791 citations · 963 across the 16 of their papers we have counts for
16 papers
Distilling Ensemble of Explanations for Weakly-Supervised Pre-Training of Image Segmentation Models
Xuhong Li, Haoyi Xiong, Yi Liu +4
While fine-tuning pre-trained networks has become a popular way to train image segmentation models, such backbone networks for image segmentation are frequently pre-trained using i…
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification
Yaqing Wang, Song Wang, Quanming Yao +1
Short text classification is a fundamental task in natural language processing. It is hard due to the lack of context information and labeled data in practice. In this paper, we pr…
Exploring the Common Principal Subspace of Deep Features in Neural Networks
Haoran Liu, Haoyi Xiong, Yaqing Wang +3
We find that different Deep Neural Networks (DNNs) trained with the same dataset share a common principal subspace in latent spaces, no matter in which architectures (e.g., Convolu…
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework
Yaqing Wang, Haoda Chu, Chao Zhang +1
In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings. Built upon large-scale pre-trained lan…
FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
Zewei Long, Jiaqi Wang, Yaqing Wang +2
Federated Semi-Supervised Learning (FedSSL) has gained rising attention from both academic and industrial researchers, due to its unique characteristics of co-training machine lear…
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning
Liwei Che, Zewei Long, Jiaqi Wang +3
Federated Learning has shown great potentials for the distributed data utilization and privacy protection. Most existing federated learning approaches focus on the supervised setti…