LSTM: A Search Space Odyssey

Long Short-Term Memory

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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

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Speech recognition with deep recurrent neural networks

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Practical Bayesian Optimization of Machine Learning Algorithms

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Framewise phoneme classification with bidirectional LSTM and other neural network architectures

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Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

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Long-term recurrent convolutional networks for visual recognition and description

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Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption

Kim, T., & Cho, S. (2019) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2019.8789968 · Cited by 89

Accurate detection of sleep apnea with long short-term memory network based on RR interval signals

Faust, O., Barika, R., Shenfield, A., Ciaccio, E. J., & Acharya, U. R. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106591 · Cited by 94

PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information

Lee, D., & Kim, K. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.12.021 · Cited by 89

High Precision LSTM Model for Short-Time Load Forecasting in Power Systems

Ciechulski, T., & Osowski, S. (2021) · Energies · doi:10.3390/en14112983 · Cited by 93

Decoding subjective emotional arousal from EEG during an immersive virtual reality experience

Hofmann, S., Klotzsche, F., Mariola, A., Nikulin, V. V., Villringer, A., & Gaebler, M. (2021) · eLife · doi:10.7554/elife.64812 · Cited by 106

BERT-Log: Anomaly Detection for System Logs Based on Pre-trained Language Model

Chen, S., & Liao, H. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2145642 · Cited by 107

NOx emission prediction using a lightweight convolutional neural network for cleaner production in a down-fired boiler

Wang, Z., Peng, X., Cao, S., Zhou, H., Fan, S., Li, K., & Huang, W. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.136060 · Cited by 89

DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks

Bayram, F., Aupke, P., Ahmed, B. S., Kassler, A., Theocharis, A. D., & Forsman, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106480 · Cited by 99

SiMaLSTM-SNP: novel semantic relatedness learning model preserving both Siamese networks and membrane computing

Gu, X., Chen, X., Lu, P., Lan, X., Li, X., & Du, Y. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05592-7 · Cited by 91

Using a Recurrent Neural Network to Reconstruct Quantum Dynamics of a Superconducting Qubit from Physical Observations

Flurin, E., Martin, L. S., Hacohen-Gourgy, S., & Siddiqi, I. A. (2020) · Physical Review X · doi:10.1103/physrevx.10.011006 · Cited by 101

A Deep Learning Perspective on Beauty, Sentiment, and Remembrance of Art

Cetinić, E., Lipić, T., & Grgić, S. (2019) · IEEE Access · doi:10.1109/access.2019.2921101 · Cited by 96

Streaming small-footprint keyword spotting using sequence-to-sequence models

He, Y., Prabhavalkar, R., Rao, K., Li, W., Bakhtin, A., & Mcgraw, I. C. (2017) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2017.8268974 · Cited by 85

Integrating deep learning and logging data analytics for lithofacies classification and 3D modeling of tight sandstone reservoirs

Liu, J., & Liu, J.-C. (2021) · Geoscience Frontiers · doi:10.1016/j.gsf.2021.101311 · Cited by 88

Predicting stress–strain curves using transfer learning: Knowledge transfer across polymer composites

Zhang, Z., Liu, Q., & Wu, D. (2022) · Materials & Design · doi:10.1016/j.matdes.2022.110700 · Cited by 88

Using LSTM networks to predict engine condition on large scale data processing framework

Aydın, O., & Guldamlasioglu, S. (2017) · doi:10.1109/iceee2.2017.7935834 · Cited by 87

Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery

Jayasinghe, L., Samarasinghe, T., Yuen, C., Low, J., & Ge, S. S. (2019) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2019.8754956 · Cited by 87

Multi-Person Pose Estimation Using Bounding Box Constraint and LSTM

Li, M., Zhou, Z., & Liu, X. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2903455 · Cited by 88

Nonintrusive reduced order modeling framework for quasigeostrophic turbulence

Rahman, S. M., Pawar, S. A., San, O., Rasheed, A., & Iliescu, T. (2019) · Physical review. E · doi:10.1103/physreve.100.053306 · Cited by 88

Toward the Prediction Level of Situation Awareness for Electric Power Systems Using CNN-LSTM Network

Wang, Q., Bu, S., He, Z. Y., & Dong, Z. Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3047607 · Cited by 91

Joint entity and relation extraction model based on rich semantics

Geng, Z., Zhang, Y., & Han, Y. M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.12.037 · Cited by 89

Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles

Luo, C., Sun, L., Dabiri, D., & Yuille, A. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341034 · Cited by 87

Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach

Tang, L., Xie, H., Wang, X., & Bie, Z. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120860 · Cited by 89

Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas

Wani, O. A., Mahdi, S. S., Yeasin, M., Kumar, S. S., Gagnon, A. S., Danish, F., Al-Ansari, N. A., El‑Hendawy, S., & Mattar, M. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-77687-x · Cited by 112

On the Practical Computational Power of Finite Precision RNNs for Language Recognition

Weiss, G., Goldberg, Y., & Yahav, E. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2117 · Cited by 226

Recurrent Memory Networks for Language Modeling

Tran, K. M., Bisazza, A., & Monz, C. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1036 · Cited by 104

Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients

Singh, K., Singh, S., & Malhotra, J. (2020) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/0954411920966937 · Cited by 89

An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids

Javaid, N., Jan, N., & Javed, M. U. (2021) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2021.03.002 · Cited by 86

A Survey of Deep Learning Techniques for Cybersecurity in Mobile Networks

Rodríguez, E., Otero, B., Gutiérrez, N., & Canal, R. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3086296 · Cited by 95

Recurrent neural networks

Tsantekidis, A., Passalis, N., & Tefas, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-385787-1.00010-5 · Cited by 102

Recognition of human activity using GRU deep learning algorithm

Mohsen, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15571-y · Cited by 94

Deep Recurrent Neural Networks for ECG Signal DenoisingPreprint

Antczak, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.11551 · Cited by 128

Deep Classification of Epileptic Signals

Ahmedt‐Aristizabal, D., Fookes, C., Nguyen, K., & Sridharan, S. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512249 · Cited by 89

Partial Discharge Recognition with a Multi-Resolution Convolutional Neural Network

Li, G., Wang, X., Li, X., Yang, A., & Rong, M. (2018) · Sensors · doi:10.3390/s18103512 · Cited by 84

Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning

Shintate, T., & Pichl, L. (2019) · Journal of risk and financial management · doi:10.3390/jrfm12010017 · Cited by 91

Anomaly detection in discrete manufacturing using self-learning approaches

Lindemann, B., Fesenmayr, F., Jazdi, N., & Weyrich, M. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.02.073 · Cited by 86

Expressive TTS Training With Frame and Style Reconstruction Loss

Liu, R., Şişman, B., Gao, G., & Li, H. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3076369 · Cited by 85

ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An overview

Escalante, H. J., Ponce-López, V., Wan, J., Riegler, M. A., Chen, B., Clapés, A., Escalera, S., Guyon, I., Baró, X., Halvorsen, P., et al. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899609 · Cited by 86

Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks

Valiente, R., Zaman, M., Özer, S., & Fallah, Y. P. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8814260 · Cited by 84

CNN-Based Automatic Prioritization of Bug Reports

Umer, Q., Liu, H., & Illahi, I. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2019.2959624 · Cited by 84

Towards real-time respiratory motion prediction based on long short-term memory neural networks

Lin, H., Shi, C., Wang, B., Chan, M. F., Tang, X., & Ji, W. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab13fa · Cited by 89

Neural Memory Networks for Seizure Type Classification

Ahmedt‐Aristizabal, D., Fernando, T., Denman, S., Petersson, L., Aburn, M. J., & Fookes, C. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175641 · Cited by 89

Multi-step ahead forecasting of regional air quality using spatial-temporal deep neural networks: A case study of Huaihai Economic Zone

Zhang, K., Thé, J. V. G., Xie, G., & Yu, H. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.123231 · Cited by 83

Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls

Jakobsen, P., Garcia-Ceja, E., Riegler, M. A., Stabell, L. A., Nordgreen, T., Tørresen, J., Fasmer, O. B., & Oedegaard, K. J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231995 · Cited by 83

Recurrent Neural Networks for Edge Intelligence

Lalapura, V. S., Amudha, J., & Satheesh, H. S. (2021) · ACM Computing Surveys · doi:10.1145/3448974 · Cited by 89

Self-Supervised Autoregressive Domain Adaptation for Time Series Data

Ragab, M., Eldele, E., Chen, Z., Wu, M., Kwoh, C. K., & Li, X. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183252 · Cited by 86

3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

Sánchez-Caballero, A., Diz, S. D. L., Fuentes-Jiménez, D., Losada-Gutiérrez, C., Marrón-Romera, M., Casillas-Pérez, D., & Sarker, M. I. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12091-z · Cited by 85

Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform

Li, Y., Jiang, Z. M., Li, H., Hassan, A. E., He, C., Huang, R., Zeng, Z., Wang, M., & Chen, P. (2020) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3385187 · Cited by 83

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis

John, C. C., Ponnusamy, V., Sriharipriya, K. C., & Nandakumar, R. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3069213 · Cited by 83

A deep learning strategy for EMG-based joint position prediction in hip exoskeleton assistive robots

Foroutannia, A., Akbarzadeh-T, M.-R., & Akbarzadeh, A. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103557 · Cited by 85

Gaussian mixture model coupled recurrent neural networks for wind speed interval forecast

Zhu, S., Yuan, X., Xu, Z., Luo, X., & Zhang, H. (2019) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.06.083 · Cited by 82

Learning compact recurrent neural networks

Lu, Z., Sindhwani, V., & Sainath, T. N. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472821 · Cited by 89

Data prediction model in wireless sensor networks based on bidirectional LSTM

Cheng, H., Xie, Z., Wu, L., Yu, Z., & Li, R. (2019) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-019-1511-4 · Cited by 82

Deep Learning Based Channel Estimation for MIMO Systems With Received SNR Feedback

Kang, J., Chun, C.-J., & Kim, I. (2020) · IEEE Access · doi:10.1109/access.2020.3006518 · Cited by 87

Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis

Zhang, Y., Tiwari, P., Song, D., Mao, X., Wang, P., Li, X., & Pandey, H. M. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.10.001 · Cited by 84

Probabilistic estimation of flexural loading capacity of existing RC structures based on observational corrosion-induced crack width distribution using machine learning

Zhang, M., Akiyama, M., Shintani, M., Xin, J., & Frangopol, D. M. (2021) · Structural Safety · doi:10.1016/j.strusafe.2021.102098 · Cited by 82

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

Ghafouri‐Fard, S., Mohammad‐Rahimi, H., Motie, P., Minabi, M. A. S., Taheri, M. N., & Nateghinia, S. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e08143 · Cited by 89

Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets

Wielgosz, M., Skoczeń, A., & Mertik, M. (2017) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2017.06.020 · Cited by 89

An Automated Hyperparameter Search-Based Deep Learning Model for Highway Traffic Prediction

Yi, H., & Bui, K. N. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2987614 · Cited by 83

Hierarchical deep multi-modal network for medical visual question answering

Gupta, D., Suman, S., & Ekbal, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113993 · Cited by 81

Uncertain wind power forecasting using LSTM‐based prediction interval

Banik, A., Behera, C., Sarathkumar, T. V., & Goswami, A. (2020) · IET Renewable Power Generation · doi:10.1049/iet-rpg.2019.1238 · Cited by 69

Learning model predictive control with long short‐term memory networks

Terzi, E., Bonassi, F., Farina, M., & Scattolini, R. (2021) · International Journal of Robust and Nonlinear Control · doi:10.1002/rnc.5519 · Cited by 93

EOGNET: A Novel Deep Learning Model for Sleep Stage Classification Based on Single-Channel EOG Signal

Fan, J., Sun, C., Long, M., Chen, C., & Chen, W. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.573194 · Cited by 81

Time series-based groundwater level forecasting using gated recurrent unit deep neural networks

Lin, H., Gharehbaghi, A., Zhang, Q., Band, S. S., Pai, H.-T., Chau, K., & Mosavi, A. H. (2022) · Engineering Applications of Computational Fluid Mechanics · doi:10.1080/19942060.2022.2104928 · Cited by 82

Continuous Seizure Detection Based on Transformer and Long-Term iEEG

Sun, Y., Jin, W., Si, X., Zhang, X., Cao, J., Wang, L., Yin, S., & Ming, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3199206 · Cited by 84

Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach

Wang, X., Fu, L., Cheng, N., Sun, R., Luan, T. H., Quan, W., & Aldubaikhy, K. (2022) · Remote Sensing · doi:10.3390/rs14174377 · Cited by 84

EEG-Based Parkinson's Disease Recognition via Attention-Based Sparse Graph Convolutional Neural Network

Chang, H., Liu, B., Zong, Y., Lu, C., & Wang, X. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3292452 · Cited by 91

Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-ArtPreprint

Jordão, A., Nazare, A. C., Sena, J., & Schwartz, W. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05226 · Cited by 125

Novel architecture for long short-term memory used in question classification

Xia, W., Zhu, W. Q., Liao, B., Chen, M., Cai, L., & Huang, L. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.020 · Cited by 82

Temporal Modeling Using Dilated Convolution and Gating for Voice-Activity-Detection

Chang, S.-Y., Li, B., Simko, G., Sainath, T. N., Tripathi, A., van den Oord, A., & Vinyals, O. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461921 · Cited by 80

Economic LSTM Approach for Recurrent Neural Networks

Khalil, K., Eldash, O., Kumar, A., & Bayoumi, M. (2019) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2019.2924663 · Cited by 83

Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks

Kraft, B., Jung, M., Körner, M., Requena‐Mesa, C., Cortés, J., & Reichstein, M. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00031 · Cited by 85

Energy efficient for UAV-enabled mobile edge computing networks: Intelligent task prediction and offloading

Wu, G., Miao, Y., Zhang, Y., & Barnawi, A. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.11.037 · Cited by 80

Smoothed LSTM-AE: A spatio-temporal deep model for multiple time-series missing imputation

Dong, L., Li, L., Li, X., Ke, Z., & Hu, Q. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.05.033 · Cited by 82

Forecasting Stock Market Indices Using Padding-Based Fourier Transform Denoising and Time Series Deep Learning Models

Song, D., Baek, A. M. C., & Kim, N. (2021) · IEEE Access · doi:10.1109/access.2021.3086537 · Cited by 85

Graph optimization neural network with spatio-temporal correlation learning for multi-node offshore wind speed forecasting

Geng, X., Xu, L., He, X., & Yu, J. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.08.066 · Cited by 80

A deep learning approach for port congestion estimation and prediction

Peng, W., Bai, X., Yang, D., Yuen, K. F., & Wu, J. (2022) · Maritime Policy & Management · doi:10.1080/03088839.2022.2057608 · Cited by 88

A survey on automatic generation of medical imaging reports based on deep learning

Pang, T., Li, P., & Zhao, L. (2023) · BioMedical Engineering OnLine · doi:10.1186/s12938-023-01113-y · Cited by 88

Improving Commute Experience for Private Car Users via Blockchain-Enabled Multitask Learning

Yang, J., Yang, K., Xiao, Z. Q., Jiang, H., Xu, S., & Dustdar, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3317639 · Cited by 81

Machine Learning Techniques for Classifying Network Anomalies and Intrusions

Li, Z., Rios, A. L. G., Xu, G., & Trajković, L. (2019) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2019.8702583 · Cited by 81

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

Singh, U., Determe, J., Horlin, F., & Doncker, P. D. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.2969588 · Cited by 79

Recurrent Neural Network for Rain Estimation Using Commercial Microwave Links

Habi, H. V., & Messer, H. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3010305 · Cited by 83

Simplifying long short-term memory acoustic models for fast training and decoding

Miao, Y., Li, J., Wang, Y., Zhang, S.-X., & Gong, Y. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472084 · Cited by 78

PV Power Prediction Based on LSTM With Adaptive Hyperparameter Adjustment

Chai, M., Xia, F., Shuotao, H., Peng, D., Cui, C., & Liu, W. (2019) · IEEE Access · doi:10.1109/access.2019.2936597 · Cited by 81

Time Series Prediction Based on Temporal Convolutional Network

Liu, Y., Dong, H., Wang, X., & Han, S. (2019) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis46139.2019.8940265 · Cited by 78

Improved Long Short-Term Memory based anomaly detection with concept drift adaptive method for supporting IoT services

Xu, R., Cheng, Y., Liu, Z., Xie, Y., & Yang, Y. J. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.05.035 · Cited by 82

Emphasizing the entrepreneur or the idea? The impact of text content emphasis on investment decisions in crowdfunding

Wang, W., Chen, W., Zhu, K. X., & Wang, H. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113341 · Cited by 78

Identification of Abnormal Movements in Infants: A Deep Neural Network for Body Part-Based Prediction of Cerebral Palsy

Sakkos, D., McCay, K. D., Marcroft, C., Embleton, N. D., Chattopadhyay, S., & Ho, E. S. L. (2021) · IEEE Access · doi:10.1109/access.2021.3093469 · Cited by 78

Structural dynamic response reconstruction and virtual sensing using a sequence to sequence modeling with attention mechanism

Jiang, K., Han, Q., Du, X., & Ni, P. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103895 · Cited by 84

A novel Multi-LSTM based deep learning method for islanding detection in the microgrid

Özcanlı, A. K., & Baysal, M. (2021) · Electric Power Systems Research · doi:10.1016/j.epsr.2021.107574 · Cited by 83

Cross-modal Contrastive Learning for Speech Translation

Ye, R., Wang, M., & Li, L. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.376 · Cited by 86

Adaptive learning rule for hardware-based deep neural networks using electronic synapse devices

Lim, S., Bae, J.-H., Eum, J.-H., Lee, S., Kim, C.-H., Kwon, D., Park, B., & Lee, J.-H. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3659-y · Cited by 79

Object-independent image-based wavefront sensing approach using phase diversity images and deep learning

Qi, X., Ju, G., Zhang, C., & Xu, S. (2019) · Optics Express · doi:10.1364/oe.27.026102 · Cited by 77

Enhancing Arabic aspect-based sentiment analysis using deep learning models

Al-Dabet, S., Tedmori, S., & AL-Smadi, M. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101224 · Cited by 88

A machine learning framework for accelerating the design process using CAE simulations: An application to finite element analysis in structural crashworthiness

Kohar, C. P., Greve, L., Eller, T., Connolly, D. S., & Inal, K. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.114008 · Cited by 83

Digital twin-driven virtual sensor approach for safe construction operations of trailing suction hopper dredger

Li, M., Lu, Q., Bai, S., Zhang, M., Tian, H., & Qin, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103961 · Cited by 77

SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory

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Recent advancements in deep learning based lung cancer detection: A systematic review

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A Novel Variant of LSTM Stock Prediction Method Incorporating Attention Mechanism

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A Comprehensive Review on Sensor-Based Electronic Nose for Food Quality and Safety

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Classification of short time series in early Parkinson s disease with deep learning of fuzzy recurrence plots

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An Empirical Exploration of Recurrent Network Architectures

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Simplified Long Short-term Memory Recurrent Neural Networks: part IPreprint

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Benchmarking of LSTM NetworksPreprint

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On the State of the Art of Evaluation in Neural Language ModelsPreprint

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