38 citations · 58 across the 13 of their papers we have counts for
6 papers · 1 filter
Sparse Algorithms for Markovian Gaussian Processes
William J. Wilkinson, Arno Solin, Vincent Adam
Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging probabilistic models for real-world time series. Sparse Markovian Gaussian proces…
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
William J. Wilkinson, Paul E. Chang, Michael Riis Andersen +1
We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian process models as a simple parameter update rule applied during Kalman smoothing.…
Gaussian Process Priors for View-Aware Inference
Yuxin Hou, Ari Heljakka, Arno Solin
While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations be…
Scalable Exact Inference in Multi-Output Gaussian Processes
Wessel P. Bruinsma, Eric Perim, Will Tebbutt +3
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-te…
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Arno Solin, Manon Kok
Gaussian processes (GPs) provide a powerful framework for extrapolation, interpolation, and noise removal in regression and classification. This paper considers constraining GPs to…
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss +2
A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based featur…