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 different 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.

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.

Kamiar Rahnama Rad : 4pm Oct 13

Kamiar will be going over the following paper at 4pm:

M. J. Wainwright (2009), Sharp thresholds for noisy and high-dimensional recovery of sparsity using L1-constrained quadratic programming (Lasso). , IEEE Transactions on Information Theory, 55:2183--2202, May 2009