<ul><li>Learning systems often need to produce accurate predictions in specific subsets of a domain, while accuracy in other regions may be less critical.</li><li>Selective matching loss functions are designed with increasing link functions over score domains, emphasizing high sensitivity regions.</li><li>Loss asymmetry in these functions helps models predict better in high sensitivity regions, distinguishing between regions of high and low importance.</li><li>Multiplying selective scalar losses with composite Softmax functions allows for multidimensional selective losses, providing advantages in various applications.</li></ul>

Selective Matching Losses -- Not All Scores Are Created Equal

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