Introduction
1. Anima Anandkumar: Learning in Higher Dimensions
2. Yoshua Bengio: Machines That Dream.
3. Brendan Frey: Deep Learning Meets Genome Biology
4. Risto Miikkulainen: Stepping Stones and Unexpected Solutions in Evolutionary Computing
5. Benjamin Recht: Machine Learning in the Wild
6. Daniela Rus: The Autonomous Car As a Driving Partner
7. Gurjeet Singh: Using Topology to Uncover the Shape of Your Data.
8. Ilya Sutskever: Unsupervised Learning, Attention, and Other Mysteries
9. Oriol Vinyals: Sequence-to-Sequence Machine Learning
10. Reza Zadeh: On the Evolution of Machine Learning
Key Takeaways
• Modern machine learning involves large amounts of data and a large number of variables, which makes it a high dimensional problem.
• Tensor methods are effective at learning such complex high dimensional problems, and have been applied in numerous domains, from social network analysis, document categorization, genomics, and towards understanding the neuronal behavior in the brain.
• As researchers continue to grapple with complex, highlydimensional problems, they will need to rely on novel techniques in non-convex optimization, in the many cases where convex techniques fall short.
