no code implementations • 27 Mar 2024 • Guangzai Ye, Li Feng, Jianlan Guo, Yuqiang Chen
Notably, to address the varying importance of features in RUL prediction, we introduce a weighted loss function in the MLP-Mixer-based architecture, marking the first time such an approach has been employed.
no code implementations • 20 May 2020 • Yuanfei Luo, Hao Zhou, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang
As a result, the intra-field information and the non-linear interactions between those operations (e. g. neural network and factorization machines) are ignored.
no code implementations • 29 Apr 2019 • Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Wei-Wei Tu, Yuqiang Chen, Qiang Yang, Wenyuan Dai
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses.
no code implementations • 12 Mar 2019 • Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L. Silver, Evelyne Viegas, Yuqiang Chen, Wenyuan Dai, Qiang Yang
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018.
no code implementations • 23 Nov 2018 • Quanming Yao, Xiawei Guo, James T. Kwok, WeiWei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang
To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms.