Understanding Submodular Optimization And Machine Learning Part 2
Let's dive into the details surrounding Submodular Optimization And Machine Learning Part 2. Many problems in
Key Takeaways about Submodular Optimization And Machine Learning Part 2
- EE596B
- Abstract:
- Andreas Krause, ETH Zürich https://simons.berkeley.edu/talks/andreas-krause-stefanie-jegelka-01-23-2017-
- Elad Hazan, Princeton University https://simons.berkeley.edu/talks/elad-hazan-01-23-2017-
- Tutorial no.
Detailed Analysis of Submodular Optimization And Machine Learning Part 2
Many problems in Norm so that basically means you can use it as a convex Norm a structured conx Norm for any particular This is Stefanie Jegelka's lecture on Submodularity, given at the
Abstract: Many
That wraps up our extensive overview of Submodular Optimization And Machine Learning Part 2.