33.1k citations · 35.3k across the 95 of their papers we have counts for
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Graph Barlow Twins: A self-supervised representation learning framework for graphs
Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla
The self-supervised learning (SSL) paradigm is an essential exploration area, which tries to eliminate the need for expensive data labeling. Despite the great success of SSL method…
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla
Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have ma…
Few-Shot Graph Learning for Molecular Property Prediction
Zhichun Guo, Chuxu Zhang, Wenhao Yu +4
The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery. The existing deep neural network m…