University of Cambridge > Talks.cam > Machine Learning @ CUED > Models and inference for temporal Gaussian processes

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Models and inference for temporal Gaussian processes

Download to your calendar using vCal

If you have a question about this talk, please contact Dr R.E. Turner .

Reformulating Gaussian process (GP) models as stochastic differential equations (SDE) opens up new opportunities to draw comparisons between classical signal processing algorithms and the state of the art inference methods that dominate the current machine learning literature. In this talk, I’ll discuss the SDE form of the popular “spectral mixture” GP, showing how in the temporal case we can think of such a model as performing probabilistic time-frequency analysis. Following this, I will discuss recent advances in approximate inference for temporal GP models, focusing on power expectation propagation (EP), which is a natural fit for sequential data. Lastly, I will present new work showing the conditions under which power EP recovers traditional methods such as the extended Kalman filter.

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Webinar - https://zoom.us/j/93763520674?pwd=WTlDZThITTFUaEpKL2xMZ1YzZWVkZz09 Average on weekdays: 9020.26762821 kWh Chemical Engineering Department

Other talks

The Imagined Conflict: On Science and God Tea and Coffee Break Machine Politics? Computers and AI in the History of Political Thought Haematology and Palliative Care TBC The uplift and erosion history of the Gamburtsev Subglacial Mountains and their role in the formation of the East Antarctic Ice Sheet
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity