- Location: Wilson Hall 103
- Contact: Pam Jones
- Email: firstname.lastname@example.org
- Phone: 615-343-4107
- Audience: Free and Open to the Public
Supervised and unsupervised learning amount to approximate functions in high-dimensional spaces, given sample values. Deep convolutional networks have obtained outstanding results for complex classification and regression problems of highly diverse data, including images, speech, natural language and all kind of physical measurements.
Dimension reduction in deep neural networks rely on separation of scales, computation of invariants over groups of symmetries, and sparse representations. This could be called applied harmonic analysis. We shall analyze the construction of invariants through deep scattering networks computed with wavelet filters and discuss open mathematical questions. Applications to image classification, quantum chemistry, and maximum entropy models of turbulences and textures will be shown. A reception will follow the lecture.
The prestigious Shanks Lecture Series is organized annually by the Department of Mathematics of Vanderbilt University in honor of Baylis and Olivia Shanks. The late Professor Baylis Shanks was chairman of the Department from 1955 through 1969. A list of previous Shanks Conferences and Lecturers can be found here.