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Institute University of Technology Sydney
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Treating missing data as a contextual signal, MARCUS dramatically improves rent prediction accuracy, slashing MAE by over 51% in some cases.
Action-conditioned verification can boost success rates in mobile manipulation tasks by over 8% while enhancing timely recall by nearly 30%.
A unified Transformer model achieves unprecedented fault tolerance in FBG sensors, outperforming traditional methods while simplifying calibration processes.
Federated learning can overcome data sparsity and privacy concerns to improve livestock growth prediction using real-world farm data.
