In this paper we propose two fast Total Variation (TV) based algorithms for image restoration by utilizing variational posterior distribution approximation. The unknown image and ...
Bruno Amizic, S. Derin Babacan, K. Michael Ng, Raf...
A Bayesian approach to analyze the modes of variation in a set of curves is suggested. It is based on a generative model thus allowing for noisy and sparse observations of curves....
Multi-class labeling is one of the core problems in image analysis. We show how this combinatorial problem can be approximately solved using tools from convex optimization. We sugg...
Variational methods have proved popular and effective for inference and learning in intractable graphical models. An attractive feature of the approaches based on the Kullback-Lei...
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...