| Codegen: An open large language model for code with multi-turn program synthesis E Nijkamp, B Pang, H Hayashi, L Tu, H Wang, Y Zhou, S Savarese, ... arXiv preprint arXiv:2203.13474, 2022 | 236 | 2022 |
| Commonsense knowledge base completion X Li, A Taheri, L Tu, K Gimpel Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016 | 186 | 2016 |
| An empirical study on robustness to spurious correlations using pre-trained language models L Tu, G Lalwani, S Gella, H He Transactions of the Association for Computational Linguistics 8, 621-633, 2020 | 144 | 2020 |
| A conversational paradigm for program synthesis E Nijkamp, B Pang, H Hayashi, L Tu, H Wang, Y Zhou, S Savarese, ... arXiv preprint arXiv:2203.13474, 2022 | 107 | 2022 |
| Pay attention to the ending: Strong neural baselines for the roc story cloze task Z Cai, L Tu, K Gimpel Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017 | 66 | 2017 |
| Learning approximate inference networks for structured prediction L Tu, K Gimpel arXiv preprint arXiv:1803.03376, 2018 | 57 | 2018 |
| ENGINE: Energy-based inference networks for non-autoregressive machine translation L Tu, RY Pang, S Wiseman, K Gimpel arXiv preprint arXiv:2005.00850, 2020 | 47 | 2020 |
| Generating diverse story continuations with controllable semantics L Tu, X Ding, D Yu, K Gimpel arXiv preprint arXiv:1909.13434, 2019 | 18 | 2019 |
| Learning to embed words in context for syntactic tasks L Tu, K Gimpel, K Livescu arXiv preprint arXiv:1706.02807, 2017 | 18 | 2017 |
| Quality signals in generated stories M Sagarkar, J Wieting, L Tu, K Gimpel Proceedings of the Seventh Joint Conference on Lexical and Computational …, 2018 | 16 | 2018 |
| Benchmarking approximate inference methods for neural structured prediction L Tu, K Gimpel arXiv preprint arXiv:1904.01138, 2019 | 15 | 2019 |
| Improving joint training of inference networks and structured prediction energy networks L Tu, RY Pang, K Gimpel arXiv preprint arXiv:1911.02891, 2019 | 12 | 2019 |
| Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models L Tu, C Xiong, Y Zhou arXiv preprint arXiv:2210.12360, 2022 | 6 | 2022 |
| An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks L Tu, T Liu, K Gimpel arXiv preprint arXiv:2010.02789, 2020 | 3 | 2020 |
| Unsupervised Dense Retrieval Deserves Better Positive Pairs: Scalable Augmentation with Query Extraction and Generation R Meng, Y Liu, S Yavuz, D Agarwal, L Tu, N Yu, J Zhang, M Bhat, Y Zhou arXiv preprint arXiv:2212.08841, 2022 | 2 | 2022 |
| Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP L Tu arXiv preprint arXiv:2108.12522, 2021 | | 2021 |
| Improving and Stabilizing Deep Energy-Based Learning L Tu, RY Pang, K Gimpel | | 2019 |
| Network Inference by Learned Node-Specific Degree Prior Q Tang, L Tu, W Wang, J Xu arXiv preprint arXiv:1602.02386, 2016 | | 2016 |
| Protein-Protein Interaction Prediction via Structured Matrix Completion Q Tang, L Tu, AA Khan, J Xu | | |
Kevin GimpelToyota Technological Institute at Chicago, QuillBotE-mailová adresa ověřena na: ttic.edu
