Thursday, October 21, 2010

Micky Vidne: October 27th. s(MC)^2 or Hesitant Particle Filter.

In my talk I will describe a recent extension of the Sequential Monte Carlo (SMC) method. SMCs (particle filters) are a commonly used method to estimate a latent dynamical process from sequential noise-contaminated observations. SMCs are extremely powerful but suffer from sample impoverishment, a situation in which very few diļ¬€erent particles represent the distribution of interest. I will describe our attempt to circumvent this fundamental problem by adding an extra MCMC step in the SMC algorithm. I will illustrate the usefulness of this algorithm by considering a toy neuroscience example.

Wednesday, October 20, 2010

Kolia Sadeghi : Oct 20th

I'll be going over this paper on Deep Boltzmann Machines and adaptive MCMC starting with some background on Restricted Boltzmann Machines.  In passing I'll give a quick overview of related architectures used to learn temporal sequences.