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Dude achieves up to 22.8% better recall in paper-code discrepancy detection, tackling the critical challenge of false positives in multi-agent systems.
Client-specific sufficiency estimation in FedSGA allows for more efficient federated learning, boosting performance while slashing unnecessary computation costs.
NTCF achieves higher performance by adapting propagation depth to local connectivity, revealing that traditional methods may overlook critical node-specific dynamics.
Achieving state-of-the-art scene completion and generation without relying on partial scans, FPSGen redefines how point clouds can be generated in challenging environments.
Long-tailed point cloud dataset distillation can boost classification accuracy by 7 points, challenging the limits of existing methods.
