| 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 | 1020 | 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, ... Journal of Machine Learning Research 21 (23), 1-7, 2020 | 236 | 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 | 84 | 2017 |
| HYTREL: Hypergraph-enhanced tabular data representation learning P Chen, S Sarkar, L Lausen, B Srinivasan, S Zha, R Huang, G Karypis Advances in Neural Information Processing Systems 36, 2024 | 25 | 2024 |
| 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 Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 5230-5238, 2023 | 18 | 2023 |
| 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 | 18 | 2022 |
| Large language models of code fail at completing code with potential bugs T Dinh, J Zhao, S Tan, R Negrinho, L Lausen, S Zha, G Karypis Advances in Neural Information Processing Systems 36, 2024 | 16 | 2024 |
| Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks S Sarkar, L Lausen arXiv preprint arXiv:2310.00789, 2023 | 4* | 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 | 2 | 2022 |
| 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 |
| 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 | 1 | 2019 |
| Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning S Sarkar, L Lausen, V Cevher, S Zha, T Brox, G Karypis arXiv preprint arXiv:2409.01483, 2024 | | 2024 |
