| The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process H Mei, J Eisner arXiv, 2016 | 909 | 2016 |
| What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment H Mei, M Bansal, MR Walter NAACL, 2016 | 347 | 2016 |
| Listen, attend, and walk: Neural mapping of navigational instructions to action sequences H Mei, M Bansal, MR Walter AAAI, 2016 | 302 | 2016 |
| Coherent Dialogue with Attention-based Language Models H Mei, M Bansal, MR Walter AAAI, 2017 | 124 | 2017 |
| Transformer embeddings of irregularly spaced events and their participants C Yang, H Mei, J Eisner arXiv preprint arXiv:2201.00044, 2021 | 98 | 2021 |
| Hypothesis generation with large language models Y Zhou, H Liu, T Srivastava, H Mei, C Tan arXiv preprint arXiv:2404.04326, 2024 | 97 | 2024 |
| Language models can improve event prediction by few-shot abductive reasoning X Shi, S Xue, K Wang, F Zhou, J Zhang, J Zhou, C Tan, H Mei Advances in Neural Information Processing Systems 36, 29532-29557, 2023 | 90 | 2023 |
| Imputing missing events in continuous-time event streams H Mei, G Qin, J Eisner International Conference on Machine Learning, 4475-4485, 2019 | 69 | 2019 |
| Easytpp: Towards open benchmarking temporal point processes S Xue, X Shi, Z Chu, Y Wang, H Hao, F Zhou, C Jiang, C Pan, JY Zhang, ... arXiv preprint arXiv:2307.08097, 2023 | 67 | 2023 |
| Hypro: A hybridly normalized probabilistic model for long-horizon prediction of event sequences S Xue, X Shi, J Zhang, H Mei Advances in Neural Information Processing Systems 35, 34641-34650, 2022 | 58 | 2022 |
| Statler: State-maintaining language models for embodied reasoning T Yoneda, J Fang, P Li, H Zhang, T Jiang, S Lin, B Picker, D Yunis, H Mei, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 15083 …, 2024 | 57 | 2024 |
| Can large language models play text games well? current state-of-the-art and open questions CF Tsai, X Zhou, SS Liu, J Li, M Yu, H Mei arXiv preprint arXiv:2304.02868, 2023 | 50 | 2023 |
| Robustness of learning from task instructions J Gu, H Zhao, H Xu, L Nie, H Mei, W Yin Findings of the association for computational linguistics: ACL 2023, 13935-13948, 2023 | 39 | 2023 |
| Personalized dynamic treatment regimes in continuous time: a bayesian approach for optimizing clinical decisions with timing W Hua, H Mei, S Zohar, M Giral, Y Xu Bayesian Analysis 17 (3), 849-878, 2022 | 34 | 2022 |
| Noise-contrastive estimation for multivariate point processes H Mei, T Wan, J Eisner Advances in neural information processing systems 33, 5204-5214, 2020 | 31 | 2020 |
| Neural Datalog through time: Informed temporal modeling via logical specification H Mei, G Qin, M Xu, J Eisner International Conference on Machine Learning, 6808-6819, 2020 | 28 | 2020 |
| Continuous-time decision transformer for healthcare applications Z Zhang, H Mei, Y Xu International conference on artificial intelligence and statistics, 6245-6262, 2023 | 27 | 2023 |
| Tiny-attention adapter: Contexts are more important than the number of parameters H Zhao, H Tan, H Mei arXiv preprint arXiv:2211.01979, 2022 | 26 | 2022 |
| Weaverbird: Empowering financial decision-making with large language model, knowledge base, and search engine S Xue, F Zhou, Y Xu, M Jin, Q Wen, H Hao, Q Dai, C Jiang, H Zhao, S Xie, ... arXiv preprint arXiv:2308.05361, 2023 | 22 | 2023 |
| Hidden state variability of pretrained language models can guide computation reduction for transfer learning S Xie, J Qiu, A Pasad, L Du, Q Qu, H Mei arXiv preprint arXiv:2210.10041, 2022 | 22 | 2022 |
Jason EisnerProfessor of Computer Science, Johns Hopkins UniversityVerified email at cs.jhu.edu
