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Found 6 papers, 4 papers with code
Graph Fairness Learning under Distribution Shifts
Recently, there has been an increasing interest in ensuring fairness on GNNs, but all of them are under the assumption that the training and testing data are under the same distribution, i. e., training data and testing data are from the same graph.
Uncovering the Structural Fairness in Graph Contrastive Learning
Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed degree distributions prevalent in the real world.
Combining Deep Learning with Physics Based Features in Explosion-Earthquake Discrimination
This paper combines the power of deep-learning with the generalizability of physics-based features, to present an advanced method for seismic discrimination between earthquakes and explosions.
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision
We study the open-domain named entity recognition (NER) problem under distant supervision.
Multi-Component Graph Convolutional Collaborative Filtering
The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph.
A Fast Proximal Point Method for Computing Exact Wasserstein Distance
However, as we will demonstrate, regularized variations with large regularization parameter will degradate the performance in several important machine learning applications, and small regularization parameter will fail due to numerical stability issues with existing algorithms.
