| Deep learning for precipitation nowcasting: A benchmark and a new model X Shi, Z Gao, L Lausen, H Wang, DY Yeung, W Wong, W Woo Advances in neural information processing systems 30, 2017 | 578 | 2017 |
| Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing J Guo, H He, T He, L Lausen, M Li, H Lin, X Shi, C Wang, J Xie, S Zha, ... The Journal of Machine Learning Research 21 (1), 845-851, 2020 | 172 | 2020 |
| Nsml: A machine learning platform that enables you to focus on your models N Sung, M Kim, H Jo, Y Yang, J Kim, L Lausen, Y Kim, G Lee, D Kwak, ... arXiv preprint arXiv:1712.05902, 2017 | 65 | 2017 |
| Exploring the role of task transferability in large-scale multi-task learning V Padmakumar, L Lausen, M Ballesteros, S Zha, H He, G Karypis arXiv preprint arXiv:2204.11117, 2022 | 6 | 2022 |
| Dive into Deep Learning for Natural Language Processing H Lin, X Shi, L Lausen, A Zhang, H He, S Zha, A Smola Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 2 | 2019 |
| CrowdRisk: exploring crowdsourcing of risk information L Lausen, M Rittenbruch, P Mitchell, E Horton, M Foth Proceedings of the 28th Australian Conference on Computer-Human Interaction …, 2016 | 2 | 2016 |
| Better context makes better code language models: A case study on function call argument completion H Pei, J Zhao, L Lausen, S Zha, G Karypis | | 2023 |
| Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic S Sarkar, K Lin, S Sengupta, L Lausen, S Zha, S Mansour arXiv preprint arXiv:2211.03966, 2022 | | 2022 |
| Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic SSK Lin, S Sengupta, L Lausen, SZS Mansour | | |
