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Published in International Conference on Artificial Intelligence and Statistics, 2021
Focus on the long-term memory and stability of tensor recurrent model with developing a degree-differentiable model benefit from long-term effect in a stable manner, cooperate with RIKEN AIP Tensor Learning Team.
Recommended citation: Qiu, H., Li, C., Weng, Y., Sun, Z., He, X. and Zhao, Q., 2021, March. On the Memory Mechanism of Tensor-Power Recurrent Models. In International Conference on Artificial Intelligence and Statistics (pp. 3682-3690). PMLR. http://proceedings.mlr.press/v130/qiu21a.html
Published in IEEE Transactions on Neural Networks and Learning Systems, 2023
Our new model, named fractional tensor recurrent unit (fTRU), is expected to seek the saddle point between long memory property and model stability during the training. We experimentally show that the proposed model achieves competitive performance with a long memory and stable manners in several forecasting tasks compared to various advanced RNNs.
Recommended citation: H. Qiu, C. Li, Y. Weng, Z. Sun and Q. Zhao, "Fractional Tensor Recurrent Unit (fTRU): A Stable Forecasting Model With Long Memory," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3338696. https://ieeexplore.ieee.org/document/10361837
Published in 2025 8th International Conference on Big Data and Artificial Intelligence (BDAI), 2025
In our study, we apply various machine learning methods on multi-source breast cancer big data for feature analysis and prognosis evaluation.
Recommended citation: H. Qiu and Y. Weng, "Machine Learning based Breast Cancer Prognosis Analysis on Multi-Source Big Data," 2025 8th International Conference on Big Data and Artificial Intelligence (BDAI), Taicang, China, 2025, pp. 278-283, doi: 10.1109/BDAI66031.2025.11325245. https://ieeexplore.ieee.org/abstract/document/11325245/
Published in The 39th IEEE International Symposium on Computer-Based Medical Systems (CBMS-2026), 2026
Q2VA has the potential to provide a robust, efficient, and population-inclusive solution for high-precision visual acuity monitoring in eye clinics.
Recommended citation: Tao, H., Liang, X., Wang, J., Xu, B., Qiu, H., & Xu, P. (Accepted/In press). Q2VA: A Bayesian Adaptive Method for Visual Acuity Assessment with Tumbling E Stimuli. In The 39th IEEE International Symposium on Computer-Based Medical Systems (CBMS-2026) https://scholar.xjtlu.edu.cn/en/publications/q2va-a-bayesian-adaptive-method-for-visual-acuity-assessment-with/
Published in The 9th IEEE International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026), 2026
EfficientNetV2 for submersible pump impeller product anomaly detection.
Recommended citation: Liu, Y., Xu, P., Gan, H. S., & Qiu, H. (Accepted/In press). Submersible Pump Impeller Product Anomaly Detection Using an Advanced Deep Learning Model ─ EfficientNetV2. In The 9th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026) IEEE. https://scholar.xjtlu.edu.cn/en/publications/submersible-pump-impeller-product-anomaly-detection-using-an-adva/
Published in The 9th IEEE International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026), 2026
Tansformer-based solutions for high-precision forward kinematics of industrial manipulators
Recommended citation: You, X., Liu, Y., Xu, P., & Qiu, H. (Accepted/In press). Benchmarking Dimension-Inverted Transformers for High-Precision Forward Kinematics of Industrial Manipulators. In The 9th IEEE International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2026) IEEE. https://scholar.xjtlu.edu.cn/en/publications/benchmarking-dimension-inverted-transformers-for-high-precision-f/
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Undergraduate course, University of Nottingham Ningbo China, School of Computer Science, 2020
Be undergraduate teaching assistant (GTA) in Machine Learning.
Undergraduate course, University of Nottingham Ningbo China, School of Computer Science, 2021
Be undergraduate teaching assistant (GTA) in System & Architectures.
Undergraduate course, University of Nottingham Ningbo China, School of Computer Science, 2021
Be undergraduate teaching assistant (GTA) in Machine Learning.
Undergraduate course, University of Nottingham Ningbo China, School of Computer Science, 2022
Be undergraduate teaching assistant (GTA) in System & Architectures.
Undergraduate course, University of Nottingham Ningbo China, School of Computer Science, 2022
Be undergraduate teaching assistant (GTA) in Machine Learning.
Undergraduate course, University of Nottingham Ningbo China, School of Computer Science, 2023
Be undergraduate teaching assistant (GTA) in System & Architectures.
Undergraduate course, University of Nottingham Ningbo China, School of Computer Science, 2024
Be undergraduate teaching assistant (GTA) in System & Architectures.
Undergraduate course, Xi'an Jiaotong-Liverpool University, School of AI and Advanced Computing, 2024
Be co-teacher in Database Development and Design.
Undergraduate course, Xi'an Jiaotong-Liverpool University, School of AI and Advanced Computing, 2025
Be co-teacher in Design and Analysis of Algorithms.
Postgraduate course, Xi'an Jiaotong-Liverpool University, School of AI and Advanced Computing, 2025
Be module leader in Big Data Mining, Analysis, and Management.
Undergraduate course, Xi'an Jiaotong-Liverpool University, School of AI and Advanced Computing, 2025
Be co-teacher in Database Development and Design.
Undergraduate course, Xi'an Jiaotong-Liverpool University, School of AI and Advanced Computing, 2025
Be co-teacher in Pattern Recognition and Computer Vision.
Postgraduate course, Xi'an Jiaotong-Liverpool University, School of AI and Advanced Computing, 2026
Be module leader in Big Data Mining, Analysis, and Management.
Postgraduate course, Xi'an Jiaotong-Liverpool University, School of AI and Advanced Computing, 2026
Be co-teacher in Computer Architecture and Operating Systems.
Undergraduate course, Xi'an Jiaotong-Liverpool University, School of AI and Advanced Computing, 2026
Be module leader in Optimisations.