Submodularity and Big Data

Explore the applications of submodular functions in addressing big data challenges in this lecture by Jeff Bilmes from the University of Washington. Gain insights into how submodularity can be used for document summarization, data subset selection in speech recognition systems, machine translation, and computer vision. Learn about efficient algorithms that achieve optimal results on standard benchmarks. Discover the advantages of choosing good data subsets, with examples from the TIMIT and Fisher corpora. Understand how bigger datasets can lead to different outcomes and the importance of this concept in big data sciences. Benefit from the speaker's expertise as a professor of Electrical Engineering, Computer Science & Engineering, and Linguistics, as well as his accolades including an NSF Career award and NAE Gilbreth Lectureship award.