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Found 10 papers, 7 papers with code
Spatio-Temporal Variational Gaussian Processes
We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP method for multivariate data that scales linearly with respect to time.
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterior linearisation (PL) as extensions of Newton's method for optimising the parameters of a Bayesian posterior distribution.
Sparse Algorithms for Markovian Gaussian Processes
Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging probabilistic models for real-world time series.
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
EP provides some benefits over the traditional methods via introduction of the so-called cavity distribution, and we combine these benefits with the computational efficiency of linearisation, providing extensive empirical analysis demonstrating the efficacy of various algorithms under this unifying framework.
Fast Variational Learning in State-Space Gaussian Process Models
Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation.
Movement Tracking by Optical Flow Assisted Inertial Navigation
We consider a visually dense approach, where the IMU data is fused with the dense optical flow field estimated from the camera data.
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes
The extended Kalman filter (EKF) is a classical signal processing algorithm which performs efficient approximate Bayesian inference in non-conjugate models by linearising the local measurement function, avoiding the need to compute intractable integrals when calculating the posterior.
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis.
Unifying Probabilistic Models for Time-Frequency Analysis
In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts.
A Generative Model for Natural Sounds Based on Latent Force Modelling
Recent advances in analysis of subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitudes to be a crucial component of perception.
