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

IntelliBot: A Dialogue-based chatbot for the insurance industry

Nuruzzaman, M., & Hussain, O. K. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105810 · Cited by 95

MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images

Xu, Y., Lam, H., & Jia, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.034 · Cited by 95

Multi-Label Multimodal Emotion Recognition With Transformer-Based Fusion and Emotion-Level Representation Learning

Le, H.-D., Lee, G.-S., Kim, S.-H., Kim, S., & Yang, H.-J. (2023) · IEEE Access · doi:10.1109/access.2023.3244390 · Cited by 97

Attend and Predict: Understanding Gene Regulation by Selective Attention on ChromatinPreprint

Singh, R., Lanchantin, J., Sekhon, A., & Qi, Y. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/329334 · Cited by 93

A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine TranslationPreprint

Lakew, S. M., Cettolo, M., & Federico, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.06957 · Cited by 113

Untitled work

(2019) · doi:10.1609/aaai.v33i01.33016908 · Cited by 26

Personalized Bundle List Recommendation

Bai, J., Zhou, C., Song, J., Qu, X., An, W., Li, Z., & Gao, J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313568 · Cited by 92

A Sequence-to-Sequence Air Quality Predictor Based on the n-Step Recurrent Prediction

Liu, B., Yan, S., Li, J., Qu, G., Li, Y., Lang, J., & Gu, R. (2019) · IEEE Access · doi:10.1109/access.2019.2908081 · Cited by 94

Component Fusion: Learning Replaceable Language Model Component for End-to-end Speech Recognition System

Shan, C., Weng, C., Wang, G., Su, D., Luo, M., Yu, D., & Xie, L. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682490 · Cited by 85

Comparison of Diverse Decoding Methods from Conditional Language Models

Ippolito, D., Kriz, R., Sedoc, J., Kustikova, M., & Callison-Burch, C. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1365 · Cited by 118

Topic-Aware Pointer-Generator Networks for Summarizing Spoken Conversations

Liu, Z., Ng, A., Lee, S., Aw, A. T., & Chen, N. F. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003764 · Cited by 94

HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm

Islam, M., & Iqbal, T. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340987 · Cited by 92

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

Flow2Vec: value-flow-based precise code embedding

Sui, Y., Cheng, X. Y., Zhang, G., & Wang, H. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428301 · Cited by 91

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Chen, C., Hou, J., Shi, X., Yang, H. Q., Birchler, J. A., & Cheng, J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-020-03952-1 · Cited by 93

Artificial Intelligence for Retrosynthesis Prediction

Jiang, Y., Yu, Y., Kong, M. C., Mei, Y., Yuan, L., Huang, Z., Kuang, K., Wang, Z., Yao, H., Zou, J., et al. (2022) · Engineering · doi:10.1016/j.eng.2022.04.021 · Cited by 105

Retrieval-Augmented Generation for Code Summarization via Hybrid GNNPreprint

Liu, S., Chen, Y., Xie, X., Siow, J. K., & Liu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05405 · Cited by 142

Learning to Map Context-Dependent Sentences to Executable Formal Queries

Suhr, A., Iyer, S. V., & Artzi, Y. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1203 · Cited by 96

Inductive Anomaly Detection on Attributed Networks

Ding, K., Li, J., Agarwal, N., & Liu, H. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/179 · Cited by 92

Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax

Fei, H., Li, F., Li, B., & Ji, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17514 · Cited by 91

Attention based multi-agent intrusion detection systems using reinforcement learning

Sethi, K., Madhav, Y. V., Kumar, R., & Bera, P. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.102923 · Cited by 95

Choose your programming copilot

Sobania, D., Briesch, M., & Rothlauf, F. (2022) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3512290.3528700 · Cited by 124

AttrLeaks on the Edge: Exploiting Information Leakage from Privacy-Preserving Co-inference

Wang, Z., Liu, K., Hu, J., Ren, J., Guo, H., & Yuan, W. M. (2023) · Chinese Journal of Electronics · doi:10.23919/cje.2022.00.031 · Cited by 91

MAGRes-UNet: Improved Medical Image Segmentation Through a Deep Learning Paradigm of Multi-Attention Gated Residual U-Net

Hussain, T., & Shouno, H. (2024) · IEEE Access · doi:10.1109/access.2024.3374108 · Cited by 91

Multi-way, multilingual neural machine translation

Fırat, O., Cho, K., Sankaran, B., Vural, F. T. Y., & Bengio, Y. (2016) · Computer Speech & Language · doi:10.1016/j.csl.2016.10.006 · Cited by 93

Domain attention model for multi-domain sentiment classification

Yuan, Z., Wu, S., Wu, F., Liu, J., & Huang, Y. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.05.004 · Cited by 93

Positional Encoding to Control Output Sequence Length

Takase, S., & Okazaki, N. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1401 · Cited by 90

Sensitive-Sample Fingerprinting of Deep Neural Networks

He, Z., Zhang, T., & Lee, R. (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.2019.00486 · Cited by 90

Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading

Qin, L., Galley, M., Brockett, C., Liu, X., Gao, X., Dolan, B., Choi, Y., & Gao, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1539 · Cited by 100

Fixed Encoder Self-Attention Patterns in Transformer-Based Machine Translation

Raganato, A., Scherrer, Y., & Tiedemann, J. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.49 · Cited by 108

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

Sentiment analysis with deep neural networks: comparative study and performance assessment

Wadawadagi, R. S., & Pagi, V. B. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09845-2 · Cited by 95

Structure-invariant testing for machine translation

He, P., Meister, C., & Su, Z. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380339 · Cited by 94

VLSI placement parameter optimization using deep reinforcement learning

Agnesina, A., Chang, K., & Lim, S. K. (2020) · 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD) · doi:10.1145/3400302.3415690 · Cited by 93

CodeFill: multi-token code completion by jointly learning from structure and naming sequences

Izadi, M., Gismondi, R., & Gousios, G. (2022) · Proceedings of the 44th International Conference on Software Engineering · doi:10.1145/3510003.3510172 · Cited by 86

Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization

Li, H., Zhu, J., Zhang, J., & Zong, C. (2018) · International Conference on Computational Linguistics · Cited by 85

A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning

Choe, Y. J., Ham, J., Park, K., & Yoon, Y. (2019) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w19-4423 · Cited by 99

Generative Encoder-Decoder Models for Task-Oriented Spoken Dialog Systems with Chatting Capability

Zhao, T., Lu, A., Lee, K., & Eskénazi, M. (2017) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w17-5505 · Cited by 95

Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences

Winata, G. I., Madotto, A., Wu, C.-S., & Fung, P. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1026 · Cited by 92

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

Liu, B., Wei, H., Niu, D., Chen, H., & He, Y. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380270 · Cited by 92

Bifold and Semantic Reasoning for Pedestrian Behavior Prediction

Rasouli, A., Rohani, M., & Luo, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01531 · Cited by 89

A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting

Assaf, A. M., Haron, H., Hamed, H. N. A., Ghaleb, F. A. A., Qasem, S. N., & Albarrak, A. M. (2023) · Applied Sciences · doi:10.3390/app13148332 · Cited by 105

ProgPrompt: program generation for situated robot task planning using large language models

Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D.-F., Tremblay, J., Fox, D., Thomason, J., & Garg, A. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10135-3 · Cited by 141

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

Mutual Information and Diverse Decoding Improve Neural Machine TranslationPreprint

Li, J., & Jurafsky, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.00372 · Cited by 141

StaQC

Yao, Z., Weld, D. S., Chen, W., & Sun, H. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186081 · Cited by 92

Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation

Fadaee, M., & Monz, C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1040 · Cited by 88

Sign Language Production: A Review

Rastgoo, R., Kiani, K., Escalera, S., & Sabokrou, M. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00384 · Cited by 94

Toward Subgraph-Guided Knowledge Graph Question Generation With Graph Neural Networks

Chen, Y., Wu, L., & Zaki, M. J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3264519 · Cited by 106

Temporal Attention-Gated Model for Robust Sequence Classification

Pei, W., Baltrušaitis, T., Tax, D. M. J., & Morency, L. (2017) · 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.2017.94 · Cited by 90

Accurate Text-Enhanced Knowledge Graph Representation Learning

An, B., Chen, B., Han, X., & Sun, L. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1068 · Cited by 88

Robust Zero-Shot Cross-Domain Slot Filling with Example Values

Shah, D., Gupta, R., Fayazi, A. A., & Hakkani-Tür, D. Z. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1547 · Cited by 89

Attention-Based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving

Chen, Y., Dong, C., Palanisamy, P., Mudalige, P., Muelling, K., & Dolan, J. M. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00172 · Cited by 87

DeepRemaster

Iizuka, S., & Simo‐Serra, E. (2019) · ACM Transactions on Graphics · doi:10.1145/3355089.3356570 · Cited by 93

Neural Sign Language Translation by Learning Tokenization

Orbay, A., & Akarun, L. (2020) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg47880.2020.00002 · Cited by 94

Knowledge graph enhanced neural collaborative recommendation

Sang, L., Xu, M. G., Qian, S., & Wu, X. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113992 · Cited by 88

Local Multi-Head Channel Self-Attention for Facial Expression Recognition

Pecoraro, R., Basile, V., & Bono, V. (2022) · Information · doi:10.3390/info13090419 · Cited by 101

ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring

Sykiotis, S., Kaselimi, M., Doulamis, A. D., & Doulamis, N. D. (2022) · Sensors · doi:10.3390/s22082926 · Cited by 93

Short-term cooling and heating loads forecasting of building district energy system based on data-driven models

Yu, H., Zhong, F., Du, Y., Xie, X., Wang, Y., Zhang, X., & Huang, S. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113513 · Cited by 87

A Comparative Study on End-to-End Speech to Text Translation

Bahar, P., Bieschke, T., & Ney, H. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003774 · Cited by 86

Incorporating context-relevant concepts into convolutional neural networks for short text classification

Xu, J., Cai, Y., Wu, X., Lei, X., Huang, Q., Leung, H.-F., & Li, Q. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.080 · Cited by 84

FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation

Lin, J., Pan, W., & Ming, Z. (2020) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3383313.3412247 · Cited by 86

Attention-Based Relation Extraction With Bidirectional Gated Recurrent Unit and Highway Network in the Analysis of Geological Data

Luo, X., Zhou, W., Wang, W., Zhu, Y., & Deng, J. (2017) · IEEE Access · doi:10.1109/access.2017.2785229 · Cited by 88

Exploiting Deep Representations for Neural Machine Translation

Dou, Z.-Y., Tu, Z., Wang, X., Shi, S., & Zhang, T. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1457 · Cited by 91

Split and Rephrase

Narayan, S., Gardent, C., Cohen, S. B., & Shimorina, A. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1064 · Cited by 89

An Effective Transition-based Model for Discontinuous NER

Dai, X., Karimi, S., Hachey, B., & Paris, C. L. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.520 · Cited by 90

Short-Term Weather Forecasting Using Spatial Feature Attention Based LSTM Model

Suleman, M. A. R., & Shridevi, S. (2022) · IEEE Access · doi:10.1109/access.2022.3196381 · Cited by 96

Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia

Chen, H., Yang, J., Fu, X., Zheng, Q., Song, X., Fu, Z., Wang, J., Liang, Y., Yin, H., Liu, Z., et al. (2022) · Sustainability · doi:10.3390/su142013231 · Cited by 95

CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation

Yu, Q., Wang, H., Kim, D., Qiao, S., Collins, M. D., Zhu, Y., Adam, H., Yuille, A., & Chen, L.-C. (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/cvpr52688.2022.00259 · Cited by 87

Explicit Sparse Transformer: Concentrated Attention Through Explicit SelectionPreprint

Zhao, G., Lin, J., Zhang, Z., Ren, X., Su, Q., & Sun, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11637 · Cited by 168

LearningQ: A Large-Scale Dataset for Educational Question Generation

Chen, G., Yang, J., Hauff, C., & Houben, G.-J. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.14987 · Cited by 83

Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning from Web Data

Zhuang, B., Liu, L., Li, Y., Shen, C., & Reid, I. D. (2017) · 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.2017.311 · Cited by 84

Dialog-to-action: conversational question answering over a large-scale knowledge base

Guo, D., Tang, D., Duan, N., Zhou, M., & Yin, J. P. (2018) · neural information processing systems · Cited by 81

StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

Yin, P., Zhou, C., He, J., & Neubig, G. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1070 · Cited by 95

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

DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks

Yavuz, S., Rastogi, A., Chao, G.-L., & Hakkani-Tür, D. Z. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5917 · Cited by 87

PoPPL: Pedestrian Trajectory Prediction by LSTM With Automatic Route Class Clustering

Xue, H., Huynh, D. Q., & Reynolds, M. A. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2975837 · Cited by 86

Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform

Chen, Z., Wang, Y., Wu, J., Deng, C., & Hu, K. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02092-6 · Cited by 84

A Transformer Self-attention Model for Time Series Forecasting

Farsani, R. M., & Pazouki, E. (2021) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22061/jecei.2020.7426.391 · Cited by 89

Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse

Jin, X., Zheng, W.-Z., Kong, J., Wang, X., Zuo, M., Zhang, Q., & Lin, S. (2021) · Agriculture · doi:10.3390/agriculture11080802 · Cited by 84

A review of deep learning-based recommender system in e-learning environments

Liu, T., Wu, Q., Chang, L., & Gu, T. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10135-2 · Cited by 93

Generative Code Modeling with GraphsPreprint

Brockschmidt, M., Allamanis, M., Gaunt, A. L., & Polozov, O. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.08490 · Cited by 103

Abstractive social media text summarization using selective reinforced Seq2Seq attention model

Liang, Z., Du, J., & Li, C. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.137 · Cited by 84

Deep Learning Architectures for DNA Sequence Classification

Bosco, G. L., & Gangi, M. A. D. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-52962-2_14 · Cited by 84

Deeplearning Model Used in Text Classification

Cai, J., Li, J., Li, W., & Ji, W. (2018) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip.2018.8632592 · Cited by 83

Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions

Hannun, A., Lee, A., Xu, Q., & Collobert, R. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2460 · Cited by 103

An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss

Zhong, P., Wang, D., & Miao, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017492 · Cited by 88

Importance of Search and Evaluation Strategies in Neural Dialogue Modeling

Kulikov, I., Miller, A., Cho, K., & Weston, J. (2019) · Proceedings of the 12th International Conference on Natural Language Generation · doi:10.18653/v1/w19-8609 · Cited by 83

Emotional Speech Synthesis with Rich and Granularized Control

Um, S., Oh, S., Byun, K., Jang, I., Ahn, C., & Kang, H.-G. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053732 · Cited by 83

An Adaptive Anti-Noise Neural Network for Bearing Fault Diagnosis Under Noise and Varying Load Conditions

Jin, G., Zhu, T., Akram, M. W., Jin, Y., & Zhu, C. (2020) · IEEE Access · doi:10.1109/access.2020.2989371 · Cited by 84

TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition

Lin, B. Y., Lee, D.-H., Shen, M., Moreno, R., Huang, X., Shiralkar, P., & Ren, X. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.752 · Cited by 94

Lip-reading with Densely Connected Temporal Convolutional Networks

Ma, P., Wang, Y., Shen, J., Petridis, S., & Pantić, M. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00290 · Cited by 83

Representation learning applications in biological sequence analysis

Iuchi, H., Matsutani, T., Yamada, K., Iwano, N., Sumi, S., Hosoda, S., Zhao, S., Fukunaga, T., & Hamada, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.039 · Cited by 88

Fair and Explainable Depression Detection in Social Media

Adarsh, V., Kumar, P. A., Lavanya, V., & Gangadharan, G. R. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103168 · Cited by 83

Dynamic Convolution With Multilevel Attention for EEG-Based Motor Imagery Decoding

Altaheri, H., Muhammad, G., & Alsulaiman, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3281911 · Cited by 83

L-SeqSleepNet: Whole-cycle Long Sequence Modeling for Automatic Sleep Staging

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