|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Fast Variational Inference in the Conjugate Exponential Family
If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins.
This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
We present a general method for deriving collapsed variational inference algorithms for probabilistic models in the conjugate exponential family. Our method unifies many existing approaches to collapsed variational inference. Our collapsed variational inference leads to a new lower bound on the marginal likelihood. We exploit the information geometry of the bound to derive much faster optimization methods based on conjugate gradients for these models. Our approach is very general and is easily applied to any model where the mean field update equations have been derived. Empirically we show significant speed-ups for probabilistic inference using our bound.
This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsEducation Society Cambridge (ESC) Computer Laboratory Security Seminar Scott Polar Research Institute - other talks
Other talksThe 2015 Tissue Engineering Congress Seismic retrofitting of historic masonry structures Disturbance Amplification in Mass Chains: how to prevent buildings from falling over TBA Defining Autism Since 1979: The Sciences of Social Impairment Centrifugal vibration absorbers and some applied mechanics history