| 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 | 103 | 2019 |
| Deep Residual Learning for Small-Footprint Keyword Spotting R Tang, J Lin 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 84 | 2018 |
| Docbert: Bert for document classification A Adhikari, A Ram, R Tang, J Lin arXiv preprint arXiv:1904.08398, 2019 | 62 | 2019 |
| 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 | 29 | 2019 |
| 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 | 19 | 2018 |
| Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting R Tang, J Lin arXiv preprint arXiv:1710.06554, 2017 | 17 | 2017 |
| DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference J Xin, R Tang, J Lee, Y Yu, J Lin arXiv preprint arXiv:2004.12993, 2020 | 14 | 2020 |
| 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 | 14 | 2020 |
| 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 | 13 | 2019 |
| 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 | 9 | 2020 |
| Flops as a direct optimization objective for learning sparse neural networks R Tang, A Adhikari, J Lin arXiv preprint arXiv:1811.03060, 2018 | 7 | 2018 |
| Progress and Tradeoffs in Neural Language Models R Tang, J Lin arXiv preprint arXiv:1811.00942, 2018 | 4 | 2018 |
| What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning J Lee, R Tang, J Lin arXiv preprint arXiv:1911.03090, 2019 | 3 | 2019 |
| 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 | 3 | 2019 |
| 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 | 3 | 2019 |
| Adaptive pruning of neural language models for mobile devices R Tang, J Lin arXiv preprint arXiv:1809.10282, 2018 | 3 | 2018 |
| Universal Voice-Enabled User Interfaces Using JavaScript J Lee, R Tang, J Lin Proceedings of the 24th International Conference on Intelligent User …, 2019 | 2 | 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 | 1 | 2020 |
| The Runoff Model-Intercomparison Project over Lake Erie and the Great Lakes J Mai, B Tolson, H Shen, E Gaborit, V Fortin, M Dimitrijevic, N Gasset, ... AGU Fall Meeting 2019, 2019 | 1 | 2019 |
| Honkling: In-Browser Personalization for Ubiquitous Keyword Spotting J Lee, R Tang, J Lin Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 1 | 2019 |
Jimmy LinUniversity of WaterlooVerified email at uwaterloo.ca
