Cited by 6,955Neural Networks and ApplicationsSpeech Recognition and SynthesisTopic Modeling

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

LSTM: A Search Space Odyssey

Open access

Greff, Klaus, Srivastava, Rupesh K., Koutnik, Jan, Steunebrink, Bas R., Schmidhuber, Jurgen·2017·IEEE Transactions on Neural Networks and Learning Systems

DOI 10.1109/tnnls.2016.2582924

Open this paper in CoChat — free account Open PDF

Abstract

Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants. In this paper, we present the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling. The hyperparameters of all LSTM variants for each task were optimized separately using random search, and their importance was assessed using the powerful functional ANalysis Of VAriance framework. In total, we summarize the results of 5400 experimental runs ( ≈ 15 years of CPU time), which makes our study the largest of its kind on LSTM networks. Our results show that none of the variants can improve upon the standard LSTM architecture significantly, and demonstrate the forget gate and the output activation function to be its most critical components. We further observe that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1109/tnnls.2016.2582924

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Self-citation
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Untitled work
(2012) · Cited by 8,009
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152
Self-citation
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143891 · Cited by 5,370
Self-citation
Long-term recurrent convolutional networks for visual recognition and description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228
Untitled work
(2013) · Cited by 3,740

Flagged first, then by citations

Show all 53 references

Research works printed but not linked: 2

gers, 2002, DEKF-LSTM, Proc European Symp Artificial Neural Networks (ESANN), 369
robinson, 1987, The utility driven dynamic error propagation network

Sorted by citations

Items 501–600 of 1,013

A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships
Ellefsen, A. L., Æsøy, V., Ushakov, S., & Zhang, H. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2019.2907402 · Cited by 116
From Load to Net Energy Forecasting: Short-Term Residential Forecasting for the Blend of Load and PV Behind the Meter
Razavi, S., Arefi, A., Ledwich, G. F., Nourbakhsh, G., Smith, D. B., & Minakshi, M. (2020) · IEEE Access · doi:10.1109/access.2020.3044307 · Cited by 104
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2016) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v30i1.9883 · Cited by 1,645
Generative Models for Effective ML on Private, Decentralized Datasets
Augenstein, S., McMahan, H. B., Ramage, D., Ramaswamy, S., Kairouz, P., Chen, M., Mathews, R., & Arcas, B. A. Y. (2019) · arXiv · doi:10.48550/arxiv.1911.06679 · Cited by 103
LSTM, GRU, Highway and a Bit of Attention: An Empirical Overview for Language Modeling in Speech Recognition
Irie, K., Tüske, Z., Alkhouli, T., Schlüter, R., & Ney, H. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-491 · Cited by 103
Short-Term Traffic Flow Forecasting Method With M-B-LSTM Hybrid Network
Qu, Z., Li, H., Li, Z., & Tao, Z. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3009725 · Cited by 105
Deep learning for brake squeal: Brake noise detection, characterization and prediction
Stender, M., Tiedemann, M., Spieler, D., Schoepflin, D., Hoffmann, N., & Oberst, S. M. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107181 · Cited by 113
Deep learning in electron microscopy
Ede, J. M. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd614 · Cited by 118
A hybrid prognostic method based on gated recurrent unit network and an adaptive Wiener process model considering measurement errors
Chen, Z., Xia, T., Li, Y., & Pan, E. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107785 · Cited by 103
Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation
Harfiya, L. N., Chang, C.-C., & Li, Y. (2021) · Sensors · doi:10.3390/s21092952 · Cited by 110
VictimFinder: Harvesting rescue requests in disaster response from social media with BERT
Zhou, B., Zou, L., Mostafavi, A., Lin, B., Yang, M., Gharaibeh, N. G., Cai, H., Abedin, J., & Mandal, D. (2022) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2022.101824 · Cited by 113
Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory
Ma, N., Yin, H., & Wang, K. (2023) · Energies · doi:10.3390/en16145240 · Cited by 103
Source Code Authorship Attribution Using Long Short-Term Memory Based Networks
Alsulami, B., Dauber, E., Harang, R., Mancoridis, S., & Greenstadt, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66402-6_6 · Cited by 106
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
Hammou, B. A., Lahcen, A. A., & Mouline, S. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102122 · Cited by 109
A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting
Urolagin, S., Sharma, N., & Datta, T. K. (2021) · Energy · doi:10.1016/j.energy.2021.120963 · Cited by 107
Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam
Phuoc, T., Anh, P. T. K., Tam, P. H., & Nguyen, V. C. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02807-x · Cited by 138
Efficient Online Learning Algorithms Based on LSTM Neural Networks
Ergen, T., & Kozat, S. S. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2741598 · Cited by 101
Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network
Kang, K., Hongbin, S., Chengkang, Z., & Brown, C. (2019) · Evolutionary Intelligence · doi:10.1007/s12065-018-00196-0 · Cited by 101
Explaining and Interpreting LSTMs
Arras, L., Arjona-Medina, J., Widrich, M., Montavon, G., Gillhofer, M., Müller, K.-R., Hochreiter, S., & Samek, W. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_11 · Cited by 111
Status of research and development of learning-based approaches in nuclear science and engineering: A review
Gomez-Fernandez, M., Higley, K. A., Tokuhiro, A., Welter, K. B., Wong, W., & Yang, H. (2019) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2019.110479 · Cited by 105
A Data-Driven Inertial Navigation/Bluetooth Fusion Algorithm for Indoor Localization
Chen, J., Zhou, B., Bao, S., Liu, X., Gu, Z., Li, L., Zhao, Y., Zhu, J., & Li, Q. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3089516 · Cited by 100
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction
Nasiri, H., & Ebadzadeh, M. M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.08.032 · Cited by 104
Single gated RNN with differential weighted information storage mechanism and its application to machine RUL prediction
Xiang, S., Li, P., Yi, H., Luo, J. T., & Qin, Y. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109741 · Cited by 101
Deep learning for ECG classification: A comparative study of 1D and 2D representations and multimodal fusion approaches
Narotamo, H., Dias, M., Santos, R., Carreiro, A. V., Gamboa, H. F. S., & Silveira, M. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106141 · Cited by 124
Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework
Lin, Y., Dai, X., Li, L., & Wang, F. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2857224 · Cited by 101
Attention in convolutional LSTM for gesture recognition
Zhang, L., Zhu, G., Mei, L., Shen, P., Shah, S. A. A., & Bennamoun, M. (2018) · neural information processing systems · Cited by 98
A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-Chain Data
Kim, G., Shin, D.-H., Choi, J. G., & Lim, S. (2022) · IEEE Access · doi:10.1109/access.2022.3177888 · Cited by 115
Green learning: Introduction, examples and outlook
Kuo, C. ‐. J., & Madni, A. M. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103685 · Cited by 119
Multi-Aspect-Aware Bidirectional LSTM Networks for Synthetic Aperture Radar Target Recognition
Zhang, F., Chen, H., Yin, Q., Li, W., Li, H.-C., & Hong, W. (2017) · IEEE Access · doi:10.1109/access.2017.2773363 · Cited by 104
A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery
Marbán, A., Srinivasan, V., Samek, W., Fernández, J., & Casals, A. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.01.011 · Cited by 101
Sequential Variational Autoencoders for Collaborative Filtering
Sachdeva, N., Manco, G., Ritacco, E., & Pudi, V. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291007 · Cited by 99
Transfer Learning-Based Hybrid Remaining Useful Life Prediction for Lithium-Ion Batteries Under Different Stresses
Pan, D., Li, H., & Wang, S. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3142757 · Cited by 99
Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET
Sepasgozar, S. S., & Pierre, S. (2022) · IEEE Access · doi:10.1109/access.2022.3144112 · Cited by 99
Vehicle Trajectory Prediction Method Coupled With Ego Vehicle Motion Trend Under Dual Attention Mechanism
Guo, H., Meng, Q., Cao, D., Chen, H., Liu, J., & Shang, B. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3163136 · Cited by 99
Deep learning for steganalysis of diverse data types: A review of methods, taxonomy, challenges and future directions
Kheddar, H., Hemis, M., Himeur, Y., Megías, D., & Amira, A. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127528 · Cited by 123
Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical 3D-Data
Andermatt, S., Pezold, S., & Cattin, P. C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_15 · Cited by 103
SMOTE and Gaussian Noise Based Sensor Data Augmentation
Arslan, M. S., Güzel, M., Demirci, M., & Özdemi̇r, S. (2019) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk.2019.8907003 · Cited by 106
A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatio-temporal patterns
Fu, X., Wu, M., Ponnarasu, S., & Zhang, L. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118721 · Cited by 98
Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics
Matz, S., Bukow, C. S., Peters, H., Deacons, C., Dinu, A., & Stachl, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-32484-w · Cited by 123
Time-Series Forecasting of Mortality Rates using Deep Learning
Perla, F., Richman, R., Scognamiglio, S., & Wüthrich, M. V. (2020) · Scandinavian Actuarial Journal · doi:10.1080/03461238.2020.1867232 · Cited by 104
Time–frequency time–space LSTM for robust classification of physiological signals
Pham, T. D. (2021) · Scientific Reports · doi:10.1038/s41598-021-86432-7 · Cited by 110
A deep neural network-based method for deep information extraction using transfer learning strategies to support automated compliance checking
Zhang, R., & El-Gohary, N. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103834 · Cited by 103
Finding A Needle in a Haystack: Automated Mining of Silent Vulnerability Fixes
Zhou, J., Pacheco, M., Wan, Z., Xia, X., Lo, D., Wang, Y., & Hassan, A. E. (2021) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase51524.2021.9678720 · Cited by 97
Hate speech and offensive language detection in Dravidian languages using deep ensemble framework
Roy, P. K., Bhawal, S., & Subalalitha, C. N. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101386 · Cited by 111
Automatic sleep stage classification: From classical machine learning methods to deep learning
Sekkal, R. N., Bereksi-Reguig, F., Fernández, D. R., Dib, N., & Sekkal, S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103751 · Cited by 104
A Survey on Time-Series Pre-Trained Models
Ma, Q., Liu, Z., Zheng, Z., Huang, Z., Zhu, S., Yu, Z., & Kwok, J. T.-Y. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3475809 · Cited by 133
Federated Learning Of Out-Of-Vocabulary Words
Chen, M., Mathews, R., Ouyang, T., & Beaufays, F. (2019) · arXiv · doi:10.48550/arxiv.1903.10635 · Cited by 97
Visual closed-loop control for pouring liquids
Schenck, C., & Fox, D. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989307 · Cited by 97
Natural Language Statistical Features of LSTM-Generated Texts
Lippi, M., Montemurro, M. A., Esposti, M. D., & Cristadoro, G. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2890970 · Cited by 97
Sequential deep learning from NTSB reports for aviation safety prognosis
Zhang, X., Srinivasan, P., & Mahadevan, S. (2021) · Safety Science · doi:10.1016/j.ssci.2021.105390 · Cited by 103
Epileptic Seizure Detection: A Deep Learning ApproachPreprint
Hussein, R., Palangi, H., Ward, R. K., & Wang, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.09848 · Cited by 136
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series ForecastingPreprint
Chen, Y., Segovia-Domínguez, I., & Gel, Y. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04100 · Cited by 96
Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks
Chakravarti, S., & Alla, S. R. M. (2019) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2019.00017 · Cited by 94
Carbon price prediction considering climate change: A text-based framework
Xie, Q., Hao, J., Li, J., & Zheng, X. (2022) · Economic Analysis and Policy · doi:10.1016/j.eap.2022.02.010 · Cited by 96
Robust recurrent neural networks for time series forecasting
Zhang, X., Zhong, C., Zhang, J. J., Ng, W. W. Y., & Ng, W. W. Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.037 · Cited by 102
Early Forecasting of Rice Blast Disease Using Long Short-Term Memory Recurrent Neural Networks
Kim, Y., Roh, J., & Kim, H. Y. (2017) · Sustainability · doi:10.3390/su10010034 · Cited by 98
DeepML: Deep LSTM for Indoor Localization with Smartphone Magnetic and Light Sensors
Wang, X., Yu, Z., & Mao, S. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8422562 · Cited by 94
Temporal Prediction of Multiapplication Consolidated Workloads in Distributed Clouds
Bi, J., Yuan, H., & Zhou, M. (2019) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2019.2895801 · Cited by 96
Deep Neural Network Based Inertial Odometry Using Low-Cost Inertial Measurement Units
Chen, C., Lu, C. X., Wahlström, J., Markham, A., & Trigoni, N. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2960780 · Cited by 98
Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
Liao, Q., Zhang, Q., Feng, X., Huang, H., Xu, H., Tian, B., Liu, J., Yu, Q., Guo, N., Liu, Q., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-01937-1 · Cited by 101
Air quality forecasting with hybrid LSTM and extended stationary wavelet transform
Zeng, Y., Chen, J., Jin, N., Jin, X., & Du, Y. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.108822 · Cited by 96
Deep learning techniques for suicide and depression detection from online social media: A scoping review
Malhotra, A., & Jindal, R. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109713 · Cited by 102
Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment
Abdulazeez, D. H., & Askar, S. K. (2023) · IEEE Access · doi:10.1109/access.2023.3241881 · Cited by 112
Model predictive control energy management strategy integrating long short-term memory and dynamic programming for fuel cell vehicles
Song, K., Huang, X., Xu, H., Sun, H., Chen, Y., & Huang, D. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2023.12.245 · Cited by 94
Time Series Forecasting Using LSTM Networks: A Symbolic ApproachPreprint
Elsworth, S., & Güttel, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05672 · Cited by 162
Continuous Security in IoT Using Blockchain
Agrawal, R., Verma, P., Sonanis, R., Goel, U., De, A., Kondaveeti, S. A., & Shekhar, S. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462513 · Cited by 90
A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning
Sun, C., Chen, C., Li, W., Fan, J., & Chen, W. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2937558 · Cited by 94
Intelligent Traffic Adaptive Resource Allocation for Edge Computing-Based 5G Networks
Chen, M., Miao, Y., Gharavi, H., Hu, L., & Humar, I. (2019) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2019.2953061 · Cited by 93
Forex exchange rate forecasting using deep recurrent neural networks
Dautel, A. J., Härdle, W. K., Lessmann, S., & Seow, H. (2020) · Digital Finance · doi:10.1007/s42521-020-00019-x · Cited by 105
A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption
Zhou, X., Lin, W., Kumar, R., Cui, P. Y., & Ma, Z. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118078 · Cited by 98
LSTM with particle Swam optimization for sales forecasting
He, Q.-Q., Wu, C., & Si, Y. (2022) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2022.101118 · Cited by 100
Reliable Semantic Communication System Enabled by Knowledge Graph
Jiang, S., Liu, Y., Zhang, Y., Luo, P., Cao, K., Xiong, J., Zhao, H., & Wei, J. (2022) · Entropy · doi:10.3390/e24060846 · Cited by 94
Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments
Jacobson, N. C., & Bhattacharya, S. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.104013 · Cited by 92
A Hybrid CNN–LSTM Algorithm for Online Defect Recognition of CO2 Welding
Liu, T., Bao, J., Wang, J., & Zhang, Y. (2018) · Sensors · doi:10.3390/s18124369 · Cited by 92
Deep Sequential Segmentation of Organs in Volumetric Medical Scans
Novikov, A. A., Major, D., Wimmer, M., Lenis, D., & Bühler, K. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2881678 · Cited by 91
LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning
Sarkar, M., & Bruyn, A. D. (2020) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2020.07.002 · Cited by 107
An intelligent tunnel firefighting system and small-scale demonstration
Wu, X., Zhang, X., Jiang, Y., Huang, X., Huang, G. Q., & Usmani, A. S. (2021) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2021.104301 · Cited by 98
Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms
Chen, H., Li, X., Wu, Y., Zuo, L., Lu, M., & Zhou, Y. (2022) · Buildings · doi:10.3390/buildings12030302 · Cited by 100
Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications
Troiano, L., Mejuto-Villa, E., & Loia, V. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2811377 · Cited by 94
Dengue forecasting in São Paulo city with generalized additive models, artificial neural networks and seasonal autoregressive integrated moving average models
Baquero, O. S., Santana, L. M. R., & Neto, F. C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195065 · Cited by 94
ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants
Pisa, I., Santín, I., Vicario, J. L., Morell, A., & Vilanova, R. (2019) · Sensors · doi:10.3390/s19061280 · Cited by 91
A novel approach for predicting tool remaining useful life using limited data
Li, H., Wang, W., Li, Z., Dong, L., & Li, Q. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106832 · Cited by 91
Capsule Attention for Multimodal EEG-EOG Representation Learning With Application to Driver Vigilance Estimation
Zhang, G., & Etemad, A. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3089594 · Cited by 102
Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks
Boroumand, A., Ghose, S., Akin, B., Narayanaswami, R., Junior, G. F. D. O., Ma, X., Shiu, E., & Mutlu, O. (2021) · International Conference on Parallel Architectures and Compilation Techniques (PACT) · doi:10.1109/pact52795.2021.00019 · Cited by 107
A Hybrid Spatiotemporal Deep Model Based on CNN and LSTM for Air Pollution Prediction
Tsokov, S., Lazarova, M. K., & Aleksieva-Petrova, A. (2022) · Sustainability · doi:10.3390/su14095104 · Cited by 105
Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security
Maaz, M., Ahmed, G., Al‐Shamayleh, A. S., Akhunzada, A., Siddiqui, S., & Al-Ghushami, A. H. (2024) · IEEE Access · doi:10.1109/access.2024.3482005 · Cited by 93
AMNet: Memorability Estimation with Attention
Fajtl, J., Argyriou, V., Monekosso, D., & Remagnino, P. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00666 · Cited by 89
Redundancy and Attention in Convolutional LSTM for Gesture Recognition
Zhu, G., Zhang, L., Yang, L., Mei, L., Shah, S. A. A., Bennamoun, M., & Shen, P. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2919764 · Cited by 90
Air Quality Forecasting using LSTM RNN and Wireless Sensor Networks
Belavadi, S. V., Rajagopal, S., Ranjani, R., & Mohan, R. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.036 · Cited by 98
Work modes recognition and boundary identification of MFR pulse sequences with a hierarchical seq2seq LSTM
Li, Y., Zhu, M., Ma, Y., & Yang, J. (2020) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2020.0060 · Cited by 92
RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process
Curreri, F., Patané, L., & Xibilia, M. G. (2021) · Sensors · doi:10.3390/s21030823 · Cited by 91
Deep Learning for Spectrum Sensing in Cognitive Radio
Solanki, S., Dehalwar, V., & Choudhary, J. (2021) · Symmetry · doi:10.3390/sym13010147 · Cited by 90
A Graph Convolutional Stacked Bidirectional Unidirectional-LSTM Neural Network for Metro Ridership Prediction
Chen, P., Fu, X., & Wang, X. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3065404 · Cited by 92
A novel approach for water quality classification based on the integration of deep learning and feature extraction techniques
Dilmi, S., & Ladjal, M. (2021) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2021.104329 · Cited by 92
Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression Programming
Cao, B., Zhao, J., Liu, X., Arabas, J., Tanveer, M., Singh, A. K., & Lv, Z. (2022) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2022.3141761 · Cited by 92
Water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system
Wan, X., Li, X., Wang, X., Yi, X., Zhao, Y., He, X., Wu, R., & Huang, M. (2022) · Environmental Research · doi:10.1016/j.envres.2022.112942 · Cited by 90
Predicting Household Electric Power Consumption Using Multi-step Time Series with Convolutional LSTM
Cascone, L., Sadiq, S., Ullah, S., Mirjalili, S., Siddiqui, H. U. R., & Umer, M. (2022) · Big Data Research · doi:10.1016/j.bdr.2022.100360 · Cited by 99
Coupling SWAT and Bi-LSTM for improving daily-scale hydro-climatic simulation and climate change impact assessment in a tropical river basin
Yang, S., Tan, M. L., Song, Q., He, J., Yao, N., Li, X., & Yang, X. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.117244 · Cited by 90
Machine Learning Methods for Spacecraft Telemetry Mining
Ibrahim, S. K., Ahmed, A. M., Zeidan, M. A. E., & Ziedan, I. (2018) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2018.2876586 · Cited by 91
Functional and Contextual Attention-Based LSTM for Service Recommendation in Mashup Creation
Shi, M., Tang, Y., & Liu, J. (2018) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2018.2877363 · Cited by 91

Authors: Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J.
Affiliations: University of Applied Sciences and Arts of Southern Switzerland (CH)
ORCID: Greff, Klaus
Venue: IEEE Transactions on Neural Networks and Learning Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2017-10
Volume: 28
Issue: 10
Pages: 2222-2232
ISSN: 2162-237X (ISSN-L)
2162-2388
DOI: 10.1109/tnnls.2016.2582924
OpenAlex: W1689711448
Cited by: 6,955
References: 53
Citations by year: 2026: 602 · 2025: 812 · 2024: 810 · 2023: 822 · 2022: 872 · 2021: 881 · 2020: 778 · 2019: 686 · 2018: 383 · 2017: 188
FWCI: 370.42
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Swiss National Science Foundation through the Project Theory and Practice of Reinforcement Learning 2, and through the Project Advanced Reinforcement Learning (138219, 156682)
European Institute of Innovation and Technology through the Project Neural Dynamics, through the Project NASCENCE and through the Project WAY (FP7-ICT-270247, FP7-ICT-317662, FP7-ICT-288551)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: long short-term memory, recurrent neural networks, hyperparameter optimization, random search, functional analysis of variance, forget gate, speech recognition, handwriting recognition

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

LSTM: A Search Space Odyssey

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks