[AI Seminar] AI Lunch -- Manzil Zaheer -- March 28

[Adams Wei Yu]

Dear faculty and students,

We look forward to seeing you Next Tuesday, March 28, at noon in NSH 3305
for AI lunch. To learn more about the seminar and lunch, please visit the AI
 Lunch webpage <http://www.cs.cmu.edu/~aiseminar/>.

On Tuesday, Manzil Zaheer <http://manzil.ml/> will give a talk titled
*Exponential Stochastic Cellular Automata For Massively Parallel Inference*.

*Abstract*:

Often statistical models and inference procedures thereof are directly not
good fit for the modern computational resources. To elaborate, current
computational resources are racks of fast, cheap, and heavily multicored
machines yet with a limited memory bandwidth whereas inference strategies
can be inherently sequentially like Gibbs sampling or memory access
intensive like expectation-maximization or other variational inference.

In this talk, we discuss an embarrassingly parallel, memory efficient
inference algorithm for latent variable models in which the complete data
likelihood is in the exponential family. The algorithm is a stochastic
cellular automaton and converges to a valid maximum a posteriori fixed
point. We explore further tricks to improve performance by reducing
pressure on memory bandwidth by use of better data structures.
We apply the algorithm to Gaussian mixture model (GMM) and latent Dirichlet
allocation (LDA) and empirically find that our algorithm is order of
magnitudes faster than state-of-the-art approaches. A simple C++/MPI
implementation on a 16-node cluster can sample more than a billion tokens
per second in case of LDA and a million images in case of GMM.

This is a joint work with Alex Smola, Jean-Baptiste Tristan, Michael Wick,
and Satwik Kottur.
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