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Technical perspective: Expressive probabilistic models and scalable method of moments

Author:
David M. Blei

Columbia University, New York City, NY

Columbia University, New York City, NY
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Communications of the ACM Volume 61 Issue 4April 2018pp 84https://doi.org/10.1145/3186260

Published:26 March 2018Publication History

Communications of the ACM

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Technical perspective: Expressive probabilistic models and scalable method of moments
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Topic modeling
2. Theory of computation
  1. Design and analysis of algorithms

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Published in
Communications of the ACM Volume 61, Issue 4
April 2018
88 pages
ISSN:0001-0782
EISSN:1557-7317
DOI:10.1145/3200906
Editor:
Andrew A. Chien
Association for Computing Machinery, New York, NY
Issue’s Table of Contents
Copyright © 2018 Owner/Author
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Technical perspective: Expressive probabilistic models and scalable method of moments

Communications of the ACM

Abstract

Cited By

Index Terms

Recommendations

Expressive probabilistic description logics

Rotation and translation invariants of Gaussian-Hermite moments

Generic orthogonal moments: Jacobi-Fourier moments for invariant image description

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Technical perspective: Expressive probabilistic models and scalable method of moments

Communications of the ACM

Abstract

Cited By

Index Terms

Recommendations

Expressive probabilistic description logics

Rotation and translation invariants of Gaussian-Hermite moments

Generic orthogonal moments: Jacobi-Fourier moments for invariant image description

Comments

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Full Access

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