most citedHERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization

3 citations · 3 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2019★ 3 cited

HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization

Gengyu Lyu, Songhe Feng, Yi Jin +3

Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing m…

cs.NI2015

Quorum-based Localized Scheme for Duty Cycling in Asynchronous Sensor Networks

Jianhui Zhang, Shao-jie Tang, Xingfa Shen +2

Many TDMA- and CSMA-based protocols try to obtain fair channel access and to increase channel utilization. It is still challenging and crucial in Wireless Sensor Networks (WSNs), e…