Tensors Gurantee a Global Optimum for Non-Convex Problems

A presentation was given by Anima Anandkumar from UC Irvine at the O'Reilly Strata Conf in San Jose, CA on 2/18/2015 titled "Tensor Methods for Large-scale Unsupervised Learning: Applications to Topic and Community Modeling." For a YouTube recording of the presentation, click here. There she demonstrates how tensors can be used to mathematically gurantee a global optimum for non-convex problems. She goes on to demonstrate how tensors computations can be parallelized. She is giving a similar talk at Strata Conf in New York, NY on 9/29/2015.

Below are some related links

1. To go to Anima Anandkumar web page at uci.edu, click here
   
2. Let’s build open source tensor libraries for data science - Tensor methods for machine learning are fast, accurate, and scalable, but we'll need well-developed libraries by Ben Lorica - March 17, 2015
   
3. The tensor renaissance in data science - The O'Reilly Data Show Podcast: Anima Anandkumar on tensor decomposition techniques for machine learning by Ben Lorica - May 7, 2015
   
4. The Learning Machine Brochure by Anima Anandkumar
   
5. Anima Anandkumar - Data Science Initiative 2014 on YouTube