University of Münster
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Dr. Marlon Becker

Current affiliations

  • Professorship of Geoinformatics for Sustainable Development (Prof. Risse)
Logo ORCIDID
0000-0002-4908-9232

Academic Education

since 10/2024Master of Arts Philosophy, University of Münster
since 10/2022Master of Arts Philosophy of Science, University of Münster
02/2021 - 03/2025PhD Computer Science, University of Münster
10/2016 - 05/2020Master of Science Chemistry, University of Münster
10/2016 - 09/2019Bachelor of Science Physics, University of Münster
10/2013 - 09/2016Bachelor of Science Chemistry, University of Münster

Publications

Accelerating Finite-Difference Frequency-Domain Simulations for Inverse Design Problems in Nanophotonics using Deep Learning
Schulte L; Butz M; Becker M; Risse B; Schuck C (2024)
In: Journal of the Optical Society of America B, 41(4)
Type of Publication: Research article (journal)
Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks
Tertilt, Henri; Mensing, Jonas; Becker, Marlon; van der Wiel, Wilfred G.; Bobbert, Peter A.; Heuer, Andreas (2024)
In: Physical Review Applied, 22(2)
Type of Publication: Research article (journal)
Probabilistic photonic computing with chaotic light
Brückerhoff-Plückelmann F; Borras H; Klein B; Varri A; Becker M; Dijkstra J; Brückerhoff M; Wright CD; Salinga M; Bhaskaran H; Risse B; Fröning H; Pernice W (2024)
In: Nature Communications, 15(1)
Type of Publication: Research article (journal)
Learning Proposal Distributions in Simulated Annealing via Template Networks: A Case Study in Nanophotonic Inverse Design.
Becker, Marlon; Butz, Marco; Lemli, David; Schuck, Carsten; Risse, Benjamin (2024)
In: Apostolos Antonacopoulos; Subhasis Chaudhuri; Rama Chellappa; Cheng-Lin Liu; Saumik Bhattacharya; Umapada Pal (eds.), Pattern Recognition. Kolkata, India: Springer.
Type of Publication: Research article (book contribution)
Learned Random Label Predictions as a Neural Network Complexity Metric
Becker, Marlon; Risse, Benjamin (2024)
In: Workshop on Scientific Methods for Understanding Deep Learning @NeurIPS , Vancouver, Canada. OpenReview
Type of Publication: Research article in digital collection (conference)
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