Haotong Qin

Center for Project-Based Learning D-ITET, ETH Zürich
Office: ETF F110, Sternwartstrasse 7, 8006 Zürich, Switzerland
Email: qinhaotong@gmail.com

I am a postdoctoral researcher at the Center for Project-Based Learning (PBL), ETH Zürich, Switzerland, working with PD Dr. Michele Magno. Previously, I received my Ph.D. degree from the School of Computer Science and Engineering (SCSE) and Shen Yuan Honors College, Beihang University in 2024, supervised by Prof. Wei Li and Prof. Xianglong Liu. I was a visiting PhD student at Computer Vision Lab, ETH Zürich. I obtained my BSc degree from SCSE, Beihang University in 2019. I interned at ByteDance, Tencent, and Microsoft Research Asia. And I was awarded by 2023 Baidu Scholarship, 2022 ByteDance Scholarship, 2023 KAUST Rising Stars in AI, and 2023 DAAD AInet Fellowship, etc.

My research interest broadly includes efficient deep learning. Specifically, I focus on deep model compression (e.g., network binarization, quantization, and distillation), efficient generative model (e.g., efficient large language models and diffusion models), neuromorphic computing (e.g., spiking neural network), hardware acceleration (e.g., hardware-aware architecture search), etc. I am/was an Program Committee member for ICML'(2023-2024), NeurIPS'2023, ICLR'2024, CVPR'(2023-2024), ICCV'2023, ECCV'2024, etc., and Organizer or Challenge Chair for workshops at CVPR'(2022-2024), AAAI'(2022-2023), and IEEE CAI'2024.

News

2024-01 I obtain the Excellent Graduate of Beijing 2024.
2024-01 I obtain the Baidu Scholarship 2023 (10 people worldwide).
2023-09 Two first-authored paper (one spotlight) for model quantization is accepted by NeurIPS 2023.
2023-09 I obtain China National Scholarship (3rd time).
2023-06 We release our open source project "Awesome Efficient AIGC".
2023-05 I am named one of the CVPR 2023 Outstanding Reviewers.
2023-04 One first-authored paper for data-free quantization is accepted by IEEE TPAMI.
2023-04 One first-authored paper for model binarization benchmark is accepted by ICML 2023.
2023-04 I obtain the DAAD AInet Fellowship 2023.
2023-02 One first-authored paper for FSMN binarization is accepted by IEEE TNNLS.
2022-12 I am selected for the Rising Stars in AI Symposium 2023 organized by KAUST AI initiative.
2022-10 I obtain the ByteDance Scholarship 2022.
2022-10 Our BiPointNet (ICLR'21) integrated into Amazon's Deep Graph Library (DGL).
2022-09 One first-authored paper for model binarization is accepted by IJCV.
2022-06 One co-authored paper for ViT quantization is accepted by ACM MM 2022.
2022-06 I obtain the Beihang Top-10 PhD Students Award 2022.
2022-05 Our BiBERT (ICLR'22) integrated into Baidu's deep learning platform PaddlePaddle.
2022-04 One first-authored paper for FSMN binarization is accepted by IJCAI 2022.
2022-03 Our survey paper for binary neural networks is selected to ESI Highly Cited Papers
2022-01 One first-authored paper for BERT binarization is accepted by ICLR 2022.
2021-09 Our BiPointNet (ICLR'21) obtain the Most Popular Paper in Beijing Area.
2021-09 I obtain China National Scholarship 2021 (2nd time).
2021-05 I obtain Huawei Scholarship 2021.
2021-03 One co-first-authored oral paper for data-free quantization is accepted by CVPR 2021.
2021-01 One first-authored paper for PointNet binarization is accepted by ICLR 2021.
2020-09 I obtain China National Scholarship 2020.
2020-09 We release our open source project "Awesome Model Quantization".
2020-02 One first-authored paper for model binarization is accepted by CVPR 2020.
2020-02 One first-authored survey paper for binary neural networks is accepted by PR.

Selected Publications

  1. Accurate LoRA-Finetuning Quantization of LLMs via Information Retention
    Haotong Qin, Xudong Ma, Xingyu Zheng, Xiaoyang Li, Yang Zhang, Shouda Liu, Jie Luo, Xianglong Liu, Michele Magno
    In arXiv preprint arXiv:2402.05445 (arXiv), 2024
  2. BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
    Wei Huang, Yangdong Liu, Haotong Qin*, Ying Li, Shiming Zhang, Xianglong Liu, Michele Magno, Xiaojuan Qi
    In arXiv preprint arXiv:2402.04291 (arXiv), 2024
  3. OHQ: On-chip Hardware-aware Quantization
    Wei Huang, Haotong Qin, Yangdong Liu, Jingzhuo Liang, Yulun Zhang, Ying Li, Xianglong Liu
    In arXiv preprint arXiv:2309.01945 (arXiv), 2024
  4. BiBench: Benchmarking and Analyzing Network Binarization
    Haotong Qin, Mingyuan Zhang, Yifu Ding, Aoyu Li, Zhongang Cai, Ziwei Liu, Fisher Yu, Xianglong Liu
    In International Conference on Machine Learning (ICML), 2023
  5. QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution
    Haotong Qin, Yulun Zhang, Yifu Ding, Yifan Liu, Xianglong Liu, Martin Danelljan, Fisher Yu
    In Conference on Neural Information Processing Systems (NeurIPS Spotlight), 2023
  6. BiMatting: Efficient Video Matting via Binarization
    Haotong Qin, Lei Ke, Xudong Ma, Martin Danelljan, Yu-Wing Tai, Chi-Keung Tang, Xianglong Liu, Fisher Yu
    In Conference on Neural Information Processing Systems (NeurIPS), 2023
  7. BiBERT: Accurate Fully Binarized BERT
    Haotong Qin, Yifu Ding, Mingyuan Zhang, Qinghua Yan, Aishan Liu, Qingqing Dang, Ziwei Liu, Xianglong Liu
    In International Conference on Learning Representations (ICLR), 2022
  8. BiFSMN: Binary Neural Network for Keyword Spotting
    Haotong Qin, Xudong Ma, Yifu Ding, Xiaoyang Li, Yang Zhang, Yao Tian, Zejun Ma, Jie Luo, Xianglong Liu
    In International Joint Conference on Artificial Intelligence (IJCAI), 2022
  9. BiPointNet: Binary Neural Network for Point Clouds
    Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Liu, Hao Su
    In International Conference on Learning Representations (ICLR), 2021
  10. Diversifying Sample Generation for Accurate Data-Free Quantization
    Xiangguo Zhang, Haotong Qin, Yifu Ding, Ruihao Gong, Qinghua Yan, Renshuai Tao, Yuhang Li, Fengwei Yu, Xianglong Liu
    In Computer Vision and Pattern Recognition (CVPR Oral), 2021
  11. Forward and Backward Information Retention for Accurate Binary Neural Networks
    Haotong Qin, Ruihao Gong, Xianglong Liu, Mingzhu Shen, Ziran Wei, Fengwei Yu, Jingkuan Song
    In Computer Vision and Pattern Recognition (CVPR), 2020
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