| Rethinking attention with performers K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ... arXiv preprint arXiv:2009.14794, 2020 | 1574 | 2020 |
| Fast and accurate entity recognition with iterated dilated convolutions E Strubell, P Verga, D Belanger, A McCallum arXiv preprint arXiv:1702.02098, 2017 | 568 | 2017 |
| Sequential regulatory activity prediction across chromosomes with convolutional neural networks DR Kelley, YA Reshef, M Bileschi, D Belanger, CY McLean, J Snoek Genome research 28 (5), 739-750, 2018 | 458 | 2018 |
| Ask the GRU Multi-task Learning for Deep Text Recommendations T Bansal, D Belanger, A McCallum proceedings of the 10th ACM Conference on Recommender Systems, 107-114, 2016 | 387 | 2016 |
| Chains of reasoning over entities, relations, and text using recurrent neural networks R Das, A Neelakantan, D Belanger, A McCallum arXiv preprint arXiv:1607.01426, 2016 | 330 | 2016 |
| Using deep learning to annotate the protein universe ML Bileschi, D Belanger, DH Bryant, T Sanderson, B Carter, D Sculley, ... Nature Biotechnology 40 (6), 932-937, 2022 | 269 | 2022 |
| Learning latent permutations with gumbel-sinkhorn networks G Mena, D Belanger, S Linderman, J Snoek arXiv preprint arXiv:1802.08665, 2018 | 268 | 2018 |
| Earthquake ruptures with strongly rate-weakening friction and off-fault plasticity, part 2: Nonplanar faults EM Dunham, D Belanger, L Cong, JE Kozdon Bulletin of the Seismological Society of America 101 (5), 2308-2322, 2011 | 257 | 2011 |
| Structured prediction energy networks D Belanger, A McCallum International Conference on Machine Learning, 983-992, 2016 | 255 | 2016 |
| Earthquake ruptures with strongly rate-weakening friction and off-fault plasticity, Part 1: Planar faults EM Dunham, D Belanger, L Cong, JE Kozdon Bulletin of the Seismological Society of America 101 (5), 2296-2307, 2011 | 192 | 2011 |
| Synthesizing normalized faces from facial identity features F Cole, D Belanger, D Krishnan, A Sarna, I Mosseri, WT Freeman Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 183 | 2017 |
| End-to-end learning for structured prediction energy networks D Belanger, B Yang, A McCallum International Conference on Machine Learning, 429-439, 2017 | 147 | 2017 |
| Rapid prediction of electron–ionization mass spectrometry using neural networks JN Wei, D Belanger, RP Adams, D Sculley ACS central science 5 (4), 700-708, 2019 | 145 | 2019 |
| Model-based reinforcement learning for biological sequence design C Angermueller, D Dohan, D Belanger, R Deshpande, K Murphy, ... International conference on learning representations, 2019 | 133 | 2019 |
| Multilingual relation extraction using compositional universal schema P Verga, D Belanger, E Strubell, B Roth, A McCallum arXiv preprint arXiv:1511.06396, 2015 | 116 | 2015 |
| Boundless: Generative adversarial networks for image extension P Teterwak, A Sarna, D Krishnan, A Maschinot, D Belanger, C Liu, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 113 | 2019 |
| ProteInfer, deep neural networks for protein functional inference T Sanderson, ML Bileschi, D Belanger, LJ Colwell Elife 12, e80942, 2023 | 99 | 2023 |
| Masked language modeling for proteins via linearly scalable long-context transformers K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ... arXiv preprint arXiv:2006.03555, 2020 | 92 | 2020 |
| Population-based black-box optimization for biological sequence design C Angermueller, D Belanger, A Gane, Z Mariet, D Dohan, K Murphy, ... International conference on machine learning, 324-334, 2020 | 57 | 2020 |
| Is transfer learning necessary for protein landscape prediction? A Shanehsazzadeh, D Belanger, D Dohan arXiv preprint arXiv:2011.03443, 2020 | 54 | 2020 |
Andrew McCallumDistinguished Professor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
