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MOLAR proposes a noise-aware framework for learning multimodal molecular representations from noisy labels by separating clean-property inference from observed label noise, outperforming baselines on molecular benchmarks.
This paper studies symmetrization of loss functions for robust training under label noise, introducing SGCE and alpha-MAE loss functions that interpolate between multi-class unhinged loss and Mean Absolute Error, with theoretical guarantees and competitive empirical performance.