In information theory, the cross-entropy between two probability distributions p {\displaystyle p} and q {\displaystyle q} , over the same underlying...

statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence), denoted D KL ( P ∥ Q ) {\displaystyle D_{\text{KL}}(P\parallel...

In information theory, the entropy of a random variable quantifies the average level of uncertainty or information associated with the variable's potential...

entropy states that the probability distribution which best represents the current state of knowledge about a system is the one with largest entropy,...

correlation for regression tasks or using information measures such as cross entropy for classification tasks. Theoretically, one can justify the diversity...

In physics, the Tsallis entropy is a generalization of the standard Boltzmann–Gibbs entropy. It is proportional to the expectation of the q-logarithm...

The cross-entropy (CE) method is a Monte Carlo method for importance sampling and optimization. It is applicable to both combinatorial and continuous...

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...

binary cross-entropy compares the observed y ∈ { 0 , 1 } {\displaystyle y\in \{0,1\}} with the predicted probabilities. The average binary cross-entropy for...

_{p}\left({\frac {n}{2}}\right)+{\frac {np}{2}}\end{aligned}}} The cross-entropy of two Wishart distributions p 0 {\displaystyle p_{0}} with parameters...

Cross-entropy benchmarking (also referred to as XEB) is a quantum benchmarking protocol which can be used to demonstrate quantum supremacy. In XEB, a...

the relationship between maximizing the likelihood and minimizing the cross-entropy, URL (version: 2019-11-06): https://stats.stackexchange.com/q/364237...

The ORM is usually trained via logistic regression, i.e. minimizing cross-entropy loss. Given a PRM, an ORM can be constructed by multiplying the total...

In statistics and information theory, a maximum entropy probability distribution has entropy that is at least as great as that of all other members of...

is different than the data set used to train the large model) using cross-entropy as the loss function between the output of the distilled model y ( x...

evaluation and comparison of language models, cross-entropy is generally the preferred metric over entropy. The underlying principle is that a lower BPW...

{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...

expression is identical to the negative of the cross-entropy (see section on "Quantities of information (entropy)"). Therefore, finding the maximum of the...

The cross-entropy (CE) method generates candidate solutions via a parameterized probability distribution. The parameters are updated via cross-entropy minimization...

interpreted geometrically by using entropy to measure variation: the MLE minimizes cross-entropy (equivalently, relative entropy, Kullback–Leibler divergence)...

loss function or "cost function" For classification, this is usually cross-entropy (XC, log loss), while for regression it is usually squared error loss...

conditional entropy conditional quantum entropy confusion and diffusion cross-entropy data compression entropic uncertainty (Hirchman uncertainty) entropy encoding...

The cross-entropy method (CE) generates candidate solutions via a parameterized probability distribution. The parameters are updated via cross-entropy minimization...

is trained by gradient descent to minimize the cross-entropy loss. In full formula, the cross-entropy loss is: − ∑ i ln ⁡ e v w i ′ ⋅ ( ∑ j ∈ i + N v...

the mean squared error criterion implemented in MSECriterion and the cross-entropy criterion implemented in ClassNLLCriterion. What follows is an example...

regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier, and the conditional maximum entropy model. Multinomial logistic regression is used...

Battiti, G. Tecchiolli (1994), recently reviewed in the reference book cross-entropy method by Rubinstein and Kroese (2004) random search by Anatoly Zhigljavsky...

perform biproportion. We have also the entropy maximization, information loss minimization (or cross-entropy) or RAS which consists of factoring the...

supervised model. In particular, it is trained to minimize the following cross-entropy loss function: L ( θ ) = − 1 ( K 2 ) E ( x , y w , y l ) [ log ⁡ ( σ...

y)\,} Despite similar notation, joint entropy should not be confused with cross-entropy. The conditional entropy or conditional uncertainty of X given...