Conference on Parsimony and Learning (CPAL)

March 2025, Stanford

Tutorials

The final day of the conference features tutorial presentations, which are open to the public. These tutorials present an up-to-date account of the intersection between low-dimensional modeling and deep learning in an accessible format.

The tutorials consist of two parallel tracks, respectively titled Learning Deep Low-dimensional Models from High-Dimensional Data: From Theory to Practice, and Advances in Machine Learning for Image Reconstruction: Sparse Models to Deep Networks.

Each track consists of four lectures. The planned content of the two tracks is summarized below. See the schedule for the precise times of each tutorial.

Track: Learning Deep Low-dimensional Models from High-Dimensional Data: From Theory to Practice

Track: Advances in Machine Learning for Image Reconstruction: Sparse Models to Deep Networks

Lectures 1-3 will cover a diverse spectrum of topics across sparse modeling and deep learning and theory with applications in medical imaging and image restoration/computer vision. A subset of works across topics will be discussed including works from tutorial presenters.