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researcher

Andreas K. Maier

26 papers hereh-index 13587 citations44 works total

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

author position
  • first author1
  • middle author23

Across the 24 of 26 papers where every author was matched, so the position is known.

fields
  • q-bio.NC11
  • cs.AI3
  • cs.CL3
  • cs.LG3
  • cs.NE2
  • cs.CV1
same name
  • Andreas K. Maier — 22 papers, h 9
  • Andreas K. Maier — 17 papers, h 2
  • Andreas K. Maier — 15 papers, h 3
  • Andreas K. Maier — 10 papers, h 3
  • Andreas K. Maier — 8 papers, h 2
  • Andreas K. Maier — 8 papers, h 2

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

activity
20182026
most citedHow deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks

61 citations · 137 across the 23 of their papers we have counts for

collaborators
Showing cs.LGShow all

3 papers · 1 filter

cs.LG2022★ 1 cited

Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity

Claus Metzner, Achim Schilling, Maximilian Traxdorf +3

Data classification, the process of analyzing data and organizing it into categories, is a fundamental computing problem of natural and artificial information processing systems. I…

cs.LG2021★ 33 cited

Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future

Andreas Maier, Harald Köstler, Marco Heisig +2

In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in…

cs.LG2018★ 61 cited

How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks

Achim Schilling, Claus Metzner, Jonas Rietsch +3

Deep neural networks typically outperform more traditional machine learning models in their ability to classify complex data, and yet is not clear how the individual hidden layers…

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