| FILIP: Fine-grained Interactive Language-Image Pre-training L Yao*, R Huang*, L Hou*, G Lu, M Niu, H Xu, X Liang, Z Li, X Jiang, C Xu 10th International Conference on Learning Representations (ICLR-2022), 2022 | 567 | 2022 |
| Dynabert: Dynamic bert with adaptive width and depth L Hou, Z Huang, L Shang, X Jiang, X Chen, Q Liu Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS-2020), 2020 | 316 | 2020 |
| Loss-aware Binarization of Deep Networks L Hou, Q Yao, JT Kwok 5th International Conference on Learning Representations (ICLR-2017), 2016 | 263 | 2016 |
| BinaryBERT: Pushing the Limit of BERT Quantization H Bai, W Zhang, L Hou, L Shang, J Jin, X Jiang, Q Liu, M Lyu, I King 59th Annual Meeting of the Association for Computational Linguistics (ACL-2021), 2021 | 231 | 2021 |
| TernaryBERT: Distillation-aware Ultra-low Bit BERT W Zhang*, L Hou*, Y Yin*, L Shang, X Chen, X Jiang, Q Liu Conference on Empirical Methods in Natural Language Processing (EMNLP-2020), 2020 | 207 | 2020 |
| Loss-aware Weight Quantization of Deep Networks L Hou, JT Kwok 6th International Conference on Learning Representations (ICLR-2018), 2018 | 164 | 2018 |
| Wukong: A 100 million large-scale chinese cross-modal pre-training benchmark J Gu, X Meng, G Lu, L Hou, N Minzhe, X Liang, L Yao, R Huang, W Zhang, ... Advances in Neural Information Processing Systems 35, 26418-26431, 2022 | 110 | 2022 |
| Efficient Learning of Timeseries Shapelets L Hou, JT Kwok, JM Zurada the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-2016), 2016 | 103 | 2016 |
| Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation W Dai, L Hou, L Shang, X Jiang, Q Liu, P Fung Findings of the Association for Computational Linguistics （ACL-IJCNLP 2022）, 2022 | 97 | 2022 |
| Timechat: A time-sensitive multimodal large language model for long video understanding S Ren, L Yao, S Li, X Sun, L Hou Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 95 | 2024 |
| Compression of Generative Pre-trained Language Models via Quantization C Tao, L Hou, W Zhang, L Shang, X Jiang, Q Liu, P Luo, N Wong 60th Annual Meeting of the Association for Computational Linguistics (ACL-2022), 2022 | 86 | 2022 |
| Improved OOD Generalization via Adversarial Training and Pre-training M Yi, L Hou, J Sun, L Shang, X Jiang, Q Liu, ZM Ma The Thirty-eighth International Conference on Machine Learning (ICML-2021), 2021 | 71 | 2021 |
| Towards efficient post-training quantization of pre-trained language models H Bai, L Hou, L Shang, X Jiang, I King, MR Lyu Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS-2022), 2021 | 59 | 2021 |
| Normalization Helps Training of Quantized LSTM L Hou, J Zhu, JT Kwok, F Gao, T Qin, T Liu Thirty-third Conference on Neural Information Processing Systems (NeurIPS-2019), 2019 | 51 | 2019 |
| Ctrl: Connect tabular and language model for ctr prediction X Li, B Chen, L Hou, R Tang CoRR, 2023 | 42 | 2023 |
| Fetv: A benchmark for fine-grained evaluation of open-domain text-to-video generation Y Liu, L Li, S Ren, R Gao, S Li, S Chen, X Sun, L Hou Advances in Neural Information Processing Systems 36, 2024 | 39 | 2024 |
| Tempcompass: Do video llms really understand videos? Y Liu, S Li, Y Liu, Y Wang, S Ren, L Li, S Chen, X Sun, L Hou arXiv preprint arXiv:2403.00476, 2024 | 37 | 2024 |
| Analysis of Quantized Models L Hou, R Zhang, JT Kwok 7th International Conference on Learning Representations (ICLR-2019), 2019 | 34 | 2019 |
| Structured pruning for efficient generative pre-trained language models C Tao, L Hou, H Bai, J Wei, X Jiang, Q Liu, P Luo, N Wong Findings of the Association for Computational Linguistics: ACL 2023, 10880-10895, 2023 | 27 | 2023 |
| Ghostbert: Generate more features with cheap operations for BERT Z Huang, L Hou, L Shang, X Jiang, X Chen, Q Liu 59th Annual Meeting of the Association for Computational Linguistics (ACL …, 2021 | 26 | 2021 |
Xin JiangNoah’s Ark Lab, Huawei TechnologiesVerified email at huawei.com
