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Found 5 papers, 1 papers with code
Smoothed Graph Contrastive Learning via Seamless Proximity Integration
Graph contrastive learning (GCL) aligns node representations by classifying node pairs into positives and negatives using a selection process that typically relies on establishing correspondences within two augmented graphs.
TIDE: Time Derivative Diffusion for Deep Learning on Graphs
A prominent paradigm for graph neural networks is based on the message-passing framework.
Geometric Multimodal Deep Learning with Multi-Scaled Graph Wavelet Convolutional Network
Capturing the intra-modality and cross-modality information of multimodal data is the essential capability of multimodal learning methods.
Cross-Modal and Multimodal Data Analysis Based on Functional Mapping of Spectral Descriptors and Manifold Regularization
The second method is a manifold regularized multimodal classification based on pointwise correspondences (M$^2$CPC) used for the problem of multiclass classification of multimodal heterogeneous, which the correspondences between modalities are determined based on the FMBSD method.
Weighted Least Squares Twin Support Vector Machine with Fuzzy Rough Set Theory for Imbalanced Data Classification
In this work, we propose an approach that efficiently used fuzzy rough set theory in weighted least squares twin support vector machine called FRLSTSVM for classification of imbalanced data.
