Cini Lab on Data Science

The Department of Engineering, Università degli Studi di Perugia organizes the seminar entitled Training Fully Connected Neural Networks is ∃R-Complete. The seminar is taught by Paul Jungeblut of Karlsruhe Institute of Technology (KIT).

Abstract: we consider the algorithmic complexity of training fully connected neural networks. In particular, we see that this problem is ER-complete. This means that it is (up to polynomial transformations) equally difficult as finding the solutions of a system of polynomial equations and inequalities in several unknowns.
In the talk I will introduce the neural network training problem and give some background on ER-completeness. Then we combine both to get an idea how ER-completeness is proven.
The talk is based on our recent paper available at

The seminar is taught within the PhD course in Industrial and information engineering.

News by prof. Fabrizio Montecchiani, Department of Engineering, Università degli Studi di Perugia.

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