4. Mutual Information http://fourier.eng.hmc.edu/e176/lectures/probability/node6.html 5. Gibbs' Inequality H(P)=−∑iPilogPi≤CrossEntropy(P,Q)=−∑iPilogQi 6. Cross Entropy 7. KL divergence 其他link: pytorch 公式 nn.KLDivLoss() ...
This paper defines a corresponding concept of mutual information between two variables in the Dempster-Shafer (D-S) belief function theory using the decomposable entropy defined by Jirousek and Shenoy. We also define the Kullback-Liebler (KL) divergence for the D-S theory as similar to the KL...
The third type of transferability measurement methods are based on information entropy, including Mutual Information [45], Kullback–Leibler (KL) divergence [46], and Jensen–Shannon (JS) divergence [47] between the source domain and the target domain. For example, Ircio et al. [45] solved ...
Feature Selection Based on Mutual Information:Criteria of Max-Dependency, Max-Relevance,and Min-Redundancy 基于互信息的特征选择:最大依赖、最大相关、最小冗余准则 摘要 特征选择对分类系统有重要意义。本文根据互信息的‘最大统计依赖准则’选择好特征。引入最小冗余最大相关准... ...
有了KL-divergence的bound,作者分析了Langevin dynamics (LD) algorithm [4],并给出了更紧的界。 这里首先考虑一个general的迭代算法:初始值相同,经过T个iterations时KL散度的界,从而可以对上面的EGE进行约束。 这里W_{t|}代表给出W_0...W_{t-1},W_t的条件概率。 随后在LD 算法下得到泛化风险界。 实验...
For mutual information estimation, we draw inspiration from MINE [19]. We utilize a neural network to estimate the joint and marginal probability distributions of text at two text views. We transform the maximization of mutual information into the KL-divergence between the joint and marginal ...
The mutual information can be defined in terms of Kullback-Leibler divergence, as being the divergence between the joint distribution Pr [X = x, Y = y] and the product distribution Pr [X = x]· 436 N. Veyrat-Charvillon and F.-X. Standaert Pr [Y = y], or as the expected ...
The mutual information can also be calculated as the KL divergence between the joint probability distribution and the product of the marginal probabilities for each variable. If the variables are not independent, we can gain some idea of whether they are ‘close’ to being independent by considerin...
from the true target distribution to those predicted by your model. The mutual information of a joint distribution p(X,Y) is the KL-divergence between the joint distribution and the product of the marginal distributions or equivalently the difference in uncertainty of r.v X given that we know...
Mutual Information Regularized Offline RL 首先给出KL散度的两种下界表示: 本节说明,如何使用互信息进行优化,并以此集成TD3+BC CQL Mutual Information Regularization 给出互信息的定义如下:互信息值越大,则给定S后动作A的不确定性越小,能够反应offline dataset的行为策略信息。因此可以考虑使用互信息来作为正则化方式...