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Jun Shu currently works at the School of Mathematics and Statistics, Xi'an Jiaotong University. Jun does research in Statistics and Applied Mathematics. Their most recent publication is...
SHU Jun, M Deyu, XU Zongben. Scientia Sinica Informationis 50 (6), 781-793, 2020. 15 * 2020: Learning an explicit hyperparameter prediction function conditioned on tasks. J Shu, D Meng, Z Xu. Journal of Machine Learning Research, 2023. 11 * 2023: MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks.
11 de feb. de 2022 · CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning. Jun Shu, Xiang Yuan, Deyu Meng, Zongben Xu. Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue.
xjtushujun / meta-weight-net Public. Notifications. Fork 66. Star 275. master. README. MIT license. Meta-Weight-Net. NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Official Pytorch implementation for noisy labels).
6 de jul. de 2021 · Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks. Jun Shu, Deyu Meng, Zongben Xu. Meta learning has attracted much attention recently in machine learning community. Contrary to conventional machine learning aiming to learn inherent prediction rules to predict labels for new query data, meta learning aims ...
Biography. Jun Shu received the BE degree from Xi’an Jiaotong University, Xi’an, China, in 2016, where he is currently working toward the PhD degree, under the tuition of Prof. Deyu Meng and Prof. Zongben Xu. His current research interests include machine learning and computer vision, especially on meta learning, robust deep learning, and ...
Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks. Jun Shu, Deyu Meng, Zongben Xu; 24 (186):1−74, 2023. Abstract. Meta learning has attracted much attention recently in machine learning community.
