Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 4 of 4 for “"Deep Kernel"”.
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The Generalised Gaussian Process Convolution Model
… a theoretical framework for nonparametric kernel models of multidimensional signals defined on multidimensional input spaces. We show that the GGPCM generalises and connects existing work; most notably, we derive a dual formulation of the cross-spectral mixture kernel presented by Ulrich et …
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Deep Embedding Kernel
Kernel methods and deep learning are two major branches of machine learning that have achieved numerous successes in both analytics and artificial intelligence. While having their own unique characteristics, both branches work through mapping data to a feature space that is supposedly more …
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Probabilistic machine learning algorithms for molecule discovery
… weighted retraining (chapter 3) and adaptive deep kernel fitting with implicit function theorem (chapter 4) are both algorithms which use a Gaussian process with a deep neural network kernel function to model the relationship between molecular structure and some property of interest. Tanimoto …
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A diallel study of flowering and of ear components of yield in Corn Belt maize and their interactions with population density
… on ear components of yield, and on components of kernel size. Density effects were highly significant for all traits, except for that of pollen shed duration, in the analyses combined over years. Hybrid-by-year interactions were highly significant for all traits. Correlations between GCA effects …