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02/03/2022
Incorporating Sum Constraints into Multitask Gaussian Processes
Machine learning models can be improved by adapting them to respect exis...
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02/22/2021
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Machine learning practitioners invest significant manual and computation...
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02/05/2020
Linearly Constrained Neural Networks
We present an approach to designing neural network based models that wil...
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09/04/2019
Deep kernel learning for integral measurements
Deep kernel learning refers to a Gaussian process that incorporates neur...
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09/29/2018
A fast quasi-Newton-type method for large-scale stochastic optimisation
During recent years there has been an increased interest in stochastic a...
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09/11/2018
Probabilistic approach to limited-data computed tomography reconstruction
We consider the problem of reconstructing the internal structure of an o...
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03/02/2017