However, it is known that the KL\ndivergence is sensitive to outliers. On the other hand, quadratic MI (QMI) is a\nvariant of MI based on the $L_2$ distance which is more robust against outliers\nthan the KL divergence, and a computationally efficient method to estimate QMI\nfrom ...
4 # MSE LOSS + KL DIVERGENCE ---> 5 BCE = F.binary_cross_entropy(x_hat, x.view(-1, 784)) 6 KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) 7 # Normalise by same number of elements as in reconstruction /...
left-hand derivatives of the funotion f at the point xj Q, R - the sets of rational and of real numbers; m(A) - the Lebesgue measure of a set A) Cf, Df - the sets of points of continuity and of disconti-nuity of the funotion f; XA - the characteristic function of the set...
We further show that the proposed density-derivative estimator is useful in improving the accuracy of non-parametric KL-divergence estimation via metric learning. The practical superiority of the proposed method is experimentally demonstrated in change detection and feature selection....
The practical superiority of the proposed method is experimentally demonstrated in change detection and feature selection.Sasaki, HiroakiNoh, Yung-KyunSugiyama, MasashiH. Sasaki, Y.-K. Noh, and M. Sugiyama. Direct density-derivative estimation and its application in KL-divergence approximation. arXiv...