1 code implementation • 16 Aug 2024 • Huaiyuan Liu, Xianzhang Liu, Donghua Yang, Hongzhi Wang, Yingchi Long, Mengtong Ji, Dongjing Miao, Zhiyu Liang
The unsupervised solver is inspired by a relaxation-plus-rounding approach, the relaxed solution is parameterized by graph neural networks, and the cost and penalty of MMCP are explicitly written out, which can train the model end-to-end.
no code implementations • 9 Dec 2023 • Chen Liang, Donghua Yang, Zhiyu Liang, Hongzhi Wang, Zheng Liang, Xiyang Zhang, Jianfeng Huang
In contrast to conventional methods that fuse features from multiple modalities, our proposed approach simplifies the neural architecture by retaining a single time series encoder, consequently leading to preserved scalability.
1 code implementation • 25 Jul 2023 • Kaixin Zhang, Hongzhi Wang, Yabin Lu, ZiQi Li, Chang Shu, Yu Yan, Donghua Yang
Although both data-driven and hybrid methods are proposed to avoid this problem, most of them suffer from high training and estimation costs, limited scalability, instability, and long-tail distribution problems on high-dimensional tables, which seriously affects the practical application of learned cardinality estimators.
1 code implementation • 11 Apr 2023 • Huaiyuan Liu, Xianzhang Liu, Donghua Yang, Zhiyu Liang, Hongzhi Wang, Yong Cui, Jun Gu
Unfortunately, the existing deep learning-based methods neglect the hidden dependencies in different dimensions and also rarely consider the unique dynamic features of time series, which lack sufficient feature extraction capability to obtain satisfactory classification accuracy.