Hashin-Shtrikman bounds under-predict taste in 77% of cases; a hybrid model with eight chemistry proxies reduces error 27-62% and enables constrained inverse design of recipes.
Visualizing data using t-SNE.Journal of Machine Learning Research, 9:2579–2605
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Predicting food taste with bound-driven optimization
Hashin-Shtrikman bounds under-predict taste in 77% of cases; a hybrid model with eight chemistry proxies reduces error 27-62% and enables constrained inverse design of recipes.