Zobrazeno 1 - 10
of 2 183
pro vyhledávání: '"Wichert, P."'
Autor:
Kerstin Lang, Christina U. Köhler, Katharina Wichert, Thomas Deix, Georg Bartsch, Gudrun Sommer, Christiane Lübke, Florian Roghmann, Moritz J. Reike, Harald Krentel, Katja Engellandt, Sven Schiermeier, Valentin Menke, Joachim Noldus, Thomas Behrens, Thomas Brüning, Heiko U. Käfferlein
Publikováno v:
Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-16 (2024)
Abstract Background For more than 80 years, cystoscopy has been the gold standard for identification of urothelial carcinoma (UCa). Because of many factors, such as pain of the patients during this procedure or the costs involved, non-invasive detect
Externí odkaz:
https://doaj.org/article/41b44147a5364a0ab27d2909476f4645
Autor:
Sacouto, Luis, Wichert, Andreas
One of the most well established brain principles, hebbian learning, has led to the theoretical concept of neural assemblies. Based on it, many interesting brain theories have spawned. Palm's work implements this concept through binary associative me
Externí odkaz:
http://arxiv.org/abs/2301.02196
Autor:
J.S. Sakamoto, L.E. Lopes-Santos, K.J.C.C. de Lacerda, A.C. Trevisan, L. Alexandre-Santos, O.Y. Fukumori, F. Bellissimo-Rodrigues, L. Wichert-Ana
Publikováno v:
Brazilian Journal of Medical and Biological Research, Vol 57 (2024)
COVID-19, caused by SARS-CoV-2, presents diverse symptoms, including neurological manifestations. This study investigated COVID-19's neurological sequelae, focusing on the central nervous system's involvement through cerebral glycolytic metabolism as
Externí odkaz:
https://doaj.org/article/55c274bbefdd4000817e5050c59a7228
Autor:
Julia Kasprzak, Timothy Goering, Karin Berger-Thürmel, Vanessa Kratzer, Wuthichai Prompinit, Sven P. Wichert, Simon Leutner, Norbert Langermann, Michael von Bergwelt-Baildon, Volker Heinemann, Hana Algül, Martin Zünkeler, Daniel Nasseh
Publikováno v:
Digital Health, Vol 10 (2024)
Objectives The treatment of rare tumors often necessitates the involvement of highly specialized teams, typically based in larger medical centers or university hospitals, which are often lacking in rural areas. The German TARGET (the Trans-sectoral P
Externí odkaz:
https://doaj.org/article/8edf269f97ab4f56acc84ddeb931d70b
When several models have similar training scores, classical model selection heuristics follow Occam's razor and advise choosing the ones with least capacity. Yet, modern practice with large neural networks has often led to situations where two networ
Externí odkaz:
http://arxiv.org/abs/2211.14347
The theory of bias-variance used to serve as a guide for model selection when applying Machine Learning algorithms. However, modern practice has shown success with over-parameterized models that were expected to overfit but did not. This led to the p
Externí odkaz:
http://arxiv.org/abs/2211.10322
A vast majority of the current research in the field of Machine Learning is done using algorithms with strong arguments pointing to their biological implausibility such as Backpropagation, deviating the field's focus from understanding its original o
Externí odkaz:
http://arxiv.org/abs/2210.14855
To economically deploy robotic manipulators the programming and execution of robot motions must be swift. To this end, we propose a novel, constraint-based method to intuitively specify sequential manipulation tasks and to compute time-optimal robot
Externí odkaz:
http://arxiv.org/abs/2208.09219
Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?
It is generally assumed that the brain uses something akin to sparse distributed representations. These representations, however, are high-dimensional and consequently they affect classification performance of traditional Machine Learning models due
Externí odkaz:
http://arxiv.org/abs/2208.12564
Drawing from memory the face of a friend you have not seen in years is a difficult task. However, if you happen to cross paths, you would easily recognize each other. The biological memory is equipped with an impressive compression algorithm that can
Externí odkaz:
http://arxiv.org/abs/2207.04827