| Loss-aware Binarization of Deep Networks L Hou, Q Yao, JT Kwok 5th International Conference on Learning Representations (ICLR-2017), 2016 | 177 | 2016 |
| Loss-aware Weight Quantization of Deep Networks L Hou, JT Kwok 6th International Conference on Learning Representations (ICLR-2018), 2018 | 105 | 2018 |
| 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 | 88 | 2020 |
| Efficient Learning of Timeseries Shapelets L Hou, JT Kwok, JM Zurada the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-2016), 2016 | 75 | 2016 |
| 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 | 52 | 2020 |
| 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 | 41 | 2021 |
| Analysis of Quantized Models L Hou, R Zhang, JT Kwok 7th International Conference on Learning Representations (ICLR-2019), 2019 | 25 | 2019 |
| 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 | 21 | 2019 |
| 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 | 18 | 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 | 12 | 2021 |
| 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 | 10 | 2021 |
| Reweighting Augmented Samples by Minimizing the Maximal Expected Loss M Yi, L Hou, L Shang, X Jiang, Q Liu, ZM Ma 9th International Conference on Learning Representations (ICLR-2021), 2021 | 7 | 2021 |
| Power law in sparsified deep neural networks L Hou, JT Kwok arXiv preprint arXiv:1805.01891, 2018 | 4 | 2018 |
| 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 | 1 | 2022 |
| Wukong: 100 Million Large-scale Chinese Cross-modal Pre-training Dataset and A Foundation Framework J Gu, X Meng, G Lu, L Hou, M Niu, H Xu, X Liang, W Zhang, X Jiang, C Xu arXiv preprint arXiv:2202.06767, 2022 | 1 | 2022 |
| Towards efficient post-training quantization of pre-trained language models H Bai, L Hou, L Shang, X Jiang, I King, MR Lyu arXiv preprint arXiv:2109.15082, 2021 | 1 | 2021 |
| 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 | | 2022 |
Xin JiangNoah’s Ark Lab, Huawei TechnologiesVerified email at huawei.com
