A fundamental link between two counterintuitive phenomena in spin glasses—reentrance and temperature chaos—has been ...
Causal Machine Learning (CML) unites ML techniques with CI in order to take advantage of both approaches’ strengths. CML ...
Abstract: Due to various reasons, outliers, ambient noise and missing data inevitably exist in the industrial processes, and thus the robustness is important when establishing monitoring models. In ...
In the predawn hours of August 19, 2024, bolts of lightning began to fork through the purple-black clouds above the Mediterranean. From the rail of a 184-foot vessel, a 22-year-old named Matthew ...
Abstract: In this work, we have developed a variational Bayesian inference theory of elasticity, which is accomplished by using a mixed Variational Bayesian inference Finite Element Method (VBI-FEM) ...
Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...
The authors proposed an important novel deep-learning framework to estimate posterior distributions of tissue microstructure parameters. The method shows superior performance to conventional Bayesian ...
The Evidence Lower Bound (ELBO) is a key objective for training generative models like Variational Autoencoders (VAEs). It parallels neuroscience, aligning with the Free Energy Principle (FEP) for ...
Approach developed at the Texas A&M School of Public Health offers promising new knowledge on idiopathic pulmonary fibrosis pathways Texas A&M University A new statistical technique developed by a ...
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