Machine Learning

In a broad perspective, my main research ambition is to understand the fundamental computational principles of learning that characterize intelligence. More specifically, my research interests are focussed on the development, analysis, and application of deep learning models and methods. I am particularly interested in analysing and developing probabilistic models and inference methods, investigating biologically plausible deep learning, and understanding the stochastic processes involved in the training and optimisation of neural networks and probabilistic models.

For more see my Faculty web page here. 

Group Leader

Prof. Dr. Asja Fischer

External Homepage

    2024

  • Layer-wise linear mode connectivity
    Adilova, L., Andriushchenko, M., Kamp, M., Fischer, A., & Jaggi, M.
    In The Twelfth International Conference on Learning Representations
  • Landscaping Linear Mode Connectivity
    Singh, S. P., Adilova, L., Kamp, M., Fischer, A., Schölkopf, B., & Hofmann, T.
    In ICML Workshop on High-dimensional Learning Dynamics: The Emergence of Structure and Reasoning

About the INI

The Institut für Neuroinformatik (INI) is a interdisciplinary research unit of the Ruhr-Universität Bochum. We aim to understand fundamental principles that characterize how organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by insights into natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology, theoretical approaches from physics, mathematics, and computer science, including, in particular, machine learning, artificial intelligence, autonomous robotics, and computer vision.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de