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

Multi-Head Attention with Disagreement Regularization

Li, J., Tu, Z., Yang, B., Lyu, M. R.-T., & Zhang, T. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1317 · Cited by 214

Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation

Dai, N., Liang, J., Qiu, X., & Huang, X. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1601 · Cited by 224

Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research

Das, S., Tariq, A., Santos, T., Kantareddy, S. S., & Banerjee, I. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_4 · Cited by 234

A Dataset for Document Grounded Conversations

Zhou, K., Prabhumoye, S., & Black, A. W. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1076 · Cited by 220

Exploiting Cross-Sentence Context for Neural Machine Translation

Wang, L., Tu, Z., Way, A., & Liu, Q. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1301 · Cited by 193

A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism

Yang, Y., Xiong, Q., Wu, C., Zou, Q., Yu, Y., Yi, H., & Gao, M. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-14687-8 · Cited by 196

Attention Correctness in Neural Image Captioning

Liu, C., Mao, J., Sha, F., & Yuille, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11197 · Cited by 210

The great Transformer: Examining the role of large language models in the political economy of AI

Luitse, D. M. R., & Denkena, W. (2021) · Big Data & Society · doi:10.1177/20539517211047734 · Cited by 242

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3200740 · Cited by 223

Learning to Generate Product Reviews from Attributes

Dong, L., Huang, S., Wei, F., Lapata, M., Zhou, M., & Xu, K. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers · doi:10.18653/v1/e17-1059 · Cited by 190

RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition

Yue, X., Kuang, Z., Lin, C., Sun, H., & Zhang, W. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_9 · Cited by 191

fairseq: A Fast, Extensible Toolkit for Sequence ModelingPreprint

Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., Grangier, D., & Auli, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.01038 · Cited by 471

Text Generation from Knowledge Graphs with Graph Transformers

Koncel-Kedziorski, R., Bekal, D., Luan, Y., Lapata, M., & Hajishirzi, H. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1238 · Cited by 190

A hybrid attention-based deep learning approach for wind power prediction

Ma, Z., & Mei, G. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119608 · Cited by 192

LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability

Miao, Z., Zhao, M., Zhang, X., & Ming, D. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120209 · Cited by 204

Retrieval-based neural source code summarization

Zhang, J., Wang, X., Zhang, H., Sun, H., & Liu, X. (2020) · Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering · doi:10.1145/3377811.3380383 · Cited by 275

Neural Machine Translation with Reconstruction

Tu, Z., Liu, Y., Shang, L., Liu, X., & Li, H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10950 · Cited by 185

Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation

Wang, X. E., Xiong, W., Wang, H., & Wang, W. Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_3 · Cited by 187

Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

Lv, S., Guo, D., Xu, J., Tang, D., Duan, N., Gong, M., Shou, L., Jiang, D., Cao, G., & Hu, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6364 · Cited by 187

Who2com: Collaborative Perception via Learnable Handshake Communication

Liu, Y., Tian, J., Ma, C., Glaser, N., Kuo, C.-W., & Kira, Z. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197364 · Cited by 207

On Extractive and Abstractive Neural Document Summarization with Transformer Language Models

Pilault, J., Li, R., Subramanian, S., & Pal, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.748 · Cited by 228

From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network

Wang, Y., Xie, H., Fang, S., Wang, J., Zhu, S., & Zhang, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01393 · Cited by 190

Transformers for modeling physical systems

Geneva, N., & Zabaras, N. J. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.11.022 · Cited by 228

Towards End-to-End Text Spotting with Convolutional Recurrent Neural Networks

Li, H., Wang, P., & Shen, C. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.560 · Cited by 230

Signal Modulation Classification Based on the Transformer Network

Cai, J., Gan, F., Cao, X., & Liu, W. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3176640 · Cited by 196

Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

Calixto, I., Liu, Q., & Campbell, N. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1175 · Cited by 185

Learning Neural Templates for Text Generation

Wiseman, S., Shieber, S. M., & Rush, A. M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1356 · Cited by 204

MSMO: Multimodal Summarization with Multimodal Output

Zhu, J., Li, H., Liu, T., Zhou, Y. P., Zhang, J., & Zong, C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1448 · Cited by 188

Interpreting network knowledge with attention mechanism for bearing fault diagnosis

Yang, Z.-B., Zhang, J., Zhao, Z., Zhai, Z., & Chen, X. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106829 · Cited by 187

Compression of Neural Machine Translation Models via Pruning

See, A., Luong, M.-T., & Manning, C. D. (2016) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1029 · Cited by 220

Current trends in the development of intelligent unmanned autonomous systems

Zhang, T., Li, Q., Zhang, C.-S., Liang, H.-W., Li, P., Wang, T.-M., Li, S., Zhu, Y.-L., & Wu, C. (2017) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1601650 · Cited by 184

A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection

Fanai, H., & Abbasimehr, H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119562 · Cited by 204

Target-Sensitive Memory Networks for Aspect Sentiment Classification

Wang, S., Mazumder, S., Liu, B., Zhou, M., & Chang, Y. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1088 · Cited by 183

A survey on deep learning approaches for text-to-SQL

Katsogiannis-Meimarakis, G., & Koutrika, G. (2023) · The VLDB Journal · doi:10.1007/s00778-022-00776-8 · Cited by 235

STGSN — A Spatial–Temporal Graph Neural Network framework for time-evolving social networks

Min, S., Gao, Z., Peng, J., Wang, L., Qin, K., & Fang, B. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106746 · Cited by 188

Spatial-Temporal Multi-Cue Network for Sign Language Recognition and Translation

Zhou, H., Zhou, W., Zhou, Y., & Li, H. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3059098 · Cited by 182

A Question Answering Approach for Emotion Cause Extraction

Gui, L., Hu, J., He, Y., Xu, R., Lu, Q., & Du, J. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1167 · Cited by 181

ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning

Sun, W., Jiang, W., Trulls, E., Tagliasacchi, A., & Yi, K. M. (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/cvpr42600.2020.01130 · Cited by 181

Sentence Similarity Learning by Lexical Decomposition and CompositionPreprint

Wang, Z., Mi, H., & Ittycheriah, A. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.07019 · Cited by 189

Aspect-based sentiment analysis with alternating coattention networks

Yang, C., Zhang, H., Jiang, B., & Li, K. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2018.12.004 · Cited by 185

A comparative survey of recent natural language interfaces for databases

Affolter, K., Stockinger, K., & Bernstein, A. (2019) · The VLDB Journal · doi:10.1007/s00778-019-00567-8 · Cited by 200

NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images

Lal, S., Das, D., Alabhya, K., Kanfade, A., Kumar, A., & Kini, J. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104075 · Cited by 188

Deep Learning for Time Series Forecasting: Advances and Open Problems

Casolaro, A., Capone, V., Iannuzzo, G., & Camastra, F. (2023) · Information · doi:10.3390/info14110598 · Cited by 226

Densely Connected CNN with Multi-scale Feature Attention for Text Classification

Wang, S., Huang, M., & Deng, Z. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/621 · Cited by 181

Phishing Email Detection Using Improved RCNN Model With Multilevel Vectors and Attention Mechanism

Fang, Y., Zhang, C., Huang, C., Liu, L., & Yang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2913705 · Cited by 195

Cross-Modal and Hierarchical Modeling of Video and Text

Zhang, B., Hu, H., & Sha, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_23 · Cited by 178

TextConvoNet: a convolutional neural network based architecture for text classification

Soni, S., Chouhan, S. S., & Rathore, S. S. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04221-9 · Cited by 197

Very Deep Self-Attention Networks for End-to-End Speech Recognition

Pham, N.-Q., Nguyen, T., Niehues, J., Müller, M., & Waibel, A. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2702 · Cited by 196

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Akbar, R., Robert, P. A., Pavlović, M., Jeliazkov, J. R., Snapkov, I., Slabodkin, A., Weber, C. R., Scheffer, L., Miho, E., Haff, I. H., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108856 · Cited by 191

Graph Attention Auto-Encoders

Salehi, A., & Davulcu, H. (2020) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai50040.2020.00154 · Cited by 205

Rolling Bearing Fault Diagnosis Method Base on Periodic Sparse Attention and LSTM

An, Y., Zhang, K., Liu, Q., Chai, Y., & Huang, X. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3173446 · Cited by 174

Improving Neural Machine Translation Models with Monolingual Data

Sennrich, R., Haddow, B., & Birch, A. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1009 · Cited by 296

Recurrent Attention Models for Depth-Based Person Identification

Haque, A., Alahi, A., & Fei-Fei, L. (2016) · 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.2016.138 · Cited by 175

Saint+: Integrating temporal features for ednet correctness prediction

Shin, D., Shim, Y., Yu, H., Lee, S., Kim, B., & Choi, Y. (2021) · LAK21: 11th International Learning Analytics and Knowledge Conference · doi:10.1145/3448139.3448188 · Cited by 141

Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization

Jin, X., Zheng, W.-Z., Kong, J., Wang, X., Bai, Y., Su, T., & Lin, S. (2021) · Energies · doi:10.3390/en14061596 · Cited by 177

A dual attention LSTM lightweight model based on exponential smoothing for remaining useful life prediction

Shi, J., Zhong, J., Zhang, Y., Xiao, B., Xiao, L., & Zheng, Y. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109821 · Cited by 185

On Adversarial Examples for Character-Level Neural Machine TranslationPreprint

Ebrahimi, J., Lowd, D., & Dou, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09030 · Cited by 172

Story Ending Generation with Incremental Encoding and Commonsense Knowledge

Guan, J., Wang, Y., & Huang, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016473 · Cited by 162

Exploring Sequence-to-Sequence Learning in Aspect Term Extraction

Ma, D., Li, S., Wu, F., Xie, X., & Wang, H. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1344 · Cited by 173

Towards Robust Neural Machine Translation

Cheng, Y., Tu, Z., Meng, F.-D., Zhai, J.-J., & Liu, Y. (2018) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p18-1163 · Cited by 167

Recurrent Neural Networks (RNNs): A gentle Introduction and OverviewPreprint

Schmidt, R. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.05911 · Cited by 470

Better Sign Language Translation with STMC-Transformer

Yin, K., & Read, J. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.525 · Cited by 190

An Ensemble of Deep Learning-Based Multi-Model for ECG Heartbeats Arrhythmia Classification

Essa, E., & Xie, X. (2021) · IEEE Access · doi:10.1109/access.2021.3098986 · Cited by 181

Multi-modal Attention Network Learning for Semantic Source Code Retrieval

Wan, Y., Shu, J., Sui, Y., Xu, G., Zhao, Z., Wu, J., & Yu, P. S. (2019) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2019.00012 · Cited by 175

Monotonic Chunkwise AttentionPreprint

Chiu, C., & Raffel, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.05382 · Cited by 169

Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning

Song, J., Gao, L., Guo, Z. Z., Liu, W., Zhang, D., & Shen, H. T. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/381 · Cited by 167

Deep Reinforcement Learning with a Natural Language Action Space

He, J., Chen, J., He, X., Gao, J., Li, L. C., Deng, L., & Mari Ostendorf, E. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1153 · Cited by 245

Transfer Learning across Low-Resource, Related Languages for Neural Machine TranslationPreprint

Nguyen, T. Q., & Chiang, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.09803 · Cited by 168

Transformer models used for text-based question answering systems

Nassiri, K., & Akhloufi, M. A. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04052-8 · Cited by 194

Challenges in Data-to-Document Generation

Wiseman, S., Shieber, S., & Rush, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1239 · Cited by 524

Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

Zhang, J., Kowsari, K., Harrison, J. H., Lobo, J. M., & Barnes, L. E. (2018) · IEEE Access · doi:10.1109/access.2018.2875677 · Cited by 173

Predicting prime editing efficiency and product purity by deep learning

Mathis, N., Allam, A., Kissling, L., Marquart, K. F., Schmidheini, L., Solari, C., Balázs, Z., Krauthammer, M. O., & Schwank, G. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01613-7 · Cited by 167

SSIM—A Deep Learning Approach for Recovering Missing Time Series Sensor Data

Zhang, Y., Thorburn, P. J., Xiang, W., & Fitch, P. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2909038 · Cited by 168

A Recurrent BERT-based Model for Question Generation

Chan, Y.-H., & Fan, Y.-C. (2019) · Proceedings of the 2nd Workshop on Machine Reading for Question Answering · doi:10.18653/v1/d19-5821 · Cited by 165

A Review of Deep Learning Based Speech Synthesis

Ning, Y., He, S., Wu, Z., Xing, C., & Zhang, L. (2019) · Applied Sciences · doi:10.3390/app9194050 · Cited by 164

A CNN-Transformer Hybrid Approach for Crop Classification Using Multitemporal Multisensor Images

Li, Z., Chen, G., & Zhang, T. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2971763 · Cited by 164

Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction

Liu, L., Zhen, J., Li, G., Zhan, G., He, Z., Du, B., & Lin, L. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3002718 · Cited by 164

DSD: Dense-Sparse-Dense Training for Deep Neural NetworksPreprint

Han, S., Pool, J., Narang, S., Mao, H., Gong, E., Tang, S., Elsen, E., Vajda, P., Paluri, M., Tran, J. J., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.04381 · Cited by 164

Incorporating Structural Alignment Biases into an Attentional Neural Translation Model

Cohn, T., Hoang, C. D. V., Vylomova, E., Yao, K., Dyer, C., & Haffari, G. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1102 · Cited by 166

Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach

Ren, S., Choi, T., Lee, K.-M., & Lin, L. (2020) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2019.101834 · Cited by 163

Benchmark for Evaluating Pedestrian Action Prediction

Kotseruba, I., Rasouli, A., & Tsotsos, J. K. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00130 · Cited by 174

A Time-Restricted Self-Attention Layer for ASR

Povey, D., Hadian, H., Ghahremani, P., Li, K., & Khudanpur, S. P. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462497 · Cited by 162

Attention Based Fully Convolutional Network for Speech Emotion Recognition

Zhang, Y., Du, J., Wang, Z., Zhang, J., & Tu, Y. (2018) · 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipa.2018.8659587 · Cited by 161

A survey on aspect-based sentiment classification

Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165

Incorporating Global Visual Features into Attention-based Neural Machine Translation.

Calixto, I., & Liu, Q. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1105 · Cited by 172

Learning to Remember Translation History with a Continuous Cache

Tu, Z., Liu, Y., Shi, S., & Zhang, T. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00029 · Cited by 161

Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech

Chung, Y.-A., & Glass, J. R. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-2341 · Cited by 182

Exploring chemical space using natural language processing methodologies for drug discovery

Öztürk, H., Özgür, A., Schwaller, P., Laino, T., & Özkırımlı, E. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.01.020 · Cited by 167

BiLSTM with Multi-Polarity Orthogonal Attention for Implicit Sentiment Analysis

Wei, J., Liao, J., Yang, Z., Wang, S., & Zhao, Q. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.11.054 · Cited by 156

Deep neural network battery life and voltage prediction by using data of one cycle only

Hsu, C.-W., Xiong, R., Chen, N.-Y., Li, J., & Tsou, N. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118134 · Cited by 160

Current and Emerging Trends in Medical Image Segmentation With Deep Learning

Conze, P.-H., Andrade-Miranda, G. X., Singh, V. K., Jaouen, V., & Visvikis, D. (2023) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2023.3265863 · Cited by 179

Memristive LSTM Network for Sentiment Analysis

Wen, S., Wei, H., Yang, Y., Guo, Z., Zeng, Z., Huang, T., & Chen, Y. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2019.2906098 · Cited by 159

Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network

Xie, P., Gao, M., Zhang, H., Niu, Y., & Wang, X. (2019) · Energy · doi:10.1016/j.energy.2019.116482 · Cited by 158

Neural Machine Reading Comprehension: Methods and Trends

Liu, S., Zhang, X., Zhang, S., Wang, H., & Zhang, W. (2019) · Applied Sciences · doi:10.3390/app9183698 · Cited by 160

The NLP Cookbook: Modern Recipes for Transformer Based Deep Learning Architectures

Singh, S. K., & Mahmood, A. (2021) · IEEE Access · doi:10.1109/access.2021.3077350 · Cited by 178

Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review

Meli, R., Morris, G. M., & Biggin, P. C. (2022) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2022.885983 · Cited by 176

Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention

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