43 citations · 100 across the 7 of their papers we have counts for
7 papers
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Hongwei Bran Li, Fernando Navarro, Ivan Ezhov +77
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a sign…
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
Wei Duan, Junyu Xuan, Maoying Qiao +1
Graph convolutional networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs general…
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
Wei Duan, Junyu Xuan, Maoying Qiao +1
Graph Convolutional Neural Networks (GCNs) has been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think…
Repulsive Mixture Models of Exponential Family PCA for Clustering
Maoying Qiao, Tongliang Liu, Jun Yu +2
The mixture extension of exponential family principal component analysis (EPCA) was designed to encode much more structural information about data distribution than the traditional…
Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approach
Maoying Qiao, Jun Yu, Wei Bian +1
Community is a common characteristic of networks including social networks, biological networks, computer and information networks, to name a few. Community detection is a basic st…
Adapting Stochastic Block Models to Power-Law Degree Distributions
Maoying Qiao, Jun Yu, Wei Bian +2
Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex fe…
Diversified Hidden Markov Models for Sequential Labeling
Maoying Qiao, Wei Bian, Richard Yida Xu +1
Labeling of sequential data is a prevalent meta-problem for a wide range of real world applications. While the first-order Hidden Markov Models (HMM) provides a fundamental approac…