Paper presentation at the International Joint Conference on Neural Networks (IJCNN)

This week the former FIW student Simon Heilig had the chance to present the work he did together with Prof. Dr. Frank-Michael Schleif and Maximilian Münch to the community of the flagship conference of neural networks from IEEE INNS/CIS. The this year’s IJCNN took place at Padova in collaboration with WCCI 2022.

The work was all about large-scale learning and approximating so-called kernel matrices. The research group demonstrated that a foundamental property was violated by a proposed memory efficient approximation approach. They did not only revisited the method, but they also extended the class of applicable functions, so that arbitrary similarity measures, stemming from real-world scenarios like protein sequence comparisons, can be utilized.
For more details, have a look at the pre-print.

In his LinkedIn post, Heilig expresses "special thanks to the organizers of the conference and to the staff, making this event to a really special starting point in my carrier! Also, I‘m grateful for the support from Center of Artificial Intelligence and Robotics, located at Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt."

Source: https://www.linkedin.com/feed/update/urn:li:activity:6956946344330600448/