no code implementations • 25 Sep 2023 • Biao Liu, Jie Wang, Ning Xu, Xin Geng
Single-positive multi-label learning (SPMLL) is a typical weakly supervised multi-label learning problem, where each training example is annotated with only one positive label.
no code implementations • 1 Aug 2023 • Biao Liu, Congyu Qiao, Ning Xu, Xin Geng, Ziran Zhu, Jun Yang
In order to fully exploit the inherent spatial label-correlation between neighboring grids, we propose a novel approach, {\ours}, i. e., VAriational Label-Correlation Enhancement for Congestion Prediction, which considers the local label-correlation in the congestion map, associating the estimated congestion value of each grid with a local label-correlation weight influenced by its surrounding grids.
no code implementations • 2 Jun 2022 • Ning Xu, Biao Liu, Jiaqi Lv, Congyu Qiao, Xin Geng
Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is correct.
no code implementations • 11 Jun 2021 • Jiaqi Lv, Biao Liu, Lei Feng, Ning Xu, Miao Xu, Bo An, Gang Niu, Xin Geng, Masashi Sugiyama
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL).
no code implementations • 3 Mar 2015 • Biao Liu, Minlie Huang
Prior knowledge has been shown very useful to address many natural language processing tasks.