• In information theory, the cross-entropy between two probability distributions p {\displaystyle p} and q {\displaystyle q} , over the same underlying...
    19 KB (3,247 words) - 09:04, 20 October 2024
  • Thumbnail for Entropy (information theory)
    In information theory, the entropy of a random variable quantifies the average level of uncertainty or information associated with the variable's potential...
    70 KB (10,018 words) - 00:46, 4 October 2024
  • The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorial and continuous...
    7 KB (1,082 words) - 14:36, 13 July 2024
  • statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence), denoted D KL ( P ∥ Q ) {\displaystyle D_{\text{KL}}(P\parallel...
    73 KB (12,534 words) - 13:54, 28 October 2024
  • correlation for regression tasks or using information measures such as cross entropy for classification tasks. Theoretically, one can justify the diversity...
    52 KB (6,574 words) - 06:47, 2 November 2024
  • entropy states that the probability distribution which best represents the current state of knowledge about a system is the one with largest entropy,...
    31 KB (4,196 words) - 13:25, 2 November 2024
  • Perplexity (category Entropy and information)
    {1}{N}}\sum _{i=1}^{N}\log _{b}q(x_{i})} may also be interpreted as a cross-entropy: H ( p ~ , q ) = − ∑ x p ~ ( x ) log b ⁡ q ( x ) {\displaystyle H({\tilde...
    13 KB (1,859 words) - 09:03, 20 October 2024
  • In physics, the Tsallis entropy is a generalization of the standard Boltzmann–Gibbs entropy. It is proportional to the expectation of the q-logarithm...
    22 KB (2,563 words) - 17:47, 6 March 2024
  • the relationship between maximizing the likelihood and minimizing the cross-entropy, URL (version: 2019-11-06): https://stats.stackexchange.com/q/364237...
    67 KB (9,707 words) - 16:01, 1 November 2024
  • of the factors’ logarithms and flipping the sign yields the classic cross-entropy loss: θ ∗ = a r g m i n θ − ∑ i T log ⁡ ∑ j = 1 J ( i ) P ( y j ( i...
    36 KB (3,910 words) - 05:39, 9 October 2024