Poster
in
Workshop: Methods and Opportunities at Small Scale (MOSS)
Approximate Message Passing on General Factor Graphs using Shallow Neural Networks
Leonhard Hennicke · Jan Lemcke · Rainer Schlosser · Ralf Herbrich
Keywords: [ Factor Graphs ] [ Message Passing ] [ Probabilistic Machine Learning ] [ Sampling ] [ Shallow Neural Networks ]
Factor graphs offer an efficient framework for probabilistic inference through message passing, with the added benefit of uncertainty quantification, which is crucial in safety-critical applications. However, their applicability is limited by the need to analytically solve update equations for factors, which are problem-specific and may involve intractable integrals. We propose to approximate the message update equations of individual factors with shallow neural networks, which we train on data generated by sampling from the respective factor equations, to capture complex factor relationships while maintaining computational tractability.