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Cross entropy9/1/2023 ![]() ![]() State-of-the-art, while also achieving a superior non-adversarial accuracy. The cross-entropy (CE) method is a new generic approach to combinatorial and multi-extremal optimization and rare event simulation. We further report the results of a series of experimentsĭemonstrating that our adversarial robustness algorithms outperform the current ![]() nbro Jat 8:20 am You wrote therefore have more information those events that are common, but you mean therefore have more information THAN those events that are common. Theoretical analysis, we also present an extensive empirical analysis comparingĬomp-sum losses. In machine learning we often use cross-entropy and information gain, which require an understanding of entropy as a foundation. Nh ã mình ã nh ngha trên, entropy là kích thc mã hóa trung bình ti thiu theo lý thuyt cho các s kin tuân theo mt phân phi xác sut c th. Regularized smooth adversarial comp-sum loss. Leads to new adversarial robustness algorithms that consist of minimizing a We show that these loss functions are beneficial in theĪdversarial setting by proving that they admit $H$-consistency bounds. We also introduce a new family of loss functions, smooth adversarialĬomp-sum losses, that are derived from their comp-sum counterparts by adding inĪ related smooth term. Make them more explicit, we give a specific analysis of these gaps for comp-sum These bounds depend on quantities called minimizability gaps. We further show that our boundsĪre tight. Loss, for the specific hypothesis set $H$ used. Zero-one loss estimation error in terms of the estimation error of a surrogate These are non-asymptotic guarantees that upper bound the We give the first $H$-consistency bounds for If the hummingbird element is 1, which means spot-on correct classification, then the cross entropy loss for that classification is zero. To recap: Take the important element of a classifier’s outputwhat we called the hummingbird element. Loss), generalized cross-entropy, the mean absolute error and otherĬross-entropy-like loss functions. So, that’s the cross entropy loss in a nutshell. Loss functions, comp-sum losses, that includes cross-entropy (or logistic But, what guarantees can we rely on when using cross-entropyĪs a surrogate loss? We present a theoretical analysis of a broad family of With the logistic loss applied to the outputs of a neural network, when the Download a PDF of the paper titled Cross-Entropy Loss Functions: Theoretical Analysis and Applications, by Anqi Mao and 2 other authors Download PDF Abstract: Cross-entropy is a widely used loss function in applications. ![]()
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