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DanceOPD reveals a novel approach to harmonizing conflicting image generation capabilities, enhancing T2I and editing performance simultaneously.
Unsupervised graph anomaly detection is now possible across diverse graphs, achieving zero-shot generalization without relying on labeled data or few-shot examples.
Uncover tax evasion rings with a novel graph neural network that leverages related party transaction data to significantly outperform existing detection methods.
Forget complex sequence models: this new method efficiently captures temporal dynamics in graphs by contrasting node representations across different timespans.
