|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 listsCUUEG talks Physics of Medicine Roadshow Infant Sorrow
Other talksThe role of macrophages in tumour progression and metastasis Jerrold Levinson on 'Ain't that a Shame: Shame in General, and Shame in Music'. Turbulent fountains: classification and scaling arguments Noise in audio and electronics Bollywood Dance Workout Engineered Quantum Systems (Prof. Gerard J. Milburn, The University of Queensland)