Deep learning is the predominant machine learning paradigm in natural language processing (NLP) and beyond. This approach not only gave huge performance improvements across a large variety of natural language processing, computer vision and other tasks, it also allows to integrate external knowledge sources. The dominance of deep neural networks also brought different fields closer together as many approaches and ideas are shared. Even though the seminar has NLP in its name, it will try to take this more general perspective. It will also put a stronger emphasis on theory.
Lecturer: Dietrich Klakow
Location: to be announced
Time: block course in the spring break 2023; there will be a kick-off meeting in December
Application for participation: CoLi/LST/LCT please use our internal registration sytem: tbd; CS and everybody else: please use the CS seminar assignment system
- 5% one page outline of talk (pdf, due in December, exact date will follow)
- 10% draft presentation (pdf, due in January, exact date will follow)
- 10% practice talk (two weeks before the official talk)
- 35% final talk (date in the break set by a doodle)
- 5% contributions to discussion during final talk of fellow participants
- 35% report (deadline in May, exact date will follow)
Exam date: there is a nominal exam date most likely in December in order to create a HISPOS registration deadline. Please keep that in mind and register in time in HISPOS.
List of Topics:
- here a list for recent ACL/NAACL/NIPS/ICML or imilar papers will follow