The program focused on the following four themes:
- Optimization: How and why can deep models be fit to observed (training) data?
- Generalization: Why do these trained models work well on similar but unobserved (test) data?
- Robustness: How can we analyze and improve the performance of these models when applied outside their intended conditions?
- Generative methods: How can deep learning be used to model probability distributions?
A minimal surface is the surface of minimal area between any given boundaries. In nature such shapes result from an equilibrium of homogeneous tension, e.g. in a soap film. Minimal surfaces have a constant mean curvature of zero, i.e. the sum of the principal curvatures at each point is zero. Particularly fascinating are minimal surfaces…
F. Sultana, A. Sufian, and P. Dutta. (2019)cite arxiv:1905.01614Comment: 7 pages, 10 figures, 1 table, Submitted to 2nd International Conference on Communication, Devices and Computing(ICCDC 2019).
A. Alemi, and I. Fischer. (2018)cite arxiv:1807.04162Comment: Presented at the ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models.