213 citations · 239 across the 8 of their papers we have counts for
7 papers
Personalized Federated Learning via Heterogeneous Modular Networks
Tianchun Wang, Wei Cheng, Dongsheng Luo +5
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite…
SEED: Sound Event Early Detection via Evidential Uncertainty
Xujiang Zhao, Xuchao Zhang, Wei Cheng +4
Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes. However, most of the existing methods focus on the offline sound e…
Unsupervised Document Embedding via Contrastive Augmentation
Dongsheng Luo, Wei Cheng, Jingchao Ni +8
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner. Inspired by recent contrastive self-superv…
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts
Junheng Hao, Muhao Chen, Wenchao Yu +2
Many large-scale knowledge bases simultaneously represent two views of knowledge graphs (KGs): an ontology view for abstract and commonsense concepts, and an instance view for spec…
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu +4
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The key idea is to recursively propagate and aggregate information along edges of the given graph.…
Parameterized Explainer for Graph Neural Network
Dongsheng Luo, Wei Cheng, Dongkuan Xu +4
Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method independently addresses the loca…