ICLR 2017: Sparse Coding, Canonical Correlation Analysis and Dictionary Learning

Sparse Coding, Canonical Correlation and Dictionary Learning are Matrix Factorization operations. They are used in a variety of ways in building Deep Neural architectures. Here are a few I noticed in  the 500 submissions at the ICLR 2017 conference that are in the open review process.

• Understanding Neural Sparse Coding with Matrix Factorization Thomas Moreau, Joan Bruna
• Energy-Based Spherical Sparse Coding Bailey Kong, Charless C. Fowlkes
• Support Regularized Sparse Coding and Its Fast Encoder Yingzhen Yang, Jiahui Yu, Pushmeet Kohli, Jianchao Yang, Thomas S. Huang
• Transformational Sparse Coding Dimitrios C. Gklezakos, Rajesh P. N. Rao
• NEUROGENESIS-INSPIRED DICTIONARY LEARNING: ONLINE MODEL ADAPTION IN A CHANGING WORLD Sahil Garg, Irina Rish, Guillermo Cecchi, Aurelie Lozano
• Deep Variational Canonical Correlation Analysis Weiran Wang, Xinchen Yan, Honglak Lee, Karen Livescu
• Differentiable Canonical Correlation Analysis Matthias Dorfer, Jan Schlüter, Gerhard Widmer
• Deep Generalized Canonical Correlation Analysis Adrian Benton, Huda Khayrallah, Biman Gujral, Drew Reisinger, Sheng Zhang, Raman Arora

Credit photo: NASA/JPL/University of Arizona

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