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Double Neural Counterfactual Regret Minimization
We propose a double neural representation for the imperfect information games, where one neural network represents the cumulative regret, and the other represents the average strategy. Expand
AGL: A Scalable System for Industrial-purpose Graph Machine Learning
We design AGL, a scalable, fault-tolerance and integrated system, with fully-functional training and inference for GNNs. Expand
Machine learning over graphs has been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as graph neuralExpand