| Incremental network quantization: Towards lossless cnns with low-precision weights A Zhou, A Yao, Y Guo, L Xu, Y Chen ICLR2017, 2017 | 1379 | 2017 |
| Llama-adapter: Efficient fine-tuning of language models with zero-init attention R Zhang, J Han, A Zhou, X Hu, S Yan, P Lu, H Li, P Gao, Y Qiao ICLR2024, 2023 | 800 | 2023 |
| Incorporating convolution designs into visual transformers K Yuan, S Guo, Z Liu, A Zhou, F Yu, W Wu ICCV2021, 2021 | 619 | 2021 |
| Llama-adapter v2: Parameter-efficient visual instruction model P Gao, J Han, R Zhang, Z Lin, S Geng, A Zhou, W Zhang, P Lu, C He, ... arXiv preprint arXiv:2304.15010, 2023 | 545 | 2023 |
| Omni-dimensional dynamic convolution C Li, A Zhou, A Yao ICLR2022, 2022 | 375 | 2022 |
| Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch A Zhou, Y Ma, J Zhu, J Liu, Z Zhang, K Yuan, W Sun, H Li ICLR2021, 2021 | 262 | 2021 |
| Adversarial Robustness vs Model Compression, or Both? S Ye, K Xu, S Liu, H Cheng, JH Lambrechts, H Zhang, A Zhou, K Ma, ... ICCV2019, 2019 | 205 | 2019 |
| Group Fisher Pruning for Practical Network Compression L Liu, S Zhang, Z Kuang, A Zhou, JH Xue, X Wang, Y Chen, W Yang, ... ICML2021, 7021-7032, 2021 | 172 | 2021 |
| Deep neural network compression with single and multiple level quantization Y Xu, Y Wang, A Zhou, W Lin, H Xiong AAAI2018 32 (1), 2018 | 153 | 2018 |
| Solving challenging math word problems using gpt-4 code interpreter with code-based self-verification A Zhou, K Wang, Z Lu, W Shi, S Luo, Z Qin, S Lu, A Jia, L Song, M Zhan, ... ICLR2024, 2023 | 131 | 2023 |
| Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? R Zhang, D Jiang, Y Zhang, H Lin, Z Guo, P Qiu, A Zhou, P Lu, KW Chang, ... ECCV 2024, 2024 | 115 | 2024 |
| Explicit loss-error-aware quantization for low-bit deep neural networks A Zhou, A Yao, K Wang, Y Chen CVPR2018, 9426-9435, 2018 | 114 | 2018 |
| Deeply-supervised knowledge synergy D Sun, A Yao, A Zhou, H Zhao CVPR2019, 6997-7006, 2019 | 94 | 2019 |
| Not all features matter: Enhancing few-shot clip with adaptive prior refinement X Zhu, R Zhang, B He, A Zhou, D Wang, B Zhao, P Gao Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 72 | 2023 |
| Journeydb: A benchmark for generative image understanding J Pan, K Sun, Y Ge, H Li, H Duan, X Wu, R Zhang, A Zhou, Z Qin, Y Wang, ... Neurips 2023 DAB tracks, 2023 | 71 | 2023 |
| MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning K Wang, H Ren, A Zhou, Z Lu, S Luo, W Shi, R Zhang, L Song, M Zhan, ... ICLR2024 (Co-first Author), 2023 | 69 | 2023 |
| An algorithm–hardware co-optimized framework for accelerating n: M sparse transformers C Fang, A Zhou, Z Wang IEEE Transactions on Very Large Scale Integration (VLSI) Systems 30 (11 …, 2022 | 65 | 2022 |
| DominoSearch: Find layer-wise fine-grained N: M sparse schemes from dense neural networks W Sun*, A Zhou*, S Stuijk, R Wijnhoven, AO Nelson, H Corporaal NeurIPS2021 34, 2021 | 64 | 2021 |
| HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions A Zhou*, D Li*, A Yao, equal contribution ICCV2019, 2019 | 51* | 2019 |
| Group R-CNN for weakly semi-supervised object detection with points S Zhang, Z Yu, L Liu, X Wang, A Zhou, K Chen Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 48 | 2022 |
Anbang Yao (姚安邦)Principal AI Scientist, PE, Intel Labs China確認したメール アドレス: intel.com
