Variational posterior approximation using stochastic gradient ascent with adaptive stepsize

Highlights：

• Variational inference, most notably the stochastic variational inference rely on the closed formed coordinate ascent algorithm. A challenge is how to scale up the learning. We proposed using Stochastic Gradient Ascent as the scalable learner for the variational inference of the Variational Bayes Dirichlet Process Mixture.

• In order to achieve speed while maintaining performance for variational inference of DPM using SGA, we adopted two stochastic optimization techniques for our SGA, for comparison. Namely, the natural gradient ascent and the momentum method.

• We show that our new stochastic gradient ascent approach to variational inference is compatible with deep ConvNet features when applied to large scale datasets such as Caltech256 and SUN397. Lastly, we justify our speed and performance claims when compared to closed form coordinate ascent learning on these datasets.