7 citations · 7 across the 2 of their papers we have counts for
3 papers
cs.LG2021
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach
Cheng-Yu Hsieh, Wei-I Lin, Miao Xu +3
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts. Previous works of MLL mainly focused on the setting where t…
cs.CL2020★ 7 cited
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning
Yufan Jiang, Shuangzhi Wu, Jing Gong +5
Multi-choice Machine Reading Comprehension (MMRC) aims to select the correct answer from a set of options based on a given passage and question. Due to task specific of MMRC, it is…
cs.DC2020
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Shaohuai Shi, Xianhao Zhou, Shutao Song +21
Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters. However, on public cloud clusters, due to the m…