AINeutralarXiv – CS AI · 10h ago6/10
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VOLTA: The Surprising Ineffectiveness of Auxiliary Losses for Calibrated Deep Learning
Researchers introduce VOLTA, a simplified deep learning approach for uncertainty quantification that outperforms ten established baselines including ensemble methods and MC Dropout. The method achieves superior calibration with expected calibration error of 0.010 and competitive accuracy across multiple datasets, suggesting that complex auxiliary losses may be unnecessary for reliable uncertainty estimation in safety-critical applications.