NIPS interesting paper on group theory and Fourier analysis applied to inference
This week I am at the annual Neural Information Processing Systems Conference a fascinating conference that combines many of my scientific interests on machine learning, computer vision, statistics, and natural language processing. Last night I visited the poster by Jonathan Huang on efficient inference for distributions on permutations.
The paper considers the problem of how to reason probabilistically in a tracking task where you have sporadic tracking information of objects. Juang and co-authors end up using concepts like irreducible representations and Clebsch-Gordan coefficients. This may be unfamiliar concepts to the reader but to me they sound like a distant echo of all my physics training since these concepts are all over quantum mechanics and quantum field theory. What a cool paper! I’ll definitely be studying this paper since I have been interested in the issue of permutations with my work on recognizing answer patterns in multiple-choice exams.
