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Graph clustering is a fundamental task in data analysis, aiming at grouping nodes with similar characteristics in the graph into clusters. This problem has been widely explored using graph neural networks (GNNs) due to their ability to leverage node attributes and graph topology for effective cluster assignments. Howev...
Multimodal representation learning seeks shared representations for cross-modal retrieval and knowledge transfer. Hub-based binding reduces pairwise supervision costs, but separate hub connections cannot guarantee reliable alignment between modalities without direct joint training. We introduce Hub-Spectral Activation...
CSN is proposed, which enables efficient Communication with Skill Neurons in decentralized MARL by exchanging the essential components of learned knowledge at neuron level by communicating only a sparse subset of model parameters and doing so intermittently.
A Bilevel Cycle Generative Adversarial Network (BCGAN) is formed that comprises two generative adversarial networks and a specific manifold fitting module that establishes upper bounds on the generalization error for stochastic bilevel minimax problems, revealing the relationship between generalization capability and p...
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