While minimal impact was observed at lower super-resolution factors, entropy regularization improved CNN accuracy at higher factors, suggesting the potential for dynamically adjusting the entropy regularization coefficient based on the super-resolution factor to optimize flood map accuracy. Moreover, similar...
By introducing the kernel function-based similarity terms in the update formula of the cluster centers, the effect of the objects not belonging to the current cluster on the update of its corresponding center can be counteracted, and simultaneously the influence of regularization coefficient on the ...
Ma [14], however, adds entropy and log probability regularization terms to the discounted rewards. In Cayci et al. [39], the value function is regularized and approximated linearly using a Softmax parameterization, in which no constraint is considered. Similarly, the entropy regularization can ...
Since there are some limitations to detect anomaly only by using the adjacency matrices of the graph, we calculate the graph entropy using the spurious correlation coefficient to measure the similarity between two graphs. Then, we propose a dynamic graph embedding model based on graph entropy to ...
Moreover, we have observed a logarithmic scaling of this quantity in models with a continuous spectrum, with a universal coefficient that is sensitive to the scaling of the density of states near the edge of the spectrum. Lastly, we found the connection between the real and imaginary parts of...
Taking ratios of gene expression values and introducing a regularization term to offset dropouts, makes the resulting inference much less sensitive to the sequencing depth, absolute scale and normalization procedure of scRNA-Seq data. Second, signalling entropy is estimated over a fairly large number ...
The value of the regularization parameter varies from 0.01 to 16, and the value of the kernel coefficient (γ) varies from 0.0001 to 2. The highest accuracy is 86.34% when the regularization parameter is 13 and γ=2. It is worth mentioning that the best combination of parameters in the ...
Maximum entropy, as applied to image restoration, can be thought of as a particular case of a more general technique known as regularization. One approach to ill-posed problems, such as image restoration, is to find solutions that are consistent with the data, but which possess other desirable...
(B31) On the right hand side of the inequality, we call the first norm as the regularization error, while the second norm as the truncation error. Note that we take the norm to be the maximum norm over the interval [−1, 1]. To be more precise, we can write the truncation error...
Its coefficient is scheme dependent and thus non-universal. In particular, it vanishes within dimensional regularization scheme which we employ throughout this paper. – 16 – a conformal anomaly, which simply arises because the presence of a defect in the vacuum provides additional ways to violate...