Sebastian Gottwald

Post-doctoral researcher at the Institute of Neural Information Processing at Ulm University with a background in mathematics and physics.

Current research interests

Current teaching

Shared projects and research notes

  • Two-kinds-of-free-energy--notebooks Supporting information for the article Gottwald, Braun. The two kinds of free energy and the Bayesian revolution (2020).
  • RDFC: Rate-distortion with fixed cardinality. An implementation and comparison of RDFC (Banerjee et al.) with standard rate-distortion algorithms.
  • Rate-Distortion. A comparison of standard algorithms to determine Shannon's rate-distortion trade-off (Blahut-Arimoto, gradient descent, mapping approach).
  • Tensorflow 2 examples. Simple tensorflow 2 implementations for function optimization and neural network training using different APIs (models.Sequential, tf.keras.layers, tf.keras.metrics, custom training).
  • CNNs with tensorflow. Implementation of a CNN from scratch using tensorflow 1.
  • pr_func. Python class to simplify calculus with discrete probability distributions and other functions (see also these examples).
  • BA_class. Python class that contains a collection of Blahut-Arimoto type algorithms
  • BMA. A modular implementation of Blahut-Arimoto algorithms for feed-forward systems of bounded-rational agents.
  • Coarse-graining and Free Energy. Some coarse-graining properties of free energy expressions.
  • Legendre transform and rate-distortion. The derivative of the rate-distortion curve is NOT the temperature.



Academic positions


Honors and Fellowships

Teaching Experience