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researcher

Jan Disselhoff

2 papers hereh-index 215 citations5 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author1

Across the 2 of 2 papers where every author was matched, so the position is known.

fields
  • cond-mat.stat-mech1
  • cs.LG1
same name
  • Jan Disselhoff — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedNonlinear Advantage: Trained Networks Might Not Be As Complex as You Think

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cond-mat.stat-mech2026

Reconstruction of spin structures from topological charge distributions via generative neural network systems

Kyra H. M. Klos, Jan Disselhoff, Michael Wand +2

Localized topological defects inherently possess a multiscale character. While their microstructure configuration depends on the specific physical system, their topological feature…

cs.LG2022★ 1 cited

Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think

Christian H. X. Ali Mehmeti-Göpel, Jan Disselhoff

We perform an empirical study of the behaviour of deep networks when fully linearizing some of its feature channels through a sparsity prior on the overall number of nonlinear unit…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.