| Energy and policy considerations for deep learning in NLP E Strubell, A Ganesh, A McCallum arXiv preprint arXiv:1906.02243, 2019 | 2117 | 2019 |
| Fast and accurate entity recognition with iterated dilated convolutions E Strubell, P Verga, D Belanger, A McCallum Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 465 | 2017 |
| Linguistically-Informed Self-Attention for Semantic Role Labeling E Strubell, P Verga, D Andor, D Weiss, A McCallum Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 398 | 2018 |
| Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction P Verga, E Strubell, A McCallum Proceedings of the 2018 Conference of the North American Chapter of the …, 2018 | 253 | 2018 |
| Energy and policy considerations for modern deep learning research E Strubell, A Ganesh, A McCallum Proceedings of the AAAI Conference on Artificial Intelligence 34 (09), 13693 …, 2020 | 197 | 2020 |
| Machine-learned and codified synthesis parameters of oxide materials E Kim, K Huang, A Tomala, S Matthews, E Strubell, A Saunders, ... Scientific data 4 (1), 1-9, 2017 | 134 | 2017 |
| Multilingual relation extraction using compositional universal schema P Verga, D Belanger, E Strubell, B Roth, A McCallum Proceedings of the 2016 Conference of the North American Chapter of the …, 2015 | 110 | 2015 |
| Inorganic materials synthesis planning with literature-trained neural networks E Kim, Z Jensen, A van Grootel, K Huang, M Staib, S Mysore, HS Chang, ... Journal of chemical information and modeling 60 (3), 1194-1201, 2020 | 86 | 2020 |
| The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures S Mysore, Z Jensen, E Kim, K Huang, HS Chang, E Strubell, J Flanigan, ... arXiv preprint arXiv:1905.06939, 2019 | 77 | 2019 |
| An introduction to quantum algorithms E Strubell | 52 | 2011 |
| Energy and policy considerations for deep learning in NLP. arXiv 2019 E Strubell, A Ganesh, A McCallum arXiv preprint arXiv:1906.02243, 2020 | 41 | 2020 |
| Aligning artificial intelligence with climate change mitigation LH Kaack, PL Donti, E Strubell, G Kamiya, F Creutzig, D Rolnick Nature Climate Change 12 (6), 518-527, 2022 | 40 | 2022 |
| Automatically extracting action graphs from materials science synthesis procedures S Mysore, E Kim, E Strubell, A Liu, HS Chang, S Kompella, K Huang, ... arXiv preprint arXiv:1711.06872, 2017 | 38 | 2017 |
| Measuring the carbon intensity of ai in cloud instances J Dodge, T Prewitt, R Tachet des Combes, E Odmark, R Schwartz, ... 2022 ACM Conference on Fairness, Accountability, and Transparency, 1877-1894, 2022 | 30 | 2022 |
| An empirical investigation of the role of pre-training in lifelong learning SV Mehta, D Patil, S Chandar, E Strubell arXiv preprint arXiv:2112.09153, 2021 | 28 | 2021 |
| Multi-task learning for parsing the alexa meaning representation language V Perera, T Chung, T Kollar, E Strubell Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 21 | 2018 |
| Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches. B Roth, N Monath, D Belanger, E Strubell, P Verga, A McCallum TAC, 2015 | 19 | 2015 |
| Learning dynamic feature selection for fast sequential prediction E Strubell, L Vilnis, K Silverstein, A McCallum Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015 | 18 | 2015 |
| Artificial intelligence and climate change: Opportunities, considerations, and policy levers to align AI with climate change goals L Kaack, P Donti, E Strubell, D Rolnick | 12 | 2020 |
| Dependency parsing with dilated iterated graph cnns E Strubell, A McCallum arXiv preprint arXiv:1705.00403, 2017 | 12 | 2017 |
Andrew McCallumDistinguished Professor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
