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Found 7 papers, 2 papers with code
Incorporating Sum Constraints into Multitask Gaussian Processes
Machine learning models can be improved by adapting them to respect existing background knowledge.
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms.
Linearly Constrained Neural Networks
We present a novel approach to modelling and learning vector fields from physical systems using neural networks that explicitly satisfy known linear operator constraints.
Deep kernel learning for integral measurements
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions.
A fast quasi-Newton-type method for large-scale stochastic optimisation
During recent years there has been an increased interest in stochastic adaptations of limited memory quasi-Newton methods, which compared to pure gradient-based routines can improve the convergence by incorporating second order information.
Probabilistic approach to limited-data computed tomography reconstruction
The approach also allows for reformulation of come classical regularization methods as Laplacian and Tikhonov regularization as Gaussian process regression, and hence provides an efficient algorithm and principled means for their parameter tuning.
Linearly constrained Gaussian processes
We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints.
