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Dr. rer. nat.
Hecker, Dirk
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Publicationtanh Neurons are Bayesian Decision Makers( 2021)The hyperbolic tangent (tanh) is a traditional choice for the activation function of the neurons of an artificial neural network. Here, we go through a simple calculation that shows that this modeling choice is linked to Bayesian decision theory. Our brief, tutoriallike discussion is intended as a reference to an observation rarely mentioned in standard textbooks.

PublicationMaxSum Dispersion via Quantum Annealing( 2019)We devise an Ising model for the maxsum dispersion problem which occurs in contexts such as Web search or text summarization. Given this Ising model, maxsum dispersion can be solved on adiabatic quantum computers; in proof of concept simulations, we solve the corresponding SchrÃ¶dinger equations and observe our approach to work well.

PublicationA QUBO Formulation of the kMedoids Problem( 2019)We are concerned with kmedoids clustering and propose aquadratic unconstrained binary optimization (QUBO) formulation of the problem of identifying k medoids among n data points without having to cluster the data. Given our QUBO formulation of this NPhard problem, it should be possible to solve it on adiabatic quantum computers.