Block Seminar Machine Learning for Language Processing (Spring 2027)

Deep learning is the predominant machine learning paradigm in language processing. This approach not only gave huge performance improvements across a large variety of natural language processing tasks. This time we will loke at smoothness of loss landscape and realted topics.

Lecturer:  Dietrich Klakow

Location:  t.d.b

Time: block course in the spring break 2027 however preparations start earlier. Here the specific time line

Closing topic doodle: tbd
Kick-Off: tbd
One page outline: tbd
Draft presentation: tbd

Practice talks and final talks will be during the spring break. Time/date will be decided during the kick-off.

Application for participation:  see CS seminar system (for CS; BioInf, DSAI, VC, ES, …), for CoLI,LST, LCT use LSF to apply.

HISPOS registration deadline: tbd

Grading (tentative):

  • 5% three slide/5 min presentation
  • 10% draft of full presentation
  • 10% practice talk
  • 25% own experiments and coding
  • 10% report on coding task
  • 35% final talk
  • 5% contributions to discussion during final talk of fellow participants

List of Topics (tentative):

  1. Investigating the overlooked hessian structure: From cnns to llms