Affinity uncertainty-based hard negative mining in graph contrastive learning
Hard negative mining has shown effective in enhancing self-supervised contrastive learning
(CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL …
(CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL …
A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data
Spatially resolved transcriptomics data are being used in a revolutionary way to decipher the
spatial pattern of gene expression and the spatial architecture of cell types. Much work has …
spatial pattern of gene expression and the spatial architecture of cell types. Much work has …
A Multitask Dynamic Graph Attention Autoencoder for Imbalanced Multilabel Time Series Classification
Graph learning is widely applied to process various complex data structures (eg, time series)
in different domains. Due to multidimensional observations and the requirement for accurate …
in different domains. Due to multidimensional observations and the requirement for accurate …