Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models
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A versatile method to automatically classify ice particle habit from various airborne optical array probes is presented. The classification is achieved using a multinomial logistic regression model. For each airborne probe, the model determines the particl ...
Physiological evidence suggests that neighboring brain regions have similar perfusion characteristics (vascular supply, collateral blood flow). It is largely unknown whether integrating perfusion CT (pCT) information from the area surrounding a given voxel ...
Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...
Machine learning tools such as neural networks and Gaussian process regression are increasingly being implemented in the development of atomistic potentials. Here, we develop a formalism to leverage such non-linear interpolation tools in describing propert ...
Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...
Deep neural network training spends most of the computation on
examples that are properly handled, and could be ignored.We propose to mitigate this phenomenon with a principled importance
sampling scheme that focuses computation on "informative" examples ...
Living in deprived neighbourhoods may have biological consequences, but few studies have assessed this empirically. We examined the association between neighbourhood deprivation and allostatic load, a biological marker of wear and tear, taking into account ...
Recent advances in statistical learning and convex optimization have inspired many successful practices. Standard theories assume smoothness---bounded gradient, Hessian, etc.---and strong convexity of the loss function. Unfortunately, such conditions may ...
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes over th ...
Multiple generalized additive models are a class of statistical regression models wherein parameters of probability distributions incorporate information through additive smooth functions of predictors. The functions are represented by basis function expan ...