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Found 3 papers, 0 papers with code
Mixed Supervised Graph Contrastive Learning for Recommendation
Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentation on the user-item bipartite graph, which predominantly relies on the multi-task learning framework involving both the pair-wise recommendation loss and the contrastive loss.
Graph Neural Ordinary Differential Equations-based method for Collaborative Filtering
This inspired us to address the computational limitations of GCN-based models by designing a simple and efficient NODE-based model that can skip some GCN layers to reach the final state, thus avoiding the need to create many layers.
Automatic Streaming Segmentation of Stereo Video Using Bilateral Space
In this paper, we take advantage of binocular camera and propose an unsupervised algorithm based on semi-supervised segmentation algorithm and extracting foreground part efficiently.
