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Sparse matrices in sparse analysis

In this talk, I will give two vignettes on the theme of sparse matrices in sparse analysis. The first vignette covers work from compressive sensing in which we want to design sparse matrices (i.e., matrices with few non-zero entries) that we use to (linearly) sense or measure compressible signals.
Location: B205 Engineering Quadrangle
Speaker(s):
Tags: Seminars

Rethinking the Role of Optimization in Learning

In this talk, I will overview our recent progress towards understanding how we learn large capacity machine learning models, especially deep neural networks. In the modern practice of deep learning, many successful models have far more trainable parameters compared to the number of training examples.
Location: B205 Engineering Quadrangle
Speaker(s):
Tags: Seminars

Astrophysics as a Testbed for Statistical Method Development

There have been many efforts to apply methods from machine learning and statistics to make discoveries in astrophysics and throughout the physical sciences. While it is clear that the use of these methods has advanced our science goals, I will argue that these collaborations can also advance research in machine learning.

Location: Jadwin Hall Room 407, Princeton Center for Theoretical Science (PCTS)
Speaker(s):
Tags: Seminars

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