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We propose a nonparametric method for constructing multivariate kernels tuned to the configuration of the sample, for density estimation in R-d, d moderate. The motivation behind the approach is to break down the construction of the kernel into two parts: ...
State-of-the-art density estimation methods for rendering participating media rely on a dense photon representation of the radiance distribution within a scene. A critical bottleneck of such kernel-based approaches is the excessive number of photons that a ...
Inertial measurement unit (IMU) is a promising tool in the quantification of energy expenditure for human on-land activities, though has never been deployed before to calculate the aquatic activities energy expenditure. Investigating the factors that influ ...
In stochastic optimal control the distribution of the exogenous noise is typically unknown and must be inferred from limited data before dynamic programming (DP)-based solution schemes can be applied. If the conditional expectations in the DP recursions ar ...
Many pharmaceutical products find their way into receiving waters, giving rise to concerns regarding their environmental impact. A procedure was proposed that enables ranking of the hazard to aquatic species and human health due to such products. In the pr ...
We show how nonlinear embedding algorithms popular for use with "shallow" semi-supervised learning techniques such as kernel methods can be easily applied to deep multi-layer architectures, either as a regularizer at the output layer, or on each layer of t ...
This study proposes a new technique for real-time building energy modelling and event detection using kernel regression. We show that this technique can exceed the performance of conventional neural network algorithms, and do so by a large margin when the ...
Traditionally, spatial analysis of point pattern has been mostly focused on Euclidean space. As many human related phenomena take place on a network, the assumption of a continuous isotropic space fails to describe events which actually occur on a one-dime ...
In this article we introduce JULIDE, a software toolkit developed to perform the 3D reconstruction, intensity normalization, volume standardization by 3D image registration and voxel-wise statistical analysis of autoradiographs of mouse brain sections. Thi ...
We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and m ...