| Dense passage retrieval for open-domain question answering V Karpukhin, B Oğuz, S Min, P Lewis, L Wu, S Edunov, D Chen, W Yih arXiv preprint arXiv:2004.04906, 2020 | 3468 | 2020 |
| Learning fair representations R Zemel, Y Wu, K Swersky, T Pitassi, C Dwork International conference on machine learning, 325-333, 2013 | 2314 | 2013 |
| Eva: Exploring the limits of masked visual representation learning at scale Y Fang, W Wang, B Xie, Q Sun, L Wu, X Wang, T Huang, X Wang, Y Cao Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 703 | 2023 |
| Scalable zero-shot entity linking with dense entity retrieval L Wu, F Petroni, M Josifoski, S Riedel, L Zettlemoyer arXiv preprint arXiv:1911.03814, 2019 | 561 | 2019 |
| Pytorch-biggraph: A large scale graph embedding system A Lerer, L Wu, J Shen, T Lacroix, L Wehrstedt, A Bose, A Peysakhovich Proceedings of Machine Learning and Systems 1, 120-131, 2019 | 468 | 2019 |
| Starspace: Embed all the things! LY Wu, A Fisch, S Chopra, K Adams, A Bordes, J Weston Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 326 | 2018 |
| Multi-dimensional gender bias classification E Dinan, A Fan, L Wu, J Weston, D Kiela, A Williams arXiv preprint arXiv:2005.00614, 2020 | 150 | 2020 |
| Multilingual autoregressive entity linking N De Cao, L Wu, K Popat, M Artetxe, N Goyal, M Plekhanov, ... Transactions of the Association for Computational Linguistics 10, 274-290, 2022 | 140 | 2022 |
| Altclip: Altering the language encoder in clip for extended language capabilities Z Chen, G Liu, BW Zhang, F Ye, Q Yang, L Wu arXiv preprint arXiv:2211.06679, 2022 | 79 | 2022 |
| Dynaboard: An evaluation-as-a-service platform for holistic next-generation benchmarking Z Ma, K Ethayarajh, T Thrush, S Jain, L Wu, R Jia, C Potts, A Williams, ... Advances in Neural Information Processing Systems 34, 10351-10367, 2021 | 62 | 2021 |
| Ultron: An ultimate retriever on corpus with a model-based indexer Y Zhou, J Yao, Z Dou, L Wu, P Zhang, JR Wen arXiv preprint arXiv:2208.09257, 2022 | 49 | 2022 |
| Inapproximability of treewidth and related problems Y Wu, P Austrin, T Pitassi, D Liu Journal of Artificial Intelligence Research 49, 569-600, 2014 | 42 | 2014 |
| Ptab: Using the pre-trained language model for modeling tabular data G Liu, J Yang, L Wu arXiv preprint arXiv:2209.08060, 2022 | 33 | 2022 |
| Inapproximability of treewidth, one-shot pebbling, and related layout problems P Austrin, T Pitassi, Y Wu International Workshop on Approximation Algorithms for Combinatorial …, 2012 | 33 | 2012 |
| Dynamicretriever: A pre-training model-based IR system with neither sparse nor dense index Y Zhou, J Yao, Z Dou, L Wu, JR Wen arXiv preprint arXiv:2203.00537, 2022 | 27 | 2022 |
| Altdiffusion: A multilingual text-to-image diffusion model F Ye, G Liu, X Wu, L Wu Proceedings of the AAAI Conference on Artificial Intelligence 38 (7), 6648-6656, 2024 | 18 | 2024 |
| DynamicRetriever: a pre-trained model-based IR system without an explicit index YJ Zhou, J Yao, ZC Dou, L Wu, JR Wen Machine Intelligence Research 20 (2), 276-288, 2023 | 15 | 2023 |
| Proceedings of the 30th International Conference on Machine Learning R Zemel, Y Wu, K Swersky, T Pitassi, C Dwork PMLR 28 (3), 325-333, 2013 | 15 | 2013 |
| Unitabe: A universal pretraining protocol for tabular foundation model in data science Y Yang, Y Wang, G Liu, L Wu, Q Liu The Twelfth International Conference on Learning Representations, 2024 | 13 | 2024 |
| Unitabe: Pretraining a unified tabular encoder for heterogeneous tabular data Y Yang, Y Wang, G Liu, L Wu, Q Liu arXiv preprint arXiv:2307.09249, 2023 | 7 | 2023 |
Vladimir Karpukhinfacebook AI researchVerified email at fb.com