| Meta Networks T Munkhdalai, H Yu ICML 2017, 2017 | 641 | 2017 |
| The CHEMDNER corpus of chemicals and drugs and its annotation principles M Krallinger, O Rabal, F Leitner, M Vazquez, D Salgado, Z Lu, R Leaman, ... Journal of cheminformatics 7 (1), 1-17, 2015 | 174 | 2015 |
| Neural Semantic Encoders T Munkhdalai, H Yu EACL 2017, 2016 | 165* | 2016 |
| Rapid adaptation with conditionally shifted neurons T Munkhdalai, X Yuan, S Mehri, A Trischler ICML 2018, 3661-3670, 2018 | 159 | 2018 |
| Neural tree indexers for text understanding T Munkhdalai, H Yu EACL 2017 1, 11, 2017 | 102 | 2017 |
| An empirical comparison of machine-learning methods on bank client credit assessments L Munkhdalai, T Munkhdalai, OE Namsrai, JY Lee, KH Ryu Sustainability 11 (3), 699, 2019 | 59 | 2019 |
| Clinical relation extraction toward drug safety surveillance using electronic health record narratives: classical learning versus deep learning T Munkhdalai, F Liu, H Yu JMIR public health and surveillance 4 (2), e9361, 2018 | 57 | 2018 |
| Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension R Das, T Munkhdalai, X Yuan, A Trischler, A McCallum ICLR 2019, 2018 | 53 | 2018 |
| Sentence Simplification with Memory-Augmented Neural Networks T Vu, B Hu, T Munkhdalai, H Yu NAACL 2018, 2018 | 51 | 2018 |
| Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations T Munkhdalai, M Li, K Batsuren, HA Park, NH Choi, KH Ryu Journal of cheminformatics 7 (1), 1-8, 2015 | 51 | 2015 |
| A feature selection-based ensemble method for arrhythmia classification E Namsrai, T Munkhdalai, M Li, JH Shin, OE Namsrai, KH Ryu Journal of Information Processing Systems 9 (1), 31-40, 2013 | 37 | 2013 |
| Metalearned Neural Memory T Munkhdalai, A Sordoni, T Wang, A Trischler NeurIPS 2019, 2019 | 29 | 2019 |
| Exploring and predicting transferability across NLP tasks T Vu, T Wang, T Munkhdalai, A Sordoni, A Trischler, A Mattarella-Micke, ... arXiv preprint arXiv:2005.00770, 2020 | 27 | 2020 |
| Self-training in significance space of support vectors for imbalanced biomedical event data T Munkhdalai, OE Namsrai, KH Ryu BMC bioinformatics 16 (7), 1-8, 2015 | 27 | 2015 |
| Metalearning with hebbian fast weights T Munkhdalai, A Trischler arXiv preprint arXiv:1807.05076, 2018 | 23 | 2018 |
| Bio named entity recognition based on co-training algorithm T Munkhdalai, M Li, T Kim, OE Namsrai, S Jeong, J Shin, KH Ryu 2012 26th International Conference on Advanced Information Networking and …, 2012 | 22 | 2012 |
| Reasoning with memory augmented neural networks for language comprehension T Munkhdalai, H Yu ICLR 2017, 2016 | 20 | 2016 |
| An end-to-end adaptive input selection with dynamic weights for forecasting multivariate time series L Munkhdalai, T Munkhdalai, KH Park, T Amarbayasgalan, E Batbaatar, ... IEEE Access 7, 99099-99114, 2019 | 19 | 2019 |
| Understanding deep learning performance through an examination of test set difficulty: A psychometric case study JP Lalor, H Wu, T Munkhdalai, H Yu Proceedings of the Conference on Empirical Methods in Natural Language …, 2018 | 19 | 2018 |
| Self-supervised meta-learning for few-shot natural language classification tasks T Bansal, R Jha, T Munkhdalai, A McCallum arXiv preprint arXiv:2009.08445, 2020 | 18 | 2020 |
hong yuProfessor, Depts Computer Science at Lowell and Amherst (Adjunct) and Medicine (Adjunct), UMassVerified email at umassmed.edu
