Backpropagation of Approximate Gradients for Stochastic Lattice Models

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dc.contributor.advisorKeemink, Sander
dc.contributor.advisorWortel, Inge
dc.contributor.authorSchering, Jan
dc.date.issued2023-07-12
dc.description.abstractStochastic lattice models (s-LMs) are efficient computational tools for simulating non-equilibrium and morphogenetic systems. Yet, to date, no efficient method for fitting s-LM parameters to a set of objectives has been devised. The current state-of-the-art method for fitting s-LMs is Approximate- Bayesian Computation, a gradient-free general-purpose Bayesian inference method for estimating posterior distributions. While powerful, it is computationally expensive and quickly becomes intractable for high-dimensional parameter spaces. Gradient-based methods, particularly gradient descent algorithms making use of backpropagation could be significantly more efficient by directly fitting the parameters without needing to estimate the posterior. However, s-LMs are non-differentiable due to stochasticity and discreteness, preventing the use of backpropagation. Recent advances in Artificial Intelligence have introduced methods to circumvent this issue. The reparameterization trick re-enables backpropagation for stochastic models. Straight-through Estimation, on the other hand, enables backpropagation of approximate gradients for discrete models. We investigate the application of these methods for fitting four different s-LMs with respect to four common types of objectives. The results are promising, showing that backpropagation of approximate gradients can successfully be applied to various s-LM tasks. However, some challenges remain that will require further investigation.
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/16419
dc.language.isoen
dc.thesis.facultyFaculteit der Sociale Wetenschappen
dc.thesis.specialisationspecialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Master Artificial Intelligence
dc.thesis.studyprogrammestudyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence
dc.thesis.typeMaster
dc.titleBackpropagation of Approximate Gradients for Stochastic Lattice Models
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