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Found 2 papers, 1 papers with code
Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection
We address this problem using Graph Neural Networks (GNNs) by proposing a dynamic relation-attentive aggregation mechanism.
Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion
In this work, we propose a novel reinforcement learning framework for training neural network controllers for complex robotic systems consisting of both rewards and constraints.
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