| Emergent complexity via multi-agent competition T Bansal, J Pachocki, S Sidor, I Sutskever, I Mordatch arXiv preprint arXiv:1710.03748, 2017 | 518 | 2017 |
| Continuous adaptation via meta-learning in nonstationary and competitive environments M Al-Shedivat, T Bansal, Y Burda, I Sutskever, I Mordatch, P Abbeel arXiv preprint arXiv:1710.03641, 2017 | 438 | 2017 |
| 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 | 403 | 2016 |
| A2N: Attending to neighbors for knowledge graph inference T Bansal, DC Juan, S Ravi, A McCallum Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 156 | 2019 |
| Learning to few-shot learn across diverse natural language classification tasks T Bansal, R Jha, A McCallum arXiv preprint arXiv:1911.03863, 2019 | 126 | 2019 |
| Content driven user profiling for comment-worthy recommendations of news and blog articles T Bansal, M Das, C Bhattacharyya Proceedings of the 9th ACM Conference on Recommender Systems, 195-202, 2015 | 96 | 2015 |
| 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 | 92 | 2020 |
| Marginal likelihood training of BiLSTM-CRF for biomedical named entity recognition from disjoint label sets N Greenberg, T Bansal, P Verga, A McCallum Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 88 | 2018 |
| A provable SVD-based algorithm for learning topics in dominant admixture corpus T Bansal, C Bhattacharyya, R Kannan Advances in neural information processing systems 27, 2014 | 57 | 2014 |
| Unsupervised pre-training for biomedical question answering V Kommaraju, K Gunasekaran, K Li, T Bansal, A McCallum, I Williams, ... arXiv preprint arXiv:2009.12952, 2020 | 48 | 2020 |
| Diverse distributions of self-supervised tasks for meta-learning in NLP T Bansal, K Gunasekaran, T Wang, T Munkhdalai, A McCallum arXiv preprint arXiv:2111.01322, 2021 | 44 | 2021 |
| Relnet: End-to-end modeling of entities & relations T Bansal, A Neelakantan, A McCallum arXiv preprint arXiv:1706.07179, 2017 | 35 | 2017 |
| Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-Learning T Bansal, S Alzubi, T Wang, JY Lee, A McCallum First Conference on Automated Machine Learning (Main Track), 2022 | 22 | 2022 |
| Going beyond corr-lda for detecting specific comments on news & blogs MK Das, T Bansal, C Bhattacharyya Proceedings of the 7th ACM international conference on Web search and data …, 2014 | 22 | 2014 |
| Simultaneously linking entities and extracting relations from biomedical text without mention-level supervision T Bansal, P Verga, N Choudhary, A McCallum Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7407-7414, 2020 | 18 | 2020 |
| A moment in the sun: Solar nowcasting from multispectral satellite data using self-supervised learning AS Bansal, T Bansal, D Irwin Proceedings of the thirteenth ACM international conference on future energy …, 2022 | 9 | 2022 |
| Self-supervised learning on multispectral satellite data for near-term solar forecasting AS Bansal, T Bansal, D Irwin International Conference on Machine Learning (ICML 2021) Workshop on …, 2021 | 4 | 2021 |
| Simultaneously Self-Attending to Text and Entities for Knowledge-Informed Text Representations D Thai, R Thirukovalluru, T Bansal, A McCallum Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP …, 2021 | 2 | 2021 |
| Relating romanized comments to news articles by inferring multi-glyphic topical correspondence G Tholpadi, M Das, T Bansal, C Bhattacharyya Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 2 | 2015 |
| Few-shot natural language processing by meta-learning without labeled data T Bansal | 1 | 2022 |
Andrew McCallumDistinguished Professor of Computer Science, University of Massachusetts AmherstEmail được xác minh tại cs.umass.edu