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Deep learning has become a major tool in machine learning and has been applied with great success to many problems. It remains to develop a theoretical understanding of deep learning. This talk will discuss a number of interesting problems that need looking at.
Michael R.Fellows
(University of Bergen, Norway)
Frontiers and key open problems in parameterized complexity
This talk will address two themes:
(1) Concrete open problems that are currently guiding the field because they are so well known and seem to require fresh approaches and new basic techniques;
(2) Some general major thematic directions that seem ripe for research initiatives.
Matthew Katz
(Ben-Gurion University of The Negev, Israel)
Geometric data structures motivated by wireless networks
Algorithmic study of wireless networks has recently led to the definition of new geometric data structures. Among these are the bounded-angle spanning tree, related to networks with angular constraints, and SINR diagram, induced by the SINR (Signal to Interference plus Noise Ratio) equation which attempts to predict whether a particular transmitter is heard at a specific location. This talk will discuss these data structures and some algorithmic issues related to them.