Call for Papers

Topics

Topics of interest in this workshop include but are not limited to:

• Theoretical understanding of deep learning models and learning algorithms
• Learning dynamics of (stochastic) gradient descent
• Stochastic neural networks and random matrix theory
• Symmetry, transformations, and equivariance
• High-dimensional loss landscapes
• Applications to real-world problems

Submission Instructions

Researchers interested in contributing should upload a short paper of up to 4 pages in PDF format by April 30, 2019, 11:59pm (anywhere on earth) using the form below. References and supplementary material can exceed 4 pages. Please upload a single PDF that includes the main paper and any supplementary material.

Submissions will be managed through CMT:

• https://cmt3.research.microsoft.com/TPDLW2019/

Authors should use the style file format in this zip file. Submissions don't need to be anonymized. The workshop allows submissions of papers that are under review or have been recently published in a conference or a journal. Authors should state any overlapping published work at the time of submission. Parallel submissions (such as to NeurIPS) are permitted.

All submissions will be reviewed and will be evaluated on the basis of their technical content. Accepted papers will be selected for either a short oral presentation or spotlight presentation, in addition to a poster presentation. Final versions will be posted on the workshop website.

If you have any questions or difficulty submitting your paper, contact us at icml2019phys4dl@gmail.com.