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Implicit Priors for Knowledge Sharing in Bayesian Neural Networks
Bayesian interpretations of neural network have a long history, dating back to early work in the 1990's and have recently regained attention because of their desirable properties like uncertainty estimation, model robustness and regularisation.
Smooth input preparation for quantum and quantum-inspired machine learning
Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage.
Quantum algorithms for training Gaussian Processes
Gaussian processes (GPs) are important models in supervised machine learning.
Quantum assisted Gaussian process regression
We show that even in some cases not ideally suited to the quantum linear systems algorithm, a polynomial increase in efficiency still occurs.
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