C. (2014). Bayesian regularization via graph laplacian. Bayesian Analysis 9, 2, 449-474.Fei Liu, Sounak Chakraborty, Fan Li, Yan Liu, Aurelie C Lozano, et al. Bayesian regular- ization via graph laplacian. Bayesian Analysis, 9(2):449-474, 2014....
We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions. Result...
To combat this, MC dropout was introduced; it uses dropout [34] as a regularization term to compute the prediction uncertainty [35]. Dropout is an effective technique that has been widely used to solve overfitting problems in deep NNs (DNNs). During the training process, dropout randomly ...
In the third application, we use the Bayesian interpretation of regularization to choose the optimal smoothing parameter for interpolation. The uncertainty modeling techniques that we develop, and the utility of these techniques in various applications, support our claim that Bayesian modeling is a ...
We consider a hierarchical Bayesian approach with a prior that is constructed by truncating a series expansion of the soft label function using the graph Laplacian eigenfunctions as basis functions. We compare our truncated prior to the untruncated Laplacian based prior in simulated and real data ...
Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularizationdoi:10.1080/03610918.2019.1634202Jarno HartogHarry van ZantenTaylor & Francis
To combat this, MC dropout was introduced; it uses dropout [34] as a regularization term to compute the prediction uncertainty [35]. Dropout is an effective technique that has been widely used to solve overfitting problems in deep NNs (DNNs). During the training process, dropout randomly ...
To avoid the high computational complexity involved in estimating the Laplacian regularization parameter, we have also considered the Jeffreys prior, as it does not depend on any hyperparameter. The prior probability distribution on the class-label image is an MLL Markov鈥揋ibbs distribution, which ...
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain. IEEE Trans. Image Process. 2017, 26, 1770–1785. [Google Scholar] [CrossRef] Raj, R.G. A hierarchical Bayesian-MAP Approach to Inverse Problems in Imaging. Inverse Probl. 2016, 32, 075003. [Google Scholar...
In this case, when using a Laplacian prior model and the MAP estimator, the problem becomes equivalent to the optimization of 𝐽(𝒛)=∥𝒈−𝑯𝑫𝒛∥2+𝜆∥𝒛∥1J(z)=g−HDz2+λz1, which is a typical 𝐿1L1 regularization method. The particularity of our work was to ...