| Docbert: Bert for document classification A Adhikari, A Ram, R Tang, J Lin arXiv preprint arXiv:1904.08398, 2019 | 277 | 2019 |
| Distilling Task-Specific Knowledge from BERT into Simple Neural Networks R Tang*, Y Lu*, L Liu*, L Mou, O Vechtomova, J Lin arXiv preprint arXiv:1903.12136, 2019 | 254 | 2019 |
| Deep Residual Learning for Small-Footprint Keyword Spotting R Tang, J Lin 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 175 | 2018 |
| DeeBERT: Dynamic early exiting for accelerating BERT inference J Xin, R Tang, J Lee, Y Yu, J Lin arXiv preprint arXiv:2004.12993, 2020 | 121 | 2020 |
| Rethinking Complex Neural Network Architectures for Document Classification A Adhikari*, A Ram*, R Tang, J Lin Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 82 | 2019 |
| Rapidly Bootstrapping a Question Answering Dataset for COVID-19 R Tang, R Nogueira, E Zhang, N Gupta, P Cam, K Cho, J Lin arXiv preprint arXiv:2004.11339, 2020 | 55 | 2020 |
| Covidex: Neural ranking models and keyword search infrastructure for the covid-19 open research dataset E Zhang, N Gupta, R Tang, X Han, R Pradeep, K Lu, Y Zhang, R Nogueira, ... arXiv preprint arXiv:2007.07846, 2020 | 52 | 2020 |
| An Experimental Analysis of the Power Consumption of Convolutional Neural Networks for Keyword Spotting R Tang, W Wang, Z Tu, J Lin 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 39 | 2018 |
| What would elsa do? freezing layers during transformer fine-tuning J Lee, R Tang, J Lin arXiv preprint arXiv:1911.03090, 2019 | 37* | 2019 |
| Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting R Tang, J Lin arXiv preprint arXiv:1710.06554, 2017 | 35 | 2017 |
| BERxiT: Early exiting for BERT with better fine-tuning and extension to regression J Xin, R Tang, Y Yu, J Lin Proceedings of the 16th conference of the European chapter of the …, 2021 | 23 | 2021 |
| Natural Language Generation for Effective Knowledge Distillation R Tang, Y Lu, J Lin Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource …, 2019 | 23 | 2019 |
| Exploring the limits of simple learners in knowledge distillation for document classification with DocBERT A Adhikari, A Ram, R Tang, WL Hamilton, J Lin Proceedings of the 5th Workshop on Representation Learning for NLP, 72-77, 2020 | 21 | 2020 |
| Flops as a direct optimization objective for learning sparse neural networks R Tang, A Adhikari, J Lin arXiv preprint arXiv:1811.03060, 2018 | 19 | 2018 |
| Howl: A Deployed, Open-Source Wake Word Detection System R Tang*, J Lee*, A Razi, J Cambre, I Bicking, J Kaye, J Lin arXiv preprint arXiv:2008.09606, 2020 | 11 | 2020 |
| Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling L Liu, W Yang, J Rao, R Tang, J Lin Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 9 | 2019 |
| The art of abstention: Selective prediction and error regularization for natural language processing J Xin, R Tang, Y Yu, J Lin Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 8 | 2021 |
| Yelling at Your TV: An Analysis of Speech Recognition Errors and Subsequent User Behavior on Entertainment Systems R Tang, F Ture, J Lin Proceedings of the 42nd Annual International ACM SIGIR Conference on …, 2019 | 7 | 2019 |
| Adaptive pruning of neural language models for mobile devices R Tang, J Lin arXiv preprint arXiv:1809.10282, 2018 | 5 | 2018 |
| Inserting information bottlenecks for attribution in transformers Z Jiang, R Tang, J Xin, J Lin arXiv preprint arXiv:2012.13838, 2020 | 4 | 2020 |
Jimmy LinUniversity of WaterlooVerified email at uwaterloo.ca
