Relevant bibliographies by topics / Neural networks with LSTM

Contents

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

Academic literature on the topic 'Neural networks with LSTM'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Neural networks with LSTM.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Neural networks with LSTM"

1

Bakir, Houda, Ghassen Chniti, and Hédi Zaher. "E-Commerce Price Forecasting Using LSTM Neural Networks." International Journal of Machine Learning and Computing 8, no. 2 (2018): 169–74. http://dx.doi.org/10.18178/ijmlc.2018.8.2.682.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Yu, Yong, Xiaosheng Si, Changhua Hu, and Jianxun Zhang. "A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures." Neural Computation 31, no. 7 (2019): 1235–70. http://dx.doi.org/10.1162/neco_a_01199.

Full text
Abstract:
Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are unable to learn the relevant information of input data when the input gap is large. By introducing gate functions into the cell structure, the long short-term memory (LSTM) could handle the problem of long-term dependencies well. Since its introduction, almost all the exciting results based on RNNs have been achieved by the LSTM. The LSTM has become the focus of deep learning. We review the LSTM cell and its variants to explore the learning capacity of the LSTM cell. Furthermore, the LSTM networks are divided into two broad categories: LSTM-dominated networks and integrated LSTM networks. In addition, their various applications are discussed. Finally, future research directions are presented for LSTM networks.
APA, Harvard, Vancouver, ISO, and other styles
3

Jia, YuKang, Zhicheng Wu, Yanyan Xu, Dengfeng Ke, and Kaile Su. "Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition." Journal of Robotics 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/2061827.

Full text
Abstract:
Long Short-Term Memory (LSTM) is a kind of Recurrent Neural Networks (RNN) relating to time series, which has achieved good performance in speech recogniton and image recognition. Long Short-Term Memory Projection (LSTMP) is a variant of LSTM to further optimize speed and performance of LSTM by adding a projection layer. As LSTM and LSTMP have performed well in pattern recognition, in this paper, we combine them with Connectionist Temporal Classification (CTC) to study piano’s continuous note recognition for robotics. Based on the Beijing Forestry University music library, we conduct experiments to show recognition rates and numbers of iterations of LSTM with a single layer, LSTMP with a single layer, and Deep LSTM (DLSTM, LSTM with multilayers). As a result, the single layer LSTMP proves performing much better than the single layer LSTM in both time and the recognition rate; that is, LSTMP has fewer parameters and therefore reduces the training time, and, moreover, benefiting from the projection layer, LSTMP has better performance, too. The best recognition rate of LSTMP is 99.8%. As for DLSTM, the recognition rate can reach 100% because of the effectiveness of the deep structure, but compared with the single layer LSTMP, DLSTM needs more training time.
APA, Harvard, Vancouver, ISO, and other styles
4

Kalinin, Maxim, Vasiliy Krundyshev, and Evgeny Zubkov. "Estimation of applicability of modern neural network methods for preventing cyberthreats to self-organizing network infrastructures of digital economy platforms,." SHS Web of Conferences 44 (2018): 00044. http://dx.doi.org/10.1051/shsconf/20184400044.

Full text
Abstract:
The problems of applying neural network methods for solving problems of preventing cyberthreats to flexible self-organizing network infrastructures of digital economy platforms: vehicle adhoc networks, wireless sensor networks, industrial IoT, “smart buildings” and “smart cities” are considered. The applicability of the classic perceptron neural network, recurrent, deep, LSTM neural networks and neural networks ensembles in the restricting conditions of fast training and big data processing are estimated. The use of neural networks with a complex architecture– recurrent and LSTM neural networks – is experimentally justified for building a system of intrusion detection for self-organizing network infrastructures.
APA, Harvard, Vancouver, ISO, and other styles
5

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 (2018): 1719–37. http://dx.doi.org/10.1142/s0218194018400259.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
6

Pal, Subarno, Soumadip Ghosh, and Amitava Nag. "Sentiment Analysis in the Light of LSTM Recurrent Neural Networks." International Journal of Synthetic Emotions 9, no. 1 (2018): 33–39. http://dx.doi.org/10.4018/ijse.2018010103.

Full text
Abstract:
Long short-term memory (LSTM) is a special type of recurrent neural network (RNN) architecture that was designed over simple RNNs for modeling temporal sequences and their long-range dependencies more accurately. In this article, the authors work with different types of LSTM architectures for sentiment analysis of movie reviews. It has been showed that LSTM RNNs are more effective than deep neural networks and conventional RNNs for sentiment analysis. Here, the authors explore different architectures associated with LSTM models to study their relative performance on sentiment analysis. A simple LSTM is first constructed and its performance is studied. On subsequent stages, the LSTM layer is stacked one upon another which shows an increase in accuracy. Later the LSTM layers were made bidirectional to convey data both forward and backward in the network. The authors hereby show that a layered deep LSTM with bidirectional connections has better performance in terms of accuracy compared to the simpler versions of LSTM used here.
APA, Harvard, Vancouver, ISO, and other styles
7

Yu, Dian, and Shouqian Sun. "A Systematic Exploration of Deep Neural Networks for EDA-Based Emotion Recognition." Information 11, no. 4 (2020): 212. http://dx.doi.org/10.3390/info11040212.

Full text
Abstract:
Subject-independent emotion recognition based on physiological signals has become a research hotspot. Previous research has proved that electrodermal activity (EDA) signals are an effective data resource for emotion recognition. Benefiting from their great representation ability, an increasing number of deep neural networks have been applied for emotion recognition, and they can be classified as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or a combination of these (CNN+RNN). However, there has been no systematic research on the predictive power and configurations of different deep neural networks in this task. In this work, we systematically explore the configurations and performances of three adapted deep neural networks: ResNet, LSTM, and hybrid ResNet-LSTM. Our experiments use the subject-independent method to evaluate the three-class classification on the MAHNOB dataset. The results prove that the CNN model (ResNet) reaches a better accuracy and F1 score than the RNN model (LSTM) and the CNN+RNN model (hybrid ResNet-LSTM). Extensive comparisons also reveal that our three deep neural networks with EDA data outperform previous models with handcraft features on emotion recognition, which proves the great potential of the end-to-end DNN method.
APA, Harvard, Vancouver, ISO, and other styles
8

Dropka, Natasha, Stefan Ecklebe, and Martin Holena. "Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks." Crystals 11, no. 2 (2021): 138. http://dx.doi.org/10.3390/cryst11020138.

Full text
Abstract:
The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations. Real time predictions of the temperatures and solid–liquid interface position in GaAs are crucial for control applications and for process visualization, i.e., for generation of digital twins. In the reported study, an LSTM network was trained on 1950 datasets with 2 external inputs and 6 outputs. Based on network performance criteria and training results, LSTMs showed the very accurate predictions of the VGF-GaAs growth process with median root-mean-square-error (RMSE) values of 2 × 10−3. This deep learning method achieved a superior predictive accuracy and timeliness compared with more traditional Nonlinear AutoRegressive eXogenous (NARX) recurrent networks.
APA, Harvard, Vancouver, ISO, and other styles
9

Xu, Lingfeng, Xiang Chen, Shuai Cao, Xu Zhang, and Xun Chen. "Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation." Sensors 18, no. 10 (2018): 3226. http://dx.doi.org/10.3390/s18103226.

Full text
Abstract:
To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 ± 1.29 and 8.67 ± 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 ± 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation.
APA, Harvard, Vancouver, ISO, and other styles
10

Wan, Huaiyu, Shengnan Guo, Kang Yin, Xiaohui Liang, and Youfang Lin. "CTS-LSTM: LSTM-based neural networks for correlatedtime series prediction." Knowledge-Based Systems 191 (March 2020): 105239. http://dx.doi.org/10.1016/j.knosys.2019.105239.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Neural networks with LSTM"

1

Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.

Full text
Abstract:
LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level parallelism options as blocks are instantiated based on parameters. The internal structure of the design implements pipelined, parallel or serial solutions depending on which is optimal in every case. The implications concerning these decisions are discussed in detail in the report. The design process is described in detail and the evaluation of the design is also presented to measure accuracy and error of the design output.This thesis work resulted in a complete synthesizable ASIC design implementing an LSTM layer, a Fully Connected layer and a Softmax layer which can perform classification of data based on trained weight matrices and bias vectors. The design primarily uses 16-bit fixed point format with 5 integer and 11 fractional bits but increased precision representations are used in some blocks to reduce error output. Additionally, a verification environment has also been designed and is capable of performing simulations, evaluating the design output by comparing it with results produced from performing the same operations with 64-bit floating point precision on a SystemVerilog testbench and measuring the encountered error. The results concerning the accuracy and the design output error margin are presented in this thesis report. The design went through Logic and Physical synthesis and successfully resulted in a functional netlist for every tested configuration. Timing, area and power measurements on the generated netlists of various configurations of the design show consistency and are reported in this report.<br>LSTM neurala nätverk har använts för taligenkänning, bildigenkänning och andra artificiella intelligensapplikationer i många år. De flesta applikationer utför LSTM-algoritmen och de nödvändiga beräkningarna i digitala moln. Offline lösningar inkluderar användningen av FPGA och GPU men de mest lovande lösningarna inkluderar ASIC-acceleratorer utformade för endast dettaändamål. Denna rapport presenterar en ASIC-design som kan utföra multipla iterationer av LSTM-algoritmen på en enkelriktad neural nätverksarkitetur utan peepholes. Den föreslagna designed ger aritmetrisk nivå-parallellismalternativ som block som är instansierat baserat på parametrar. Designens inre konstruktion implementerar pipelinerade, parallella, eller seriella lösningar beroende på vilket anternativ som är optimalt till alla fall. Konsekvenserna för dessa beslut diskuteras i detalj i rapporten. Designprocessen beskrivs i detalj och utvärderingen av designen presenteras också för att mäta noggrannheten och felmarginal i designutgången. Resultatet av arbetet från denna rapport är en fullständig syntetiserbar ASIC design som har implementerat ett LSTM-lager, ett fullständigt anslutet lager och ett Softmax-lager som kan utföra klassificering av data baserat på tränade viktmatriser och biasvektorer. Designen använder huvudsakligen 16bitars fast flytpunktsformat med 5 heltal och 11 fraktions bitar men ökade precisionsrepresentationer används i vissa block för att minska felmarginal. Till detta har även en verifieringsmiljö utformats som kan utföra simuleringar, utvärdera designresultatet genom att jämföra det med resultatet som produceras från att utföra samma operationer med 64-bitars flytpunktsprecision på en SystemVerilog testbänk och mäta uppstådda felmarginal. Resultaten avseende noggrannheten och designutgångens felmarginal presenteras i denna rapport.Designen gick genom Logisk och Fysisk syntes och framgångsrikt resulterade i en funktionell nätlista för varje testad konfiguration. Timing, area och effektmätningar på den genererade nätlistorna av olika konfigurationer av designen visar konsistens och rapporteras i denna rapport.
APA, Harvard, Vancouver, ISO, and other styles
2

Cavallie, Mester Jon William. "Using LSTM Neural Networks To Predict Daily Stock Returns." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-106124.

Full text
Abstract:
Long short-term memory (LSTM) neural networks have been proven to be effective for time series prediction, even in some instances where the data is non-stationary. This lead us to examine their predictive ability of stock market returns, as the development of stock prices and returns tend to be a non-stationary time series. We used daily stock trading data to let an LSTM train models at predicting daily returns for 60 stocks from the OMX30 and Nasdaq-100 indices. Subsequently, we measured their accuracy, precision, and recall. The mean accuracy was 49.75 percent, meaning that the observed accuracy was close to the accuracy one would observe by randomly selecting a prediction for each day and lower than the accuracy achieved by blindly predicting all days to be positive. Finally, we concluded that further improvements need to be made for models trained by LSTMs to have any notable predictive ability in the area of stock returns.
APA, Harvard, Vancouver, ISO, and other styles
3

Ärlemalm, Filip. "Harbour Porpoise Click Train Classification with LSTM Recurrent Neural Networks." Thesis, KTH, Teknisk informationsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215088.

Full text
Abstract:
The harbour porpoise is a toothed whale whose presence is threatened in Scandinavia. Onestep towards preserving the species in critical areas is to study and observe the harbourporpoise population growth or decline in these areas. Today this is done by using underwateraudio recorders, so called hydrophones, and manual analyzing tools. This report describes amethod that modernizes the process of harbour porpoise detection with machine learning. Thedetection method is based on data collected by the hydrophone AQUAclick 100. The data isprocessed and classified automatically with a stacked long short-term memory recurrent neuralnetwork designed specifically for this purpose.<br>Vanlig tumlare är en tandval vars närvaro i Skandinavien är hotad. Ett steg mot att kunnabevara arten i utsatta områden är att studera och observera tumlarbeståndets tillväxt ellertillbakagång i dessa områden. Detta görs idag med hjälp av ljudinspelare för undervattensbruk,så kallade hydrofoner, samt manuella analysverktyg. Den här rapporten beskriver enmetod som moderniserar processen för detektering av vanlig tumlare genom maskininlärning.Detekteringen är baserad på insamlad data från hydrofonen AQUAclick 100. Bearbetning ochklassificering av data har automatiserats genom att använda ett staplat återkopplande neuraltnätverk med långt korttidsminne utarbetat specifikt för detta ändamål.
APA, Harvard, Vancouver, ISO, and other styles
4

Li, Edwin. "LSTM Neural Network Models for Market Movement Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231627.

Full text
Abstract:
Interpreting time varying phenomena is a key challenge in the capital markets. Time series analysis using autoregressive methods has been carried out over the last couple of decades, often with reassuring results. However, such methods sometimes fail to explain trends and cyclical fluctuations, which may be characterized by long-range dependencies or even dependencies between the input features. The purpose of this thesis is to investigate whether recurrent neural networks with LSTM-cells can be used to capture these dependencies, and ultimately be used as a complement for index trading decisions. Experiments are made on different setups of the S&amp;P-500 stock index, and two distinct models are built, each one being an improvement of the previous model. The first model is a multivariate regression model, and the second model is a multivariate binary classifier. The output of each model is used to reason about the future behavior of the index. The experiment shows for the configuration provided that LSTM RNNs are unsuitable for predicting exact values of daily returns, but gives satisfactory results when used to predict the direction of the movement.<br>Att förstå och kunna förutsäga hur index varierar med tiden och andra parametrar är ett viktigt problem inom kapitalmarknader. Tidsserieanalys med autoregressiva metoder har funnits sedan årtionden tillbaka, och har oftast gett goda resultat. Dessa metoder saknar dock möjligheten att förklara trender och cykliska variationer i tidsserien, något som kan karaktäriseras av tidsvarierande samband, men även samband mellan parametrar som indexet beror utav. Syftet med denna studie är att undersöka om recurrent neural networks (RNN) med long short-term memory-celler (LSTM) kan användas för att fånga dessa samband, för att slutligen användas som en modell för att komplettera indexhandel. Experimenten är gjorda mot en modifierad S&amp;P-500 datamängd, och två distinkta modeller har tagits fram. Den ena är en multivariat regressionsmodell för att förutspå exakta värden, och den andra modellen är en multivariat klassifierare som förutspår riktningen på nästa dags indexrörelse. Experimenten visar för den konfiguration som presenteras i rapporten att LSTM RNN inte passar för att förutspå exakta värden för indexet, men ger tillfredsställande resultat när modellen ska förutsäga indexets framtida riktning.
APA, Harvard, Vancouver, ISO, and other styles
5

Holm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

Full text
Abstract:
The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.
APA, Harvard, Vancouver, ISO, and other styles
6

Zambezi, Samantha. "Predicting social unrest events in South Africa using LSTM neural networks." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33986.

Full text
Abstract:
This thesis demonstrates an approach to predict the count of social unrest events in South Africa. A comparison is made between traditional forecasting approaches and neural networks; the traditional forecast method selected being the Autoregressive Integrated Moving Average (ARIMA model). The type of neural network implemented was the Long Short-Term Memory (LSTM) neural network. The basic theoretical concepts of ARIMA and LSTM neural networks are explained and subsequently, the patterns of the social unrest time series were analysed using time series exploratory techniques. The social unrest time series contained a significant number of irregular fluctuations with a non-linear trend. The structure of the social unrest time series suggested that traditional linear approaches would fail to model the non-linear behaviour of the time series. This thesis confirms this finding. Twelve experiments were conducted, and in these experiments, features, scaling procedures and model configurations are varied (i.e. univariate and multivariate models). Multivariate LSTM achieved the lowest forecast errors and performance improved as more explanatory features were introduced. The ARIMA model's performance deteriorated with added complexity and the univariate ARIMA produced lower forecast errors compared to the multivariate ARIMA. In conclusion, it can be claimed that multivariate LSTM neural networks are useful for predicting social unrest events.
APA, Harvard, Vancouver, ISO, and other styles
7

Graffi, Giacomo. "A novel approach for Credit Scoring using Deep Neural Networks with bank transaction data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

Find full text
Abstract:
With the PSD2 open banking revolution FinTechs obtained a key role in the financial industry. This role implies the inquiry and development of new techniques, products and solutions to compete with other players in this area. The aim of this thesis is to investigate the applicability of the state-of-the-art Deep Learning techniques for Credit Risk Modeling. In order to accomplish it, a PSD2-related synthetic and anonymized dataset has been used to simulate an application process with only one account per user. Firstly, a machine-readable representation of the bank accounts has been created, starting from the raw transactions’ data and scaling the variables using the quantile function. Afterwards, a Deep Neural Network has been created in order to capture the complex relations between the input variables and to extract information from the accounts’ representations. The proposed architecture accomplished the assigned tasks with a Gini index of 0.55, exploiting a Convolutional encoder to extract features from the inputs and a Recurrent decoder to analyze them.
APA, Harvard, Vancouver, ISO, and other styles
8

Lin, Alvin. "Video Based Automatic Speech Recognition Using Neural Networks." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2343.

Full text
Abstract:
Neural network approaches have become popular in the field of automatic speech recognition (ASR). Most ASR methods use audio data to classify words. Lip reading ASR techniques utilize only video data, which compensates for noisy environments where audio may be compromised. A comprehensive approach, including the vetting of datasets and development of a preprocessing chain, to video-based ASR is developed. This approach will be based on neural networks, namely 3D convolutional neural networks (3D-CNN) and Long short-term memory (LSTM). These types of neural networks are designed to take in temporal data such as videos. Various combinations of different neural network architecture and preprocessing techniques are explored. The best performing neural network architecture, a CNN with bidirectional LSTM, compares favorably against recent works on video-based ASR.
APA, Harvard, Vancouver, ISO, and other styles
9

Alam, Samiul. "Recurrent neural networks in electricity load forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233254.

Full text
Abstract:
In this thesis two main studies are conducted to compare the predictive capabilities of feed-forward neural networks (FFNN) and long short-term memory networks (LSTM) in electricity load forecasting. The first study compares univariate networks using past electricity load, as well as multivariate networks using past electricity load and air temperature, in day-ahead load forecasting using varying lookback periods and sparsity of past observations. The second study compares FFNNs and LSTMs of different complexities (i.e. network sizes) when restrictions imposed by limitations of the real world are taken into consideration. No significant differences are found between the predictive performances of the two neural network approaches. However, adding air temperature as extra input to the LSTM is found to significantly decrease its performance. Furthermore, the predictive performance of the FFNN is found to significantly decrease as the network complexity grows, while the predictive performance of the LSTM is found to increase as the network complexity grows. All the findings considered, we do not find that there is enough evidence in favour of the LSTM in electricity load forecasting.<br>I denna uppsats beskrivs två studier som jämför feed-forward neurala nätverk (FFNN) och long short-term memory neurala nätverk (LSTM) i prognostisering av elkonsumtion. I den första studien undersöks univariata modeller som använder tidigare elkonsumtion, och flervariata modeller som använder tidigare elkonsumtion och temperaturmätningar, för att göra prognoser av elkonsumtion för nästa dag. Hur långt bak i tiden tidigare information hämtas ifrån samt upplösningen av tidigare information varieras. I den andra studien undersöks FFNN- och LSTM-modeller med praktiska begränsningar såsom tillgänglighet av data i åtanke. Även storleken av nätverken varieras. I studierna finnes ingen skillnad mellan FFNN- och LSTM-modellernas förmåga att prognostisera elkonsumtion. Däremot minskar FFNN-modellens förmåga att prognostisera elkonsumtion då storleken av modellen ökar. Å andra sidan ökar LSTM-modellens förmåga då storkelen ökar. Utifrån dessa resultat anser vi inte att det finns tillräckligt med bevis till förmån för LSTM-modeller i prognostisering av elkonsumtion.
APA, Harvard, Vancouver, ISO, and other styles
10

Roxbo, Daniel. "A Detailed Analysis of Semantic Dependency Parsing with Deep Neural Networks." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156831.

Full text
Abstract:
The use of Long Short Term Memory (LSTM) networks continues to yield better results in natural language processing tasks. One area which recently has seen significant improvements is semantic dependency parsing, where the current state-of-the-art model uses a multilayer LSTM combined with an attention-based scoring function to predict the dependencies. In this thesis the state of the art model is first replicated and then extended to include features based on syntactical trees, which was found to be useful in a similar model. In addition, the effect of part-of-speech tags is studied. The replicated model achieves a labeled F1 score of 93.6 on the in-domain data and 89.2 on the out-of-domain data on the DM dataset, which shows that the model is indeed replicable. Using multiple features extracted from syntactic gold standard trees of the DELPH-IN Derivation Tree (DT) type increased the labeled scores to 97.1 and 94.1 respectively, while the use of predicted trees of the Stanford Basic (SB) type did not improve the results at all. The usefulness of part-of-speech tags was found to be diminished in the presence of other features.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Neural networks with LSTM"

1

Dominique, Valentin, and Edelman Betty, eds. Neural networks. Sage Publications, 1999.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Rojas, Raúl. Neural Networks. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Müller, Berndt, Joachim Reinhardt, and Michael T. Strickland. Neural Networks. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Almeida, Luis B., and Christian J. Wellekens, eds. Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52255-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Davalo, Eric, and Patrick Naïm. Neural Networks. Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-12312-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Müller, Berndt, and Joachim Reinhardt. Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Neural networks. Palgrave, 2000.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Abdi, Hervé, Dominique Valentin, and Betty Edelman. Neural Networks. SAGE Publications, Inc., 1999. http://dx.doi.org/10.4135/9781412985277.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Hoffmann, Norbert. Simulating neural networks. Vieweg, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Bischof, Horst. Pyramidal neural networks. Lawrence Erlbaum Associates, 1995.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Neural networks with LSTM"

1

Zhang, Nan, Wei-Long Zheng, Wei Liu, and Bao-Liang Lu. "Continuous Vigilance Estimation Using LSTM Neural Networks." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46672-9_59.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Alexandre, Luís A., and J. P. Marques de Sá. "Error Entropy Minimization for LSTM Training." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840817_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Yu, Wen, Xiaoou Li, and Jesus Gonzalez. "Fast Training of Deep LSTM Networks." In Advances in Neural Networks – ISNN 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22796-8_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Klapper-Rybicka, Magdalena, Nicol N. Schraudolph, and Jürgen Schmidhuber. "Unsupervised Learning in LSTM Recurrent Neural Networks." In Artificial Neural Networks — ICANN 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_95.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Li, SiLiang, Bin Xu, and Tong Lee Chung. "Definition Extraction with LSTM Recurrent Neural Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47674-2_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Hu, Jian, Xin Xin, and Ping Guo. "LSTM with Matrix Factorization for Road Speed Prediction." In Advances in Neural Networks - ISNN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59072-1_29.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Agafonov, Anton, and Alexander Yumaganov. "Bus Arrival Time Prediction with LSTM Neural Network." In Advances in Neural Networks – ISNN 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22796-8_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Gers, Felix A., Juan Antonio Pérez-Ortiz, Douglas Eck, and Jürgen Schmidhuber. "Learning Context Sensitive Languages with LSTM Trained with Kalman Filters." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_107.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Gers, Felix A., Douglas Eck, and Jürgen Schmidhuber. "Applying LSTM to Time Series Predictable through Time-Window Approaches." In Artificial Neural Networks — ICANN 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_93.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Beringer, Nicole, Alex Graves, Florian Schiel, and Jürgen Schmidhuber. "Classifying Unprompted Speech by Retraining LSTM Nets." In Artificial Neural Networks: Biological Inspirations – ICANN 2005. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550822_90.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Neural networks with LSTM"

1

Sun, Qingnan, Marko V. Jankovic, Lia Bally, and Stavroula G. Mougiakakou. "Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network." In 2018 14th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2018. http://dx.doi.org/10.1109/neurel.2018.8586990.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Arshi, Sahar, Li Zhang, and Rebecca Strachan. "Prediction Using LSTM Networks." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852206.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Lin, Tao, Tian Guo, and Karl Aberer. "Hybrid Neural Networks for Learning the Trend in Time Series." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/316.

Full text
Abstract:
The trend of time series characterizes the intermediate upward and downward behaviour of time series. Learning and forecasting the trend in time series data play an important role in many real applications, ranging from resource allocation in data centers, load schedule in smart grid, and so on. Inspired by the recent successes of neural networks, in this paper we propose TreNet, a novel end-to-end hybrid neural network to learn local and global contextual features for predicting the trend of time series. TreNet leverages convolutional neural networks (CNNs) to extract salient features from local raw data of time series. Meanwhile, considering the long-range dependency existing in the sequence of historical trends of time series, TreNet uses a long-short term memory recurrent neural network (LSTM) to capture such dependency. Then, a feature fusion layer is to learn joint representation for predicting the trend. TreNet demonstrates its effectiveness by outperforming CNN, LSTM, the cascade of CNN and LSTM, Hidden Markov Model based method and various kernel based baselines on real datasets.
APA, Harvard, Vancouver, ISO, and other styles
4

Pulver, Andrew, and Siwei Lyu. "LSTM with working memory." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965940.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Yang, Dongdong, Senzhang Wang, and Zhoujun Li. "Ensemble Neural Relation Extraction with Adaptive Boosting." 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.2018/630.

Full text
Abstract:
Relation extraction has been widely studied to extract new relational facts from open corpus. Previous relation extraction methods are faced with the problem of wrong labels and noisy data, which substantially decrease the performance of the model. In this paper, we propose an ensemble neural network model - Adaptive Boosting LSTMs with Attention, to more effectively perform relation extraction. Specifically, our model first employs the recursive neural network LSTMs to embed each sentence. Then we import attention into LSTMs by considering that the words in a sentence do not contribute equally to the semantic meaning of the sentence. Next via adaptive boosting, we build strategically several such neural classifiers. By ensembling multiple such LSTM classifiers with adaptive boosting, we could build a more effective and robust joint ensemble neural networks based relation extractor. Experiment results on real dataset demonstrate the superior performance of the proposed model, improving F1-score by about 8% compared to the state-of-the-art models.
APA, Harvard, Vancouver, ISO, and other styles
6

Sundermeyer, Martin, Ralf Schlüter, and Hermann Ney. "LSTM neural networks for language modeling." In Interspeech 2012. ISCA, 2012. http://dx.doi.org/10.21437/interspeech.2012-65.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hu, Weifei, Yihan He, Zhenyu Liu, Jianrong Tan, Ming Yang, and Jiancheng Chen. "A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin." In ASME 2020 Power Conference collocated with the 2020 International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/power2020-16500.

Full text
Abstract:
Abstract Precise time series prediction serves as an important role in constructing a Digital Twin (DT). The various internal and external interferences result in highly non-linear and stochastic time series data sampled from real situations. Although artificial Neural Networks (ANNs) are often used to forecast time series for their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components, each of which is composed of single-frequency and stationary signal, and a residual signal. The decomposed signals are used to train the BO-LSTM neural networks, in which the hyper-parameters of the LSTM neural networks are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed hybrid method (EEMD-BO-LSTM), this paper conducts a case study of wind speed time series prediction and has a comprehensive comparison between the proposed method and other approaches including the persistence model, ARIMA, LSTM neural networks, B0-LSTM neural networks, and EEMD-LSTM neural networks. Results show an improved prediction accuracy using the EEMD-BO-LSTM method by multiple accuracy metrics.
APA, Harvard, Vancouver, ISO, and other styles
8

Qin, Yu, Jiajun Du, Xinyao Wang, and Hongtao Lu. "Recurrent Layer Aggregation using LSTM." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852077.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Shi, Zhiyuan, Min Xu, Quan Pan, Bing Yan, and Haimin Zhang. "LSTM-based Flight Trajectory Prediction." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489734.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Rosato, Antonello, Federico Succetti, Marcello Barbirotta, and Massimo Panella. "ADMM Consensus for Deep LSTM Networks." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207512.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Neural networks with LSTM"

1

Ankel, Victoria, Stella Pantopoulou, Matthew Weathered, Darius Lisowski, Anthonie Cilliers, and Alexander Heifetz. One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1760289.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Johnson, John L., and C. C. Sung. Neural Networks. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada222110.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Smith, Patrick I. Neural Networks. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/815740.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Holder, Nanette S. Introduction to Neural Networks. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada248258.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wiggins, Vince L., Larry T. Looper, and Sheree K. Engquist. Neural Networks: A Primer. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada235920.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Abu-Mostafa, Yaser S., and Amir F. Atiya. Theory of Neural Networks. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada253187.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Alltop, W. O. Piecewise Linear Neural Networks. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada265031.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

Full text
Abstract:
We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-world applications, where in practice trade-offs between model accuracy and runtime efficiency are often sought. Previous literature presents solutions to train models at each individual fixed efficiency/accuracy trade-off point. But how to produce a model flexible in runtime precision is largely unexplored. When the demand of efficiency/accuracy trade-off varies from time to time or even dynamically changes in runtime, it is infeasible to re-train models accordingly, and the storage budget may forbid keeping multiple models. Our proposed framework achieves this flexibility without performance degradation. More importantly, we demonstrate that this achievement is agnostic to model architectures. We experimentally validated our method with different deep network backbones (AlexNet-small, Resnet-20, Resnet-50) on different datasets (SVHN, Cifar-10, ImageNet) and observed consistent results.
APA, Harvard, Vancouver, ISO, and other styles
9

Keller, P. E. Artificial neural networks in medicine. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10162484.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Chua, Leon O. Nonlinear Circuits and Neural Networks. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada298633.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Neural networks with LSTM' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography