Mirowski P. author LeCun Y. author 2009 This article presents a method for training Dynamic Fac- tor Graphs (DFG) with continuous latent state variables. A DFG in- cludes factors modeling joint probabilities between hidden and observed variables, and factors modeling dynamical constraints on hidden vari- ables. The DFG assigns a scalar energy to each configuration of hidden and observed variables. A gradient-based inference procedure finds the minimum-energy state sequence for a given observation sequence. Be- cause the factors are designed to ensure a constant partition function, they can be trained by minimizing the expected energy over training sequences with respect to the factors’ parameters. These alternated in- ference and parameter updates can be seen as a deterministic EM-like procedure. Using smoothing regularizers, DFGs are shown to reconstruct chaotic attractors and to separate a mixture of independent oscillatory sources perfectly. DFGs outperform the best known algorithm on the CATS competition benchmark for time series prediction. DFGs also suc- cessfully reconstruct missing motion capture data. Bayesian exported from refbase (http://www.helixpartners.com/refbase-0.9.5/show.php?record=650), last updated on Mon, 04 Jan 2010 20:27:20 -0800 text Mirowski+LeCun2009 Helix Partners @ m.perone @ 341 miscellaneous