Relevant bibliographies by topics / LSTM unit

Contents

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

Academic literature on the topic 'LSTM unit'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "LSTM unit"

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Dangovski, Rumen, Li Jing, Preslav Nakov, Mićo Tatalović, and Marin Soljačić. "Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications." Transactions of the Association for Computational Linguistics 7 (November 2019): 121–38. http://dx.doi.org/10.1162/tacl_a_00258.

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Stacking long short-term memory (LSTM) cells or gated recurrent units (GRUs) as part of a recurrent neural network (RNN) has become a standard approach to solving a number of tasks ranging from language modeling to text summarization. Although LSTMs and GRUs were designed to model long-range dependencies more accurately than conventional RNNs, they nevertheless have problems copying or recalling information from the long distant past. Here, we derive a phase-coded representation of the memory state, Rotational Unit of Memory (RUM), that unifies the concepts of unitary learning and associative
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Han, Shipeng, Zhen Meng, Xingcheng Zhang, and Yuepeng Yan. "Hybrid Deep Recurrent Neural Networks for Noise Reduction of MEMS-IMU with Static and Dynamic Conditions." Micromachines 12, no. 2 (2021): 214. http://dx.doi.org/10.3390/mi12020214.

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Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as environmental noise, electronic noise, mechanical noise and manufacturing error. These can seriously affect the application of MEMS-IMU used in different fields. Focus has been on MEMS gyro since it is an essential and, yet, complex sensor in MEMS-IMU which is very sensitive to noises and errors from the random sources. In this study, recurrent neur
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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 (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 depend
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Wang, Jianyong, Lei Zhang, Yuanyuan Chen, and Zhang Yi. "A New Delay Connection for Long Short-Term Memory Networks." International Journal of Neural Systems 28, no. 06 (2018): 1750061. http://dx.doi.org/10.1142/s0129065717500617.

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Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a more sophisticated recurrent unit, called Delay Connected LSTM (DCLSTM). The proposed delay connection brings two main merits to DCLSTM with introducing no extra parameters. First, it allows the output of the DCLSTM unit to maintain LSTM, which is a
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He, Wei, Jufeng Li, Zhihe Tang, et al. "A Novel Hybrid CNN-LSTM Scheme for Nitrogen Oxide Emission Prediction in FCC Unit." Mathematical Problems in Engineering 2020 (August 17, 2020): 1–12. http://dx.doi.org/10.1155/2020/8071810.

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Fluid Catalytic Cracking (FCC), a key unit for secondary processing of heavy oil, is one of the main pollutant emissions of NOx in refineries which can be harmful for the human health. Owing to its complex behaviour in reaction, product separation, and regeneration, it is difficult to accurately predict NOx emission during FCC process. In this paper, a novel deep learning architecture formed by integrating Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for nitrogen oxide emission prediction is proposed and validated. CNN is used to extract features among multidime
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Donoso-Oliva, C., G. Cabrera-Vives, P. Protopapas, R. Carrasco-Davis, and P. A. Estevez. "The effect of phased recurrent units in the classification of multiple catalogues of astronomical light curves." Monthly Notices of the Royal Astronomical Society 505, no. 4 (2021): 6069–84. http://dx.doi.org/10.1093/mnras/stab1598.

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ABSTRACT In the new era of very large telescopes, where data are crucial to expand scientific knowledge, we have witnessed many deep learning applications for the automatic classification of light curves. Recurrent neural networks (RNNs) are one of the models used for these applications, and the Long Short-Term Memory (LSTM) unit stands out for being an excellent choice for the representation of long time series. In general, RNNs assume observations at discrete times, which may not suit the irregular sampling of light curves. A traditional technique to address irregular sequences consists of a
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Pan, Yu, Jing Xu, Maolin Wang, et al. "Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4683–90. http://dx.doi.org/10.1609/aaai.v33i01.33014683.

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Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs very difficult. To address this challenge, we pro
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Shafqat, Wafa, and Yung-Cheol Byun. "A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model." Sustainability 12, no. 10 (2020): 4107. http://dx.doi.org/10.3390/su12104107.

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The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based con
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Wu, Beng, Wei He, Jing Wang, Huaqing Liang, and Chong Chen. "A convolutional-LSTM model for nitrogen oxide emission forecasting in FCC unit." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 1537–45. http://dx.doi.org/10.3233/jifs-192086.

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As the environment issue is put on the agenda, air pollution also concerns a lot. Nitrogen oxide (NOx) an is important factor which affects air pollution and is also the main gas emissions of the smoke and waste gas of FCC unit in petrochemical industry. It is important to accurately predict the NOx emission in advance for petrochemical industry to avoid air pollution incidents. In this paper, convolutional neural network (CNN) and long short-term memory (LSTM) are combined to predict the NOx emission in Fluid Catalytic Cracking unit (FCC unit). Convolutional-LSTM (CLSTM) is able to extract th
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Appati, Justice Kwame, Ismail Wafaa Denwar, Ebenezer Owusu, and Michael Agbo Tettey Soli. "Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques." International Journal of Intelligent Information Technologies 17, no. 2 (2021): 72–95. http://dx.doi.org/10.4018/ijiit.2021040104.

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This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0
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Dissertations / Theses on the topic "LSTM unit"

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Sarika, Pawan Kumar. "Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213.

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Today, we are living in a data-driven world. Due to a surge in data generation, there is a need for efficient and accurate techniques to analyze data. One such kind of data which is needed to be analyzed are text reviews given for movies. Rather than classifying the reviews as positive or negative, we will classify the sentiment of the reviews on the scale of one to ten. In doing so, we will compare two recurrent neural network algorithms Long short term memory(LSTM) and Gated recurrent unit(GRU). The main objective of this study is to compare the accuracies of LSTM and GRU models. For trainin
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Imramovská, Klára. "Detekce komorových extrasystol v EKG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442489.

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The thesis deals with problems of automatic detection of premature ventricular contractions in ECG records. One detection method which uses a convolutional neural network and LSTM units is implemented in the Python language. Cardiac cycles extracted from one-lead ECG were used for detection. F1 score for binary classification (PVC and normal beat) on the test dataset reached 96,41 % and 81,76 % for three-class classification (PVC, normal beat and other arrhythmias). Lastly, the accuracy of the classification is evaluated and discussed, the achieved results for binary classification are compara
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Mealey, Thomas C. "Binary Recurrent Unit: Using FPGA Hardware to Accelerate Inference in Long Short-Term Memory Neural Networks." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1524402925375566.

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Radhakrishnan, Saieshwar. "Domain Adaptation of IMU sensors using Generative Adversarial Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286821.

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Autonomous vehicles rely on sensors for a clear understanding of the environment and in a heavy duty truck, the sensors are placed at multiple locations like the cabin, chassis and the trailer in order to increase the field of view and reduce the blind spot area. Usually, these sensors perform best when they are stationary relative to the ground, hence large and fast movements, which are quite common in a truck, may lead to performance reduction, erroneous data or in the worst case, a sensor failure. This enforces a need to validate the sensors before using them for making life-critical decisi
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Gattoni, Giacomo. "Improving the reliability of recurrent neural networks while dealing with bad data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In practical applications, machine learning and deep learning models can have difficulty in achieving generalization, especially when dealing with training samples that are either noisy or limited in quantity. Standard neural networks do not guarantee the monotonicity of the input features with respect to the output, therefore they lack interpretability and predictability when it is known a priori that the input-output relationship should be monotonic. This problem can be encountered in the CPG industry, where it is not possible to ensure that a deep learning model will learn the increasing
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Anbil, Parthipan Sarath Chandar. "On challenges in training recurrent neural networks." Thèse, 2019. http://hdl.handle.net/1866/23435.

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Dans un problème de prédiction à multiples pas discrets, la prédiction à chaque instant peut dépendre de l’entrée à n’importe quel moment dans un passé lointain. Modéliser une telle dépendance à long terme est un des problèmes fondamentaux en apprentissage automatique. En théorie, les Réseaux de Neurones Récurrents (RNN) peuvent modéliser toute dépendance à long terme. En pratique, puisque la magnitude des gradients peut croître ou décroître exponentiellement avec la durée de la séquence, les RNNs ne peuvent modéliser que les dépendances à court terme. Cette thèse explore ce problème dans les
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Books on the topic "LSTM unit"

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D, Ashari. Antara tugas & hobby [i.e. hobi]: Otobiografi unik seorang pejuang, diplomat, menteri, pelukis, atlet, vegetarian, dan pelopor LSM. Yayasan Wiratama 45, 1999.

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Book chapters on the topic "LSTM unit"

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Li, Yancui, Chunxiao Lai, Jike Feng, and Hongyu Feng. "Chinese and English Elementary Discourse Units Recognition Based on Bi-LSTM-CRF Model." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63031-7_24.

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Okut, Hayrettin. "Deep Learning for Subtyping and Prediction of Diseases: Long-Short Term Memory." In Deep Learning Applications. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96180.

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The long short-term memory neural network (LSTM) is a type of recurrent neural network (RNN). During the training of RNN architecture, sequential information is used and travels through the neural network from input vector to the output neurons, while the error is calculated and propagated back through the network to update the network parameters. Information in these networks incorporates loops into the hidden layer. Loops allow information to flow multi-directionally so that the hidden state signifies past information held at a given time step. Consequently, the output is dependent on the previous predictions which are already known. However, RNNs have limited capacity to bridge more than a certain number of steps. Mainly this is due to the vanishing of gradients which causes the predictions to capture the short-term dependencies as information from earlier steps decays. As more layers in RNN containing activation functions are added, the gradient of the loss function approaches zero. The LSTM neural networks (LSTM-ANNs) enable learning long-term dependencies. LSTM introduces a memory unit and gate mechanism to enable capture of the long dependencies in a sequence. Therefore, LSTM networks can selectively remember or forget information and are capable of learn thousands timesteps by structures called cell states and three gates.
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Husna, Asma, Saman Hassanzadeh Amin, and Bharat Shah. "Demand Forecasting in Supply Chain Management Using Different Deep Learning Methods." In Advances in Logistics, Operations, and Management Science. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3805-0.ch005.

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Supply chain management (SCM) is a fast growing and largely studied field of research. Forecasting of the required materials and parts is an important task in companies and can have a significant impact on the total cost. To have a reliable forecast, some advanced methods such as deep learning techniques are helpful. The main goal of this chapter is to forecast the unit sales of thousands of items sold at different chain stores located in Ecuador with holistic techniques. Three deep learning approaches including artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM) are adopted here for predictions from the Corporación Favorita grocery sales forecasting dataset collected from Kaggle website. Finally, the performances of the applied models are evaluated and compared. The results show that LSTM network tends to outperform the other two approaches in terms of performance. All experiments are conducted using Python's deep learning library and Keras and Tensorflow packages.
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Saxena, Suchitra, Shikha Tripathi, and Sudarshan Tsb. "Deep Robot-Human Interaction with Facial Emotion Recognition Using Gated Recurrent Units & Robotic Process Automation." In Machine Learning and Artificial Intelligence. IOS Press, 2020. http://dx.doi.org/10.3233/faia200773.

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This research work proposes a Facial Emotion Recognition (FER) system using deep learning algorithm Gated Recurrent Units (GRUs) and Robotic Process Automation (RPA) for real time robotic applications. GRUs have been used in the proposed architecture to reduce training time and to capture temporal information. Most work reported in literature uses Convolution Neural Networks (CNN), Hybrid architecture of CNN with Long Short Term Memory (LSTM) and GRUs. In this work, GRUs are used for feature extraction from raw images and dense layers are used for classification. The performance of CNN, GRUs and LSTM are compared in the context of facial emotion recognition. The proposed FER system is implemented on Raspberry pi3 B+ and on Robotic Process Automation (RPA) using UiPath RPA tool for robot human interaction achieving 94.66% average accuracy in real time.
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Conference papers on the topic "LSTM unit"

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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}. 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 pre
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Nina, Oliver, and Andres Rodriguez. "Simplified LSTM unit and search space probability exploration for image description." In 2015 10th International Conference on Information, Communications and Signal Processing (ICICS). IEEE, 2015. http://dx.doi.org/10.1109/icics.2015.7459976.

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Wan, Vincent, Yannis Agiomyrgiannakis, Hanna Silen, and Jakub Vít. "Google’s Next-Generation Real-Time Unit-Selection Synthesizer Using Sequence-to-Sequence LSTM-Based Autoencoders." In Interspeech 2017. ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1107.

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Ho, Thi-Nga, Duy-Cat Can, and EngSiong Chng. "An Investigation of Word Embeddings with Deep Bidirectional LSTM for Sentence Unit Detection in Automatic Speech Transcription." In 2018 International Conference on Asian Language Processing (IALP). IEEE, 2018. http://dx.doi.org/10.1109/ialp.2018.8629114.

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Feng, Yufei, Fuyu Lv, Weichen Shen, et al. "Deep Session Interest Network for Click-Through Rate Prediction." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/319.

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Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems. How to capture users' dynamic and evolving interests from their behavior sequences remains a continuous research topic in the CTR prediction. However, most existing studies overlook the intrinsic structure of the sequences: the sequences are composed of sessions, where sessions are user behaviors separated by their occurring time. We observe that user behaviors are highly homogeneous in each session, and heterogeneous cross sessions. Based on this obs
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Gupta, Ashit, Vishal Jadhav, Mukul Patil, Anirudh Deodhar, and Venkataramana Runkana. "Forecasting of Fouling in Air Pre-Heaters Through Deep Learning." In ASME 2021 Power Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/power2021-64665.

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Abstract Thermal power plants employ regenerative type air pre-heaters (APH) for recovering heat from the boiler flue gases. APH fouling occurs due to deposition of ash particles and products formed by reactions between leaked ammonia from the upstream selective catalytic reduction (SCR) unit and sulphur oxides (SOx) present in the flue gases. Fouling is strongly influenced by concentrations of ammonia and sulphur oxide as well as the flue gas temperature within APH. It increases the differential pressure across APH over time, ultimately leading to forced outages. Owing to lack of sensors with
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Yang, Ruiyue, Wei Liu, Xiaozhou Qin, et al. "A Physics-Constrained Data-Driven Workflow for Predicting Coalbed Methane Well Production Using A Combined Gated Recurrent Unit and Multi-Layer Perception Neural Network Model." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205903-ms.

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Abstract Coalbed methane (CBM) has emerged as one of the clean unconventional resources to supplement the rising demand of conventional hydrocarbons. Analyzing and predicting CBM production performance is critical in choosing the optimal completion methods and parameters. However, the conventional numerical simulation has challenges of complicated gridding issues and expensive computational costs. The huge amount of available production data that has been collected in the field site opens up a new opportunity to develop data-driven approaches in predicting the production rate. Here, we propose
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Wang, Fuyong, Yun Zai, Jiuyu Zhao, and Siyi Fang. "Field Application of Deep Learning for Flow Rate Prediction with Downhole Temperature and Pressure." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21364-ms.

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Abstract Well real-time flow rate is one of the most important production parameters in oilfield and accurate flow rate information is crucial for production monitoring and optimization. With the wide application of permanent downhole gauge (PDG), the high-frequency and large volume of downhole temperature and pressure make applying of deep learning technique to predict flow rate possible. Flow rate of production well is predicted with long short-term memory (LSTM) network using downhole temperature and pressure production data. The specific parameters of LSTM neural network are given, as well
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Lau, Zhi Jie, and Chris Philips. "Advanced T-LSIM System Detections using Amplified External Isolated Source-Sense Unit." In ISTFA 2018. ASM International, 2018. http://dx.doi.org/10.31399/asm.cp.istfa2018p0200.

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Abstract Thermal-Laser Signal Injection Microscopy (T-LSIM) is a widely used fault isolation technique. Although there are several T-LSIM systems on the market, each is limited in terms of the voltage and current it can produce. In this paper, the authors explain how they incorporated an Amplified External Isolated Source-Sense (AxISS) unit into their T-LSIM platform, increasing its current sourcing capability and voltage biasing range. They also provide examples highlighting the types of faults and failures that the modified system can detect.
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Alencar, Victor Aquiles Soares de Barros, Lucas Ribeiro Pessamilio, Felipe Rooke Da Silva, Heder Soares Bernardino, and Alex Borges Vieira. "Predição de Séries Temporais de Demanda em Modelos de Compartilhamento de Veículos para Modelos Uni e Multi Variáveis." In Workshop de Computação Urbana. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/courb.2020.12355.

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O compartilhamento de veículos é alternativa para a mobilidade urbana que vem sendo largamente adotada. Porém, essa abordagem está sujeita a problemas, como desbalanceamento da frota ao longo do dia, por conta de demandas variadas em grandes centros urbanos. Neste trabalho aplicamos duas técnicas de séries temporais, o LSTM e o Prophet, para inferir a demanda de três serviços reais de compartilhamento de veículos. Além dos dados históricos, atributos climáticos também foram considerados numa das aplicações do LSTM. Como resultado, foi observado que a adição de dados meteorológicos melhorou o d
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