Computation with infinite neural networks
Neural Computation
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Evaluation of gaussian processes and other methods for non-linear regression
Evaluation of gaussian processes and other methods for non-linear regression
Neural Computation
Kernels for Vector-Valued Functions: A Review
Foundations and Trends® in Machine Learning
Data fusion with Gaussian processes
Robotics and Autonomous Systems
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Gaussian process prior systems generally consist of noisy measurements of samples of the putatively Gaussian process of interest, where the samples serve to constrain the posterior estimate. Here we consider the case where the measurements are instead noisy weighted sums of samples. This framework incorporates measurements of derivative information and of filtered versions of the process, thereby allowing GPs to perform sensor fusion and tomography; allows certain group invariances (ie symmetries) to be weakly enforced; and under certain conditions suitable application allows the dataset to be dramatically reduced in size. The method is applied to a sparsely sampled image, where each sample is taken using a broad and non-monotonic point spread function. It is also applied to nonlinear dynamic system identification applications where a nonlinear function is followed by a known linear dynamic system, and where observed data can be a mixture of irregularly sampled higher derivatives of the signal of interest.