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

Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks

Voelker, A. R., Kajić, I., & Eliasmith, C. (2019) · neural information processing systems · Cited by 108

Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining

Huang, W.-C., Hayashi, T., Wu, Y.-C., Kameoka, H., & Toda, T. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1066 · Cited by 108

Monotonic Multihead AttentionPreprint

Ma, X., Pino, J., Cross, J., Puzon, L., & Gu, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.12406 · Cited by 108

Compositional Sequence Labeling Models for Error Detection in Learner Writing

Rei, M., & Yannakoudakis, H. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1112 · Cited by 111

Interactive neural machine translation

Peris, Á., Domingo, M., & Casacuberta, F. (2016) · Computer Speech & Language · doi:10.1016/j.csl.2016.12.003 · Cited by 108

Enhancing Attention-Based LSTM With Position Context for Aspect-Level Sentiment Classification

Zeng, J., Ma, X., & Zhou, K. (2019) · IEEE Access · doi:10.1109/access.2019.2893806 · Cited by 109

Fault detection and identification of rolling element bearings with Attentive Dense CNN

Plakias, S., & Boutalis, Y. S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.143 · Cited by 109

Neural Models for Information RetrievalPreprint

Mitra, B., & Craswell, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.01509 · Cited by 194

Reinforcement Learning Based Graph-to-Sequence Model for Natural Question GenerationPreprint

Chen, Y., Wu, L., & Zaki, M. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.04942 · Cited by 135

Imputer: Sequence Modelling via Imputation and Dynamic ProgrammingPreprint

Chan, W., Saharia, C., Hinton, G. E., Norouzi, M., & Jaitly, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.08926 · Cited by 102

Learning Private Neural Language Modeling with Attentive Aggregation

Ji, S., Pan, S., Long, G., Li, X., Jiang, J. B., & Huang, Z. H. (2018) · UTS ePRESS (University of Technology Sydney) · Cited by 106

Deceiving End-to-End Deep Learning Malware Detectors using Adversarial ExamplesPreprint

Kreuk, F., Barak, A., Aviv-Reuven, S., Baruch, M., Benny Pinkas, B., & Keshet, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04528 · Cited by 168

Understanding Back-Translation at ScalePreprint

Edunov, S., Ott, M., Auli, M., & Grangier, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09381 · Cited by 307

Joint Training for Neural Machine Translation Models with Monolingual Data

Zhang, Z., Liu, S., Li, M., Zhou, M. Q., & Chen, E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11248 · Cited by 105

Humor Detection: A Transformer Gets the Last Laugh

Weller, O., & Seppi, K. D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1372 · Cited by 114

Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations

Zhang, J.-X., Ling, Z.-H., & Dai, L.-R. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2960721 · Cited by 105

Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness

Wu, S., Li, Y., Zhang, D., Zhou, Y., & Wu, Z. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.515 · Cited by 105

WeightNet: Revisiting the Design Space of Weight Networks

Ma, N., Zhang, X., Huang, J., & Sun, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_46 · Cited by 108

Spatiotemporal Attention-Based Graph Convolution Network for Segment-Level Traffic Prediction

Li, D., & Lasenby, J. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3078187 · Cited by 107

Deep learning models for forecasting dengue fever based on climate data in Vietnam

Nguyen, V.-H., Hanh, T. T. T., Mulhall, J., Minh, H. V., Duong, T. Q., Chien, N. V., Nhung, N. T. T., Lan, V. H., Minh, H. B., Cuong, D. M., et al. (2022) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0010509 · Cited by 123

Recent Advances in Large Language Models for Healthcare

Nassiri, K., & Akhloufi, M. A. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4020062 · Cited by 108

Bi-Directional Spatial-Semantic Attention Networks for Image-Text Matching

Huang, F., Zhang, X., Zhao, Z., & Li, Z. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2882225 · Cited by 104

Learning to Generate Questions by LearningWhat not to Generate

Liu, B., Zhao, M. M., Niu, D., Lai, K., He, Y., Wei, H., & Xu, Y. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313737 · Cited by 105

Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition

Xu, K., Yang, Z., Kang, P., Wang, Q., & Liu, W. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.04.002 · Cited by 101

Automatic Assessment of Depression From Speech via a Hierarchical Attention Transfer Network and Attention Autoencoders

Zhao, Z., Bao, Z., Zhang, Z., Deng, J., Cummins, N., Wang, H., Tao, J., & Schuller, B. W. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2955012 · Cited by 110

Curriculum Pre-training for End-to-End Speech Translation

Wang, C., Wu, Y., Liu, S., Zhou, M., & Yang, Z. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.344 · Cited by 103

Integrated Multiple Directed Attention-Based Deep Learning for Improved Air Pollution Forecasting

Dairi, A., Harrou, F., Khadraoui, S., & Sun, Y. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3091511 · Cited by 104

Real-world ride-hailing vehicle repositioning using deep reinforcement learning

Jiao, Y., Tang, X., Qin, Z. T., Li, S., Zhang, F., Hongtu Zhu, B., & Ye, J. (2021) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2021.103289 · Cited by 104

Nonautoregressive Encoder–Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction

Fei, H., Ren, Y., Zhang, Y., & Ji, D. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3129483 · Cited by 104

Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

Rama-Maneiro, E., Vidal, J. C., & Lama, M. (2021) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3139807 · Cited by 129

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

Effective Domain Mixing for Neural Machine Translation

Britz, D., Le, Q. V., & Pryzant, R. (2017) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w17-4712 · Cited by 104

Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation

He, T., Tan, X., Xia, Y., He, D., Qin, T., Chen, Z., & Liu, T. (2018) · neural information processing systems · Cited by 103

High-Resolution Remote Sensing Image Scene Classification via Key Filter Bank Based on Convolutional Neural Network

Li, F., Feng, R., Han, W., & Wang, L. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2987060 · Cited by 104

Deep learning in electron microscopy

Ede, J. M. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd614 · Cited by 118

Identifying Where to Focus in Reading Comprehension for Neural Question Generation

Du, X., & Cardie, C. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1219 · Cited by 100

Adding Attentiveness to the Neurons in Recurrent Neural Networks

Zhang, P., Xue, J., Lan, C., Zeng, W., Gao, Z., & Zheng, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_9 · Cited by 105

Vision-Based Navigation With Language-Based Assistance via Imitation Learning With Indirect Intervention

Nguyen, K., Dey, D., Brockett, C., & Dolan, B. (2019) · 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.2019.01281 · Cited by 112

Deep attention based music genre classification

Yu, Y., Luo, S., Liu, S., Qiao, H., Liu, Y., & Feng, L. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.054 · Cited by 102

Spatio-Temporal Fusion Based Convolutional Sequence Learning for Lip Reading

Zhang, X., Cheng, F., & Shilin, W. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00080 · Cited by 101

Two-Stage Copy-Move Forgery Detection With Self Deep Matching and Proposal SuperGlue

Liu, Y., Xia, C. Y., Zhu, X., & Xu, S. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3132828 · Cited by 110

Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining

Zhou, B., Chen, Z., Clapés, A., Wan, J., Liang, Y., Escalera, S., Lei, Z., & Zhang, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01908 · Cited by 125

Deep networks for system identification: A survey

Pillonetto, G., Aravkin, A. Y., Gedon, D., Lennart Ljung, A., Ribeiro, A. H., & Schön, T. B. (2024) · Automatica · doi:10.1016/j.automatica.2024.111907 · Cited by 153

Attention Routing Between Capsules

Choi, J., Seo, H., Im, S., & Kang, M. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00247 · Cited by 101

Accurate Retinal Vessel Segmentation in Color Fundus Images via Fully Attention-Based Networks

Li, K., Qi, X., Luo, Y., Yao, Z., Zhou, X., & Sun, M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3028180 · Cited by 102

Context-dependent word representation for neural machine translation

Choi, H., Cho, K., & Bengio, Y. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.01.007 · Cited by 101

A neural framework for retrieval and summarization of source code

Chen, Q., & Zhou, M. (2018) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3238147.3240471 · Cited by 100

Confusionset-guided Pointer Networks for Chinese Spelling Check

Wang, D., Tay, Y., & Li, Z. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1578 · Cited by 99

Multimodal Summarization with Guidance of Multimodal Reference

Zhu, J., Zhou, Y., Zhang, J., Li, H., Zong, C., & Li, C. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6525 · Cited by 101

Impact of Stemming and Word Embedding on Deep Learning-Based Arabic Text Categorization

Almuzaini, H. A., & Azmi, A. M. (2020) · IEEE Access · doi:10.1109/access.2020.3009217 · Cited by 111

Looking Beyond Single Images for Weakly Supervised Semantic Segmentation Learning

Wang, W., Sun, G., & Gool, L. V. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3168530 · Cited by 100

Enhancing self-management in type 1 diabetes with wearables and deep learning

Zhu, T., Uduku, C. N., Li, K., Herrero, P., Oliver, N. S., & Georgiou, P. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00626-5 · Cited by 114

Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN

Chen, J., & Zhuge, H. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1438 · Cited by 101

Syntax-Directed Attention for Neural Machine Translation

Chen, K., Wang, R., Utiyama, M., Sumita, E., & Zhao, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11910 · Cited by 99

An Analysis of Attention Mechanisms: The Case of Word Sense Disambiguation in Neural Machine Translation

Tang, G., Sennrich, R., & Nivre, J. (2018) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w18-6304 · Cited by 99

A Survey of Deep Learning Techniques for Neural Machine TranslationPreprint

Yang, S., Wang, Y., & Chu, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07526 · Cited by 176

Seq2seq Dependency Parsing.

Li, Z., Cai, J., He, S., & Zhao, H. (2018) · International Conference on Computational Linguistics · Cited by 99

Noise Masking Recurrent Neural Network for Respiratory Sound Classification

Kochetov, K., Lane, E., Balashov, M., Filchenkov, A. A., & Shalyto, A. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01424-7_21 · Cited by 98

Coronary Arteries Segmentation Based on 3D FCN With Attention Gate and Level Set Function

Shen, Y., Fang, Z., Gao, Y., Xiong, N. N., Zhong, C., & Tang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2908039 · Cited by 98

Grow and Prune Compact, Fast, and Accurate LSTMs

Dai, X., Yin, H., & Jha, N. K. (2019) · IEEE Transactions on Computers · doi:10.1109/tc.2019.2954495 · Cited by 103

DIRE: A Neural Approach to Decompiled Identifier Naming

Lacomis, J., Yin, P., Schwartz, E. J., Allamanis, M., Goues, C. L., Neubig, G., & Vasilescu, B. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00064 · Cited by 108

SibNet: Sibling Convolutional Encoder for Video Captioning

Liu, S., Ren, Z., & Yuan, J. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2940007 · Cited by 101

The importance of short lag-time in the runoff forecasting model based on long short-term memory

Chen, X., Huang, J., Han, Z., Gao, H., Liu, M., Li, Z., Liu, X., Li, Q., Qi, H., & Huang, Y. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125359 · Cited by 98

A Self-Supervised Gait Encoding Approach With Locality-Awareness for 3D Skeleton Based Person Re-Identification

Rao, H., Wang, S., Hu, X., Tan, M., Guo, Y., Cheng, J. S., Liu, X., & Hu, B. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3092833 · Cited by 104

Deep Learning in Sentiment Analysis: Recent Architectures

Abdullah, T., & Ahmet, A. (2022) · ACM Computing Surveys · doi:10.1145/3548772 · Cited by 116

Molecular Structure Extraction from Documents Using Deep Learning

Staker, J., Marshall, K., Abel, R., & McQuaw, C. M. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00669 · Cited by 106

Deep Ordinal Hashing With Spatial Attention

Jin, L., Shu, X., Li, K., Li, Z., Qi, G.-J., & Tang, J. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2883522 · Cited by 94

Back-Translation-Style Data Augmentation for end-to-end ASR

Hayashi, T., Watanabe, S., Zhang, Y., Toda, T., Hori, T., Astudillo, R. F., & Takeda, K. (2018) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639619 · Cited by 97

Neural Models for Key Phrase Extraction and Question Generation

Subramanian, S., Wang, T., Yuan, X., Zhang, S., Trischler, A., & Bengio, Y. (2018) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/w18-2609 · Cited by 100

HMEAE: Hierarchical Modular Event Argument Extraction

Wang, X., Wang, Z., Han, X., Liu, Z., Li, J., Li, P., Sun, M., Zhou, J., & Ren, X. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1584 · Cited by 99

Deep Attentive Video Summarization With Distribution Consistency Learning

Ji, Z., Zhao, Y., Pang, Y., Li, X., & Han, J. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2991083 · Cited by 100

Translating math formula images to LaTeX sequences using deep neural networks with sequence-level training

Wang, Z., & Liu, J. (2020) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-020-00360-2 · Cited by 113

Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis

Wu, Z., & Ong, D. C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17659 · Cited by 101

Is Attention Better Than Matrix Decomposition?Preprint

Geng, Z., Guo, M.-H., Chen, H., Li, X., Wei, K., & Lin, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04553 · Cited by 140

Filtering and Mining Parallel Data in a Joint Multilingual Space

Schwenk, H. (2018) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p18-2037 · Cited by 118

CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning

Yao, Z., Peddamail, J. R., & Sun, H. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313632 · Cited by 96

On the Robustness of Self-Attentive Models

Hsieh, Y.-L., Cheng, M., Juan, D.-C., Wei, W., Hsu, W.-L., & Hsieh, C.-J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1147 · Cited by 96

Text-to-SQL Generation for Question Answering on Electronic Medical Records

Wang, P., Shi, T., & Reddy, C. K. A. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380120 · Cited by 114

Modeling Spatial-Temporal Dynamics for Traffic Prediction.Preprint

Yao, H., Tang, X., Wei, H., Zheng, G., Yu, Y., & Li, Z. (2018) · arXiv (Cornell University) · Cited by 97

Characterizing the natural language descriptions in software logging statements

He, P., Chen, Z., He, S., & Lyu, M. R.-T. (2018) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3238147.3238193 · Cited by 103

Deriving Machine Attention from Human Rationales

Bao, Y., Chang, S., Yu, M., & Barzilay, R. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1216 · Cited by 117

End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories

Mao, R., Lin, C., & Guérin, F. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1378 · Cited by 100

Semi-Supervised Learning for Neural Keyphrase Generation

Ye, H., & Wang, L. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1447 · Cited by 94

Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems

Chiang, T.-R., & Chen, Y.-N. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1272 · Cited by 117

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

Automatic Source Code Summarization with Extended Tree-LSTM

Shido, Y., Kobayashi, Y., Yamamoto, A., Miyamoto, A., & Matsumura, T. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851751 · Cited by 102

End-To-End Multi-Speaker Speech Recognition With Transformer

Chang, X., Zhang, W., Qian, Y., Roux, J. L., & Watanabe, S. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054029 · Cited by 98

Adaptive deformable convolutional network

Chen, F., Wu, F., Xu, J. W., Gao, G., Ge, Q., & Jing, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.128 · Cited by 95

A novel framework for detecting social bots with deep neural networks and active learning

Wu, Y., Fang, Y., Shang, S., Jin, J., Wei, L., & Wang, H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106525 · Cited by 95

Photovoltaic power forecasting: A Transformer based framework

Piantadosi, G., Dutto, S., Galli, A., Vito, S. D., Sansone, C., & Francia, G. D. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100444 · Cited by 120

Compositional generalization through meta sequence-to-sequence learningPreprint

Lake, B. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05381 · Cited by 117

TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

Yin, P., & Neubig, G. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/d18-2002 · Cited by 203

Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism

Wang, P.-Y., Chen, C.-T., Su, J.-W., Wang, T.-Y., & Huang, S.-H. (2021) · IEEE Access · doi:10.1109/access.2021.3071306 · Cited by 105

Semantic Text Matching for Long-Form Documents

Jiang, J., Zhang, M., Li, C., Bendersky, M., Golbandi, N., & Najork, M. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313707 · Cited by 97

Dialogue Act Recognition via CRF-Attentive Structured Network

Chen, Z., Yang, R., Zhao, Z., Cai, D., & He, X. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3209997 · Cited by 93

DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation

Kearney, V. P., Chan, J. W. S., Wang, T., Perry, A., Descovich, M., Morin, O., Yom, S. S., & Solberg, T. D. (2020) · Scientific Reports · doi:10.1038/s41598-020-68062-7 · Cited by 97

A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond

Xiao, Y., Wu, L., Guo, J., Li, J., Zhang, M., Qin, T., & Liu, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3277122 · Cited by 117

Electricity demand forecasting with hybrid classical statistical and machine learning algorithms: Case study of Ukraine

Grandón, T. G., Schwenzer, J., Steens, T., & Breuing, J. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122249 · Cited by 123

Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards

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