Improving Gradient Flow with Unrolled Highway Expectation Maximization

High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm

We propose a generic stochastic expectationmaximization (EM) algorithm for the estimation of high-dimensional latent variable models. At the core of our algorithm is a novel semi-stochastic variance-reduced gradient designed for the Qfunction in the EM algorithm. Under a mild condition on the initialization, our algorithm is guaranteed to attain a linear convergence rate to the unknown paramete...

Expectation Maximization

Expectation Maximization

The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...

Autonomous Exploration with Expectation-Maximization

We consider the problem of autonomous mobile robot exploration in an unknown environment for the purpose of building an accurate feature-based map efficiently. Most literature on this subject is focused on the combination of a variety of utility functions, such as curbing robot pose uncertainty and the entropy of occupancy grid maps. However, the effect of uncertain poses is typically not well ...

Data Synthesis with Expectation-Maximization

A problem of increasing importance in computer graphics is to generate data with the style of some previous training data, but satisfying new constraints. If we use a probabilistic latent variable model, then learning the model will normally be performed using Expectation-Maximization (EM), or one of its generalizations. We show that data synthesis for such problems can also be performed using ...