTM (with Attention)." In Business Process Management Workshops, 321–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66498-5_24.

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Li, Changliang, Changsong Li, and Pengyuan Liu. "Sentiment Analysis Based on LSTM Architecture with Emoticon Attention." In Lecture Notes in Computer Science, 232–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26142-9_21.

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Cai, Guoyong, and Hongyu Li. "Joint Attention LSTM Network for Aspect-Level Sentiment Analysis." In Lecture Notes in Computer Science, 147–57. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01012-6_12.

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Wang, Xiuling, Hao Chen, Zhoujun Li, and Zhonghua Zhao. "Unrest News Amount Prediction with Context-Aware Attention LSTM." In Lecture Notes in Computer Science, 369–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97310-4_42.

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Zhang, Kai, Weiping Ren, and Yangsen Zhang. "Attention-Based Bi-LSTM for Chinese Named Entity Recognition." In Lecture Notes in Computer Science, 643–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04015-4_56.

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Conference papers on the topic "Attention LSTM"

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Chen, Zhenzhong, and Wanjie Sun. "Scanpath Prediction for Visual Attention using IOR-ROI LSTM." 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.2018/89.

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Predicting scanpath when a certain stimulus is presented plays an important role in modeling visual attention and search. This paper presents a model that integrates convolutional neural network and long short-term memory (LSTM) to generate realistic scanpaths. The core part of the proposed model is a dual LSTM unit, i.e., an inhibition of return LSTM (IOR-LSTM) and a region of interest LSTM (ROI-LSTM), capturing IOR dynamics and gaze shift behavior simultaneously. IOR-LSTM simulates the visual working memory to adaptively integrate and forget scene information. ROI-LSTM is responsible for predicting the next ROI given the inhibited image features. Experimental results indicate that the proposed architecture can achieve superior performance in predicting scanpaths.
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Xu, Cheng, Junzhong Ji, Menglong Zhang, and Xiaodan Zhang. "Attention-gated LSTM for Image Captioning." In 2019 International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI). IEEE, 2019. http://dx.doi.org/10.1109/icusai47366.2019.9124779.

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Ahmed, Mahtab, Muhammad Rifayat Samee, and Robert E. Mercer. "Improving Tree-LSTM with Tree Attention." In 2019 IEEE 13th International Conference on Semantic Computing (ICSC). IEEE, 2019. http://dx.doi.org/10.1109/icosc.2019.8665673.

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Cheng, Weiguo, and Zhenyi Xu. "ECS Request Prediction with Attention-LSTM." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9327311.

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Zhang, Zhichao, Junyu Dong, Qilu Zhao, Lin Qi, and Shu Zhang. "Attention LSTM for Scene Graph Generation." In 2021 6th International Conference on Image, Vision and Computing (ICIVC). IEEE, 2021. http://dx.doi.org/10.1109/icivc52351.2021.9526967.

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Xing, Bowen, Lejian Liao, Dandan Song, Jingang Wang, Fuzheng Zhang, Zhongyuan Wang, and Heyan Huang. "Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/738.

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Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM.
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Guo, Jingjie, Kelang Tian, Kejiang Ye, and Cheng-Zhong Xu. "MA-LSTM: A Multi-Attention Based LSTM for Complex Pattern Extraction." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9412402.

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Song, Jingkuan, Lianli Gao, Zhao Guo, Wu Liu, Dongxiang Zhang, and Heng Tao Shen. "Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/381.

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Recent progress has been made in using attention based encoder-decoder framework for video captioning. However, most existing decoders apply the attention mechanism to every generated words including both visual words (e.g., “gun” and "shooting“) and non-visual words (e.g. "the“, "a”).However, these non-visual words can be easily predicted using natural language model without considering visual signals or attention.Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of video captioning.To address this issue, we propose a hierarchical LSTM with adjusted temporal attention (hLSTMat) approach for video captioning. Specifically, the proposed framework utilizes the temporal attention for selecting specific frames to predict related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and deep semantic information to support the video caption generation. To demonstrate the effectiveness of our proposed framework, we test our method on two prevalent datasets: MSVD and MSR-VTT, and experimental results show that our approach outperforms the state-of-the-art methods on both two datasets.
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Xie, Qi, Yongjun Wang, and Zhiquan Qin. "Malware Family Classification using LSTM with Attention." In 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020. http://dx.doi.org/10.1109/cisp-bmei51763.2020.9263499.

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Lei, Li, Ming Chen, Chengwan He, and Duojiao Li. "XSS Detection Technology Based on LSTM-Attention." In 2020 5th International Conference on Control, Robotics and Cybernetics (CRC). IEEE, 2020. http://dx.doi.org/10.1109/crc51253.2020.9253484.

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