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Found 4 papers, 3 papers with code
ByteCover3: Accurate Cover Song Identification on Short Queries
Deep learning based methods have become a paradigm for cover song identification (CSI) in recent years, where the ByteCover systems have achieved state-of-the-art results on all the mainstream datasets of CSI.
Graph Contrastive Learning with Implicit Augmentations
Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e. g., randomly adding or dropping edges and nodes).
Wasserstein Adversarially Regularized Graph Autoencoder
This paper introduces Wasserstein Adversarially Regularized Graph Autoencoder (WARGA), an implicit generative algorithm that directly regularizes the latent distribution of node embedding to a target distribution via the Wasserstein metric.
How Neural Processes Improve Graph Link Prediction
Link prediction is a fundamental problem in graph data analysis.
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