33.1k citations · 35.3k across the 95 of their papers we have counts for
18 papers · 1 filter
Interpretable ML for Imbalanced Data
Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk +2
Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compou…
FakeEdge: Alleviate Dataset Shift in Link Prediction
Kaiwen Dong, Yijun Tian, Zhichun Guo +2
Link prediction is a crucial problem in graph-structured data. Due to the recent success of graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the…
Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control
Tânia Carvalho, Nuno Moniz, Luís Antunes +1
Protecting user data privacy can be achieved via many methods, from statistical transformations to generative models. However, all of them have critical drawbacks. For example, cre…
Understanding CNN Fragility When Learning With Imbalanced Data
Damien Dablain, Kristen N. Jacobson, Colin Bellinger +2
Convolutional neural networks (CNNs) have achieved impressive results on imbalanced image data, but they still have difficulty generalizing to minority classes and their decisions…
Boosting Graph Neural Networks via Adaptive Knowledge Distillation
Zhichun Guo, Chunhui Zhang, Yujie Fan +3
Graph neural networks (GNNs) have shown remarkable performance on diverse graph mining tasks. Although different GNNs can be unified as the same message passing framework, they lea…
Linkless Link Prediction via Relational Distillation
Zhichun Guo, William Shiao, Shichang Zhang +4
Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction. Despite their effectiveness, the high latency brought by non-trivial neighborhood da…