Preview Mode Links will not work in preview mode

Unconventional Computing

Dec 21, 2018

Constrained counting is a fundamental problem in artificial intelligence, with applications in probabilistic reasoning, inexact computing, and statistical physics. In today’s episode, we will learn about research at Rice which studies how graph decompositions and tensor network contraction can be used to solve constrained counting problems.

Welcome to Unconventional Computing, a Rice University Computer Science podcast! I’m Cannon Lewis, your host and a Ph.D. student in the Computer Science Department here at Rice. My guest on today’s special, grad-student-only episode is Jeff Dudek. Jeff is a Ph.D. student in the Computer Science Department working in the field of machine learning with Professor Moshe Vardi.  For more information, please visit CS.