no code implementations • 16 Oct 2023 • Xin Bing, Dian Jin, Yuqian Zhang
Vintage factor analysis is one important type of factor analysis that aims to first find a low-dimensional representation of the original data, and then to seek a rotation such that the rotated low-dimensional representation is scientifically meaningful.
1 code implementation • 2 Jun 2023 • Long Ma, Dian Jin, Nan An, JinYuan Liu, Xin Fan, Risheng Liu
A bilevel learning framework is constructed to endow the scene-irrelevant generality of the encoder towards diverse scenes (i. e., freezing the encoder in the adaptation and testing phases).
1 code implementation • 11 May 2023 • Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie zhou
We recruit annotators to search for relevant information using our interface and then answer questions.
1 code implementation • 9 Nov 2021 • Yuzhe Gao, Xing Li, Jiajian Zhang, Yu Zhou, Dian Jin, Jing Wang, Shenggao Zhu, Xiang Bai
We leverage a Siamese ComplementaryModule to fully exploit the continuity characteristic of the textinstances in the temporal dimension, which effectively alleviatesthe missed detection of the text instances, and hence ensuresthe completeness of each text trajectory.
1 code implementation • NeurIPS 2021 • Dian Jin, Xin Bing, Yuqian Zhang
In this paper, we study the problem of seeking a unique decomposition of a low rank matrix $Y\in \mathbb{R}^{p\times n}$ that admits a sparse representation.