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…
A. Slivkins. (2019)cite arxiv:1904.07272Comment: The manuscript is complete, but comments are very welcome! To be published with Foundations and Trends in Machine Learning.
S. Kuutti, R. Bowden, Y. Jin, P. Barber, und S. Fallah. (2019)cite arxiv:1912.10773Comment: 23 pages, 3 figures, Accepted in IEEE Transactions on Intelligent Transportation Systems.
A. Adamatzky, P. Ayres, G. Belotti, und H. Wosten. (2019)cite arxiv:1912.13262Comment: The position paper to be published in the International Journal of Unconventional Computing.