M. Sc. Heinke Hihn

I am a research associate and Ph.D. student within the Learning Systems Group headed by Prof. Dr. Dr. Daniel A. Braun.

Research

 

I work on learning and optimization problems in artificial learning systems that have to cope with limited resources, such as time and memory, where i apply methods and algorithms from the areas of

  • Information-theoretic Learning Models
  • Reinforcement Learning
  • Monte Carlo Optimization
  • Meta Learning
  • Continual Learning

 

Teaching

  • Master Project Learning Robots
  • Master Project Deep Reinforcement Learning

  • Master Project Learning Robots

  • Master Project Learning Robots

  • Master Project Learning Robots
  • Lecture Learning Systems I
  • Lecture Einführung in die Programmierung / Einführung in die Informatik I - Grundlagen

  • Master Project Learning Robots

  • Master Project Learning Robots
  • Lecture Learning Systems I
  • Lecture Einführung in die Informatik I - Grundlagen der Programmierung

  • Master Project Learning Robots 
  • Lecture Foundations And Concepts of Cognitive Systems Modeling

  • Master Project Learning Robots
  • Lecture Learning Systems II

  • Master Project Learning Robots 
  • Lecture Foundations And Concepts of Cognitive Systems Modeling
  • Lecture Learning Systems I

  • Master Project Learning Robots 
  • Lecture Learning Systems II

Bachelor's and Master's Thesis Topics

I'm interested in Machine Learning in general, so i offer Bachelor's and Master's Thesis topics in this field. Here's a list of ongoing and completed topics I supervise(d):

Ongoing:
  • T. Meuser: Bestimmung der Relevanz von Monitoring-Werten für Machine Learning Systeme (M.Sc.)
Completed:
  • C. Graml: Mixture-Of-Experts Learning for Probabilistic Movement Primitives (M.Sc.)
  • J. Triep: Learning to Manipulate a Robotic Arm Platform Through Imitation Learning (M. Sc.)
  • M. Füßinger: Information-theoretic Regularization in Neural Networks (B.Sc.)
  • F. Kneist: Segmentation of Web Content via Deep Convolutional Neural Networks (M.Sc.)
  • C. Landgraf: Instance Segmentation in bin-picking Scenarios using Convolutional Neural Networks (M.Sc.)
  • N. Mehlhase: Detecting Anomalies in Medical Images with Deep Convolutional Neural Networks (M.Sc.)
  • Z. Yeqiang: Object Grasping with Probabilistic Movement Primitives (B. Sc.)
  • P. Schwarz: Improving Model-Based Reinforcement Learning with Adaptive Action Priors (M. Sc.)
  • L. Wehinger: Deep Generative Replay with Regularized Autoencoder for Long Task Sequences (M.Sc.)
  • B. Bernard: Movie Genre Classification based on Transformer Models (M.Sc.)
  • J. Eberhardt: Machine Learning Methods for Detecting Fake News (B.Sc.)
  • Z. Yeqiang: Continual Learning in Non-I.I.D. Environments (M.Sc.)
  • M. Sirtmatsis: Visualizations for Training Progress in Deep Reinforcement Learning (B.Sc., jointly with A. Bäuerle from the Institute of Media Informatics)
  • Y. Berner: Transfer Learning for License Plate Segmentation Deep  Neural Networks (B.Sc.)
  • A. Ludwig: Exploring Strategies for Deep Q-Learning (B.Sc.)
  • S. Graf: Detecting COVID-19 Infections in Medical X-Ray Images with Deep Convolutional Networks (B. Sc.)

If you are looking for a thesis in machine learning, feel free to contact me.

Publications

2021
  • Thiam P., Hihn H., Braun D.A., Kestler H.A., Schwenker F. (2021): Multi-Modal Pain Intensity Assessment based on Physiological Signals: A Deep Learining Perspective. In: Frontiers in Physiology [link, open access]
2020
  • Hihn H., Braun D.A. (2020): Hierarchical Expert Networks for Meta-Learning. In: 4th ICML Workshop on Life Long Machine Learning. [workshop]
  • Hihn H., Braun D.A. (2020): Specialization in Hierarchical Learning Systems: A Unified Information-theoretic Approach for Supervised, Unsupervised and Reinforcement Learning. In: Neural Processing Letters [link, open access]
  • Bellmann P., Hihn H., Braun D.A., Schwenker F (2020.: Binary Classification: Counterbalancing Class Imbalance by Applying Regression Models in Combination with One-Sided Label Shifts, In: 13th International Conference on Agents and Artificial Intelligence ("ICAART") [arxiv]
2019
  • Hihn H., Gottwald S., Braun D.A. (2019): An Information-theoretic On-Line Learning Principle for Specialization in Hierarchical Decision-Making Systems. In: 58th IEEE Conference on Decision Making and Control, CDC 2019. [arxiv][talk slides]
2018
  • Hihn H., Gottwald S., Braun D.A. (2018): Bounded Rational Decision-Making with Adaptive Neural Network Priors. In: Pancioni L., Schwenker F., Trentin E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2018. Lecture Notes in Computer Science, vol 11081. Springer, Cham [link | arxiv]
2016
  • Hihn H., Meudt S., Schwenker F.: Inferring mental overload based on postural behavior and gestures. In: Proceedings of the 2nd workshop on Emotion Representations and Modelling for Companion Systems. ACM (2016/11/16) [link]
  • Hihn H., Meudt S., Schwenker F.: On Gestures and Postural Behavior as a Modality in Ensemble Methods. In: IAPR Workshop on Artificial Neural Networks in Pattern Recognition. Springer International Publishing (2016/9/28) [link]

 

See also my Google Scholar Profile.

Miscellaneous

I'm a member of the Cognitive Systems Examination Board (Fachprüfungsausschuss) an, where i represent the computer science research staff.