Effective Approaches to Attention-based Neural Machine Translation

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

BLEU: a method for automatic evaluation of machine translation

Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248

Recurrent Models of Visual AttentionPreprint

Mnih, V., Heess, N., Graves, A., & Kavukcuoglu, K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.6247 · Cited by 3,393

Statistical phrase-based translation

Koehn, P., Och, F. J., & Marcu, D. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073445.1073462 · Cited by 3,265

Recurrent Neural Network RegularizationPreprint

Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.2329 · Cited by 3,174

Recurrent Continuous Translation Models

Kalchbrenner, N., & Blunsom, P. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1176 · Cited by 1,277

Statistical Phrase-Based Translation

Koehn, P., Och, F. J., & Marcu, D. (2003) · doi:10.21236/ada461156 · Cited by 970

EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction

Zhou, F., Zhou, H., Yang, Z., & Yang, L. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.07.065 · Cited by 157

Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks

Phan, H., Andreotti, F., Cooray, N., Chén, O. Y., & Vos, M. D. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512480 · Cited by 152

GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks

Li, J., Louri, A., Karanth, A., & Bunescu, R. (2021) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca51647.2021.00070 · Cited by 160

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Xie, F., Yuan, H., Ning, Y., Ong, M. E. H., Feng, M., Hsu, W., Chakraborty, B., & Liu, N. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103980 · Cited by 176

COGS: A Compositional Generalization Challenge Based on Semantic Interpretation

Kim, N., & Linzen, T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.731 · Cited by 153

Automatically Neutralizing Subjective Bias in Text

Pryzant, R., Martinez, R. D., Dass, N., Kurohashi, S., Jurafsky, D., & Yang, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5385 · Cited by 171

How to Find Your Friendly Neighborhood: Graph Attention Design with Self-SupervisionPreprint

Kim, D., & Oh, A. H. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.04879 · Cited by 211

Deep Neural Network Initialization With Decision Trees

Humbird, K., Peterson, J. L., & McClarren, R. G. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2869694 · Cited by 153

Sparse Tensor Core

Zhu, M., Zhang, T., Gu, Z., & Xie, Y. (2019) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3352460.3358269 · Cited by 157

Multi-channel Transformers for Multi-articulatory Sign Language Translation

Camgöz, N. C., Koller, O., Hadfield, S., & Bowden, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66823-5_18 · Cited by 156

Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach

Sawhney, R., Agarwal, S., Wadhwa, A., Derr, T., & Shah, R. R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16127 · Cited by 171

A Long-Text Classification Method of Chinese News Based on BERT and CNN

Chen, X., Cong, P., & Lv, S. (2022) · IEEE Access · doi:10.1109/access.2022.3162614 · Cited by 168

Benchmarking and Analyzing Deep Neural Network Training

Zhu, H., Akrout, M., Zheng, B., Pelegris, A., Jayarajan, A., Phanishayee, A., Schroeder, B., & Pekhimenko, G. G. (2018) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc.2018.8573476 · Cited by 203

Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision, and Challenges

Zhou, J., Huang, J. X., Chen, Q., Hu, Q., Wang, T., & He, L. (2019) · IEEE Access · doi:10.1109/access.2019.2920075 · Cited by 157

Deep learning models for cuffless blood pressure monitoring from PPG signals using attention mechanism

El-Hajj, C., & Kyriacou, P. A. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102301 · Cited by 156

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Chen, X., Wu, Y., He, X., & Ming, W. (2023) · IEEE Access · doi:10.1109/access.2023.3339561 · Cited by 167

Generating Responses with a Specific Emotion in Dialog

Song, Z., Zheng, X., Liu, L., Xu, M., & Huang, X. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1359 · Cited by 156

Global Encoding for Abstractive Summarization

Lin, J., Sun, X., Ma, S., & Su, Q. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2027 · Cited by 150

An Attentional Model for Speech Translation Without Transcription

Duong, L., Anastasopoulos, A., Chiang, D., Bird, S., & Cohn, T. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1109 · Cited by 158

Coevolutionary Recommendation Model

Lu, Y., Dong, R., & Smyth, B. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186158 · Cited by 165

GTC: Guided Training of CTC towards Efficient and Accurate Scene Text Recognition

Hu, W., Cai, X., Hou, J. Y., Yi, S., & Lin, Z. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6735 · Cited by 149

Generate Neural Template Explanations for Recommendation

Li, L., Zhang, Y., & Chen, L. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3411992 · Cited by 149

Speech emotion recognition using recurrent neural networks with directional self-attention

Li, D., Liu, J., Yang, Z., Sun, L., & Wang, Z. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114683 · Cited by 154

SeaFormer++: Squeeze-Enhanced Axial Transformer for Mobile Visual Recognition

Wan, Q., Huang, Z., Lu, J., Yu, G., & Zhang, L. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02345-2 · Cited by 182

Applying Deep Learning to the Cache Replacement Problem

Shi, Z., Huang, X., Jain, A., & Lin, C. (2019) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3352460.3358319 · Cited by 169

Dreem Open Datasets: Multi-Scored Sleep Datasets to Compare Human and Automated Sleep Staging

Guillot, A., Sauvet, F., During, E., & Thorey, V. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3011181 · Cited by 148

Long short-term memory neural network with weight amplification and its application into gear remaining useful life prediction

Xiang, S., Qin, Y., Zhu, C., Wang, Y., & Chen, H. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103587 · Cited by 151

Code smell detection by deep direct-learning and transfer-learning

Sharma, T., Efstathiou, V., Λουρίδας, Π., & Spinellis, D. D. (2021) · Journal of Systems and Software · doi:10.1016/j.jss.2021.110936 · Cited by 167

IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing

Zhu, T., Kuang, L., Daniels, J., Herrero, P., Li, K., & Georgiou, P. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3143375 · Cited by 164

Contextual Parameter Generation for Universal Neural Machine Translation

Platanios, E. A., Sachan, M., Neubig, G., & Mitchell, T. M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1039 · Cited by 147

DQNViz: A Visual Analytics Approach to Understand Deep Q-Networks

Wang, J., Gou, L., Shen, H., & Yang, H. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864504 · Cited by 153

An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

Ran, X., Shan, Z., Fang, Y., & Lin, C. (2019) · Sensors · doi:10.3390/s19040861 · Cited by 148

CAM-RNN: Co-Attention Model Based RNN for Video Captioning

Zhao, B., Li, X., & Lu, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2916757 · Cited by 147

RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data

Xu, Y., Biswal, S., Deshpande, S. R., Maher, K. O., & Sun, J. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220051 · Cited by 144

Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study

Li, F., Jin, Y., Liu, W., Rawat, B. P. S., Cai, P., & Yu, H. (2019) · JMIR Medical Informatics · doi:10.2196/14830 · Cited by 161

Automatic Dialogue Generation with Expressed Emotions

Huang, C., Zai͏̈ane, O. R., Trabelsi, A., & Dziri, N. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2008 · Cited by 146

Graph2Seq: Graph to Sequence Learning with Attention-based Neural NetworksPreprint

Xu, K., Wu, L., Wang, Z., Feng, Y., Witbrock, M., & Sheinin, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.00823 · Cited by 225

Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation

Xu, J., Ren, X., Lin, J., & Sun, X. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1428 · Cited by 208

Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling

Shen, T., Zhou, T., Long, G., Jiang, J. B., Wang, S., & Chengqi Zhang, B. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/604 · Cited by 154

Learning to Parse and Translate Improves Neural Machine Translation

Eriguchi, A., Tsuruoka, Y., & Cho, K. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2012 · Cited by 152

Coverage Embedding Models for Neural Machine Translation

Mi, H., Sankaran, B., Wang, Z., & Ittycheriah, A. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1096 · Cited by 144

Neural machine translation: A review of methods, resources, and tools

Tan, Z., Wang, S., Yang, Z., Chen, G., Huang, X., Sun, M., & Liu, Y. (2020) · AI Open · doi:10.1016/j.aiopen.2020.11.001 · Cited by 193

Protein sequence design with deep generative models

Wu, Z., Johnston, K. E., Arnold, F. H., & Yang, K. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.004 · Cited by 152

State of health estimation for lithium-ion batteries based on hybrid attention and deep learning

Zhao, H., Chen, Z., Shu, X., Shen, J., Lei, Z., & Zhang, Y. (2022) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.109066 · Cited by 142

Regression Transformer enables concurrent sequence regression and generation for molecular language modelling

Born, J., & Manica, M. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00639-z · Cited by 169

An attention-based effective neural model for drug-drug interactions extraction

Zheng, W., Lin, H., Luo, L., Zhao, Z., Li, Z., Zhang, Y., Yang, Z., & Wang, J. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1855-x · Cited by 148

Neural Open Information Extraction

Cui, L., Wei, F., & Zhou, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2065 · Cited by 158

Automatically learning construction injury precursors from text

Baker, H., Hallowell, M. R., & Tixier, A. J. ‐. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103145 · Cited by 155

Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification

Yao, H., Zhang, X., Zhou, X., & Liu, S. (2019) · Cancers · doi:10.3390/cancers11121901 · Cited by 143

A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment

Zhu, Y., Zhang, W., Chen, Y., & Gao, H. (2019) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-019-1605-z · Cited by 151

Multi-level attention network: application to brain tumor classification

Shaik, N. S., & Cherukuri, T. K. (2021) · Signal Image and Video Processing · doi:10.1007/s11760-021-02022-0 · Cited by 144

DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer

Li, B., Zhao, Y., Shi, Z., & Sheng, L. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20014 · Cited by 143

Is Word Segmentation Necessary for Deep Learning of Chinese Representations?

Li, X., Meng, Y., Sun, X., Han, Q., Yuan, A., & Li, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1314 · Cited by 153

Domain Control for Neural Machine Translation

SYSTRAN International, P., Kobus, C., Crego, J., & Sénellart, J. (2017) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-049-6_049 · Cited by 143

Learning context-aware structural representations to predict antigen and antibody binding interfaces

Pittala, S., & Bailey‐Kellogg, C. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa263 · Cited by 142

Transformer Encoder With Multi-Modal Multi-Head Attention for Continuous Affect Recognition

Chen, H., Jiang, D., & Hichem Sahli, B. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3037496 · Cited by 146

Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network

Cheng, J., Zhao, S., Zhang, J., King, I., Zhang, X., & Wang, H. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3133037 · Cited by 141

Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition

Michael, J., Labahn, R., Grüning, T., & Zollner, J. (2019) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2019.00208 · Cited by 170

Listening while speaking: Speech chain by deep learning

Tjandra, A., Sakti, S., & Nakamura, S. (2017) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2017.8268950 · Cited by 141

Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions

Ye, H., Jiang, X., Luo, Z., & Chao, W. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1168 · Cited by 159

Feature Extraction Using an RNN Autoencoder for Skeleton-Based Abnormal Gait Recognition

Jun, K., Lee, D.-W., Lee, K., Lee, S., & Kim, M. S. (2020) · IEEE Access · doi:10.1109/access.2020.2967845 · Cited by 145

KVT: k-NN Attention for Boosting Vision Transformers

Wang, P., Wang, X., Wang, F., Lin, M., Chang, S., Li, H., & Jin, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20053-3_17 · Cited by 142

HTC-Net: A hybrid CNN-transformer framework for medical image segmentation

Tang, H., Chen, Y., Wang, T., Zhou, Y., Zhao, L., Gao, Q., Du, M., Tan, T., Zhang, X., & Tong, T. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105605 · Cited by 147

Deeper Attention to Abusive User Content Moderation

Pavlopoulos, J., Malakasiotis, P., & Androutsopoulos, I. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1117 · Cited by 151

On the diversity of multi-head attention

Li, J., Wang, X., Tu, Z., & Lyu, M. R.-T. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.038 · Cited by 147

A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis

Gu, S., Zhang, L., Hou, Y., & Song, Y. (2018) · International Conference on Computational Linguistics · Cited by 139

Machine translation from text to sign language: a systematic review

Kahlon, N. K., & Singh, W. (2021) · Universal Access in the Information Society · doi:10.1007/s10209-021-00823-1 · Cited by 139

End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism

Eom, H., Lee, D., Han, S., Hariyani, Y. S., Lim, Y., Sohn, I., Park, K., & Park, C. (2020) · Sensors · doi:10.3390/s20082338 · Cited by 144

Modeling Source Syntax for Neural Machine Translation

Li, J., Xiong, D., Tu, Z., Zhu, M., Zhang, M., & Zhou, G. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1064 · Cited by 137

Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future

Chatterjee, J., & Dethlefs, N. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111051 · Cited by 155

Deep reinforcement and transfer learning for abstractive text summarization: A review

Alomari, A., Idris, N. B., Sabri, A. Q. M., & Alsmadi, I. M. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101276 · Cited by 149

A data-driven method for multi-step-ahead prediction and long-term prognostics of proton exchange membrane fuel cell

Benaggoune, K., Yue, M., Jemeï, S., & Noureddine Zerhouni, A. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118835 · Cited by 137

Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey

Pudi, D., Boppu, S., Manikandan, M. S., & Cenkeramaddi, L. R. (2022) · IEEE Access · doi:10.1109/access.2022.3229767 · Cited by 153

C 2 SLR: Consistency-enhanced Continuous Sign Language Recognition

Zuo, R., & Mak, B. K.-W. (2022) · 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/cvpr52688.2022.00507 · Cited by 146

A Dataset for Multi-Target Stance Detection

Sobhani, P., Inkpen, D. Z., & Zhu, X. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2088 · Cited by 136

Deep Reinforcement Learning for Safe Local Planning of a Ground Vehicle in Unknown Rough Terrain

Josef, S., & Degani, A. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3011912 · Cited by 136

Personalized Blood Glucose Prediction for Type 1 Diabetes Using Evidential Deep Learning and Meta-Learning

Zhu, T., Li, K., Herrero, P., & Georgiou, P. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3187703 · Cited by 156

Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research

Storey, V. C., Yue, W. T., Zhao, J. L., & Lukyanenko, R. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10581-7 · Cited by 208

Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation

Camgöz, N. C., Koller, O., Hadfield, S., & Bowden, R. (2020) · 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/cvpr42600.2020.01004 · Cited by 198

A Copy-Augmented Sequence-to-Sequence Architecture Gives Good Performance on Task-Oriented Dialogue

Eric, M., & Manning, C. D. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2075 · Cited by 142

Supervised Attentions for Neural Machine Translation

Mi, H., Wang, Z., & Ittycheriah, A. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1249 · Cited by 134

Federated Continual Learning via Knowledge Fusion: A Survey

Yang, X., Yu, H., Gao, X., Wang, H., Zhang, J., & Li, T. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3363240 · Cited by 146

The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Jin, X.-B., Robert Jeremiah, R. J., Su, T.-L., Bai, Y.-T., & Kong, J.-L. (2021) · Sensors · doi:10.3390/s21062085 · Cited by 137

Retrospective Encoders for Video Summarization

Zhang, K., Grauman, K. L., & Sha, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_24 · Cited by 132

CNN–MHSA: A Convolutional Neural Network and multi-head self-attention combined approach for detecting phishing websites

Xiao, X., Zhang, D., Hu, G., Jiang, Y., & Xia, S. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.02.013 · Cited by 136

Sequence-to-Sequence Acoustic Modeling for Voice Conversion

Zhang, J.-X., Ling, Z.-H., Liu, L.-J., Jiang, Y., & Dai, L.-R. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2892235 · Cited by 134

Introduction to machine and deep learning for medical physicists

Cui, S., Tseng, H., Pakela, J. M., Haken, R. K. T., & Naqa, I. E. (2020) · Medical Physics · doi:10.1002/mp.14140 · Cited by 133

Neural Machine Translation with Deep Attention

Zhang, B., Xiong, D., & Su, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2876404 · Cited by 138

Irony detection via sentiment-based transfer learning

Zhang, S., Zhang, X., Chan, J., & Rosso, P. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.04.006 · Cited by 139

Instance Weighting for Neural Machine Translation Domain Adaptation

Wang, R., Utiyama, M., Liu, L., Chen, K., & Sumita, E. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1155 · Cited by 130

Exploring Trajectory Prediction Through Machine Learning Methods

Wang, C., Ma, L., Li, R., Durrani, T. S., & Zhang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2929430 · Cited by 130

Neural Machine Translation With GRU-Gated Attention Model

Zhang, B., Xiong, D., Xie, J., & Su, J. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2957276 · Cited by 133

A reinforcement learning approach for optimizing multiple traveling salesman problems over graphs

Hu, Y., Yao, Y., & Lee, W. S. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106244 · Cited by 130

An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

Guo, Q., Sun, Z., Zhang, J., & Theng, Y. L. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5337 · Cited by 130

A long video caption generation algorithm for big video data retrieval

Ding, S., Qu, S., Xi, Y., & Wan, S. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.054 · Cited by 128

A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes

Loyola, P., Marrese-Taylor, E., & Matsuo, Y. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p17-2045 · Cited by 128

Learning Private Neural Language Modeling with Attentive Aggregation

Ji, S., Pan, S., Long, G., Li, X., Jiang, J., & Huang, Z. H. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852464 · Cited by 172

IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis

Majumder, N., Poria, S., Gelbukh, A. F., Akhtar, M. S., Cambria, E., & Ekbal, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1377 · Cited by 127

SemStyle: Learning to Generate Stylised Image Captions Using Unaligned Text

Mathews, A., Xie, L., & He, X. (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.2018.00896 · Cited by 123

Stack-Pointer Networks for Dependency Parsing

Ma, X., Hu, Z., Liu, J., Peng, N., Neubig, G., & Hovy, E. H. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1130 · Cited by 151

Bilingual attention based neural machine translation

Kang, L., He, S., Wang, M., Long, F., & Su, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03563-8 · Cited by 49

Interactive Attention for Neural Machine TranslationPreprint

Meng, F., Lu, Z., Li, H., & Liu, Q. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.05011 · Cited by 68

Neural Machine Translation with Target-Attention Model

Yang, M., Zhang, M., Chen, K., Wang, R., & Zhao, T. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019edp7157 · Cited by 9

Neural Machine Translation with Deep Attention

Zhang, B., Xiong, D., & Su, J. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2876404 · Cited by 138

Neural Machine Translation with Bilingual History Involved Attention

Xue, H., Feng, Y., You, D., Zhang, W., & Li, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32236-6_23 · Cited by 1