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Found 5 papers, 3 papers with code
Graph Augmentation for Recommendation
To address these challenges, we propose a principled framework called GraphAug.
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
In addition, we introduce a cross-view contrastive learning paradigm to model the inter-dependencies across view-specific region representations and preserve underlying relation heterogeneity.
Automated Spatio-Temporal Graph Contrastive Learning
In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources.
Exploring the Correlation between Likelihood of Flow-based Generative Models and Image Semantics
In this paper, we explore the correlation between flows' likelihood and image semantics.
Reject Illegal Inputs: Scaling Generative Classifiers with Supervised Deep Infomax
The supervised probabilistic constraints are equivalent to a generative classifier on high-level data representations, where class conditional log-likelihoods of samples can be evaluated.
