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Disambiguation of "Trappenberg, Thomas"

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Self-training for Cell Segmentation and Counting.

, , , , and . Canadian AI, volume 11489 of Lecture Notes in Computer Science, page 406-412. Springer, (2019)

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To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Thomas Trappenberg

Rüdiger Trappenberg

Thomas Thomas

Thomas Harasim

Die Rolle des nucleostemin-Gens in dem Prozess der Ribosomenbiogenese. Uni München, (2009)

Thomas Baron

Untersuchungen an ungeschädigten und durch Pilzbefall geschädigten Nadelholzbauteilen mit ausgewählten Prüfverfahren. TU Dresden, (2009)
 

Other publications of authors with the same name

Characterizing a Brain-Based Value-Function Approximator., and . Canadian AI, volume 6657 of Lecture Notes in Computer Science, page 92-103. Springer, (2011)Self-training for Cell Segmentation and Counting., , , , and . Canadian AI, volume 11489 of Lecture Notes in Computer Science, page 406-412. Springer, (2019)Overlap versus Imbalance., and . Canadian AI, volume 6085 of Lecture Notes in Computer Science, page 220-231. Springer, (2010)Learning Adaptive Weight Masking for Adversarial Examples., , , and . IJCNN, page 1-8. IEEE, (2019)Dynamic Cooperation and Competition in a Network of Spiking Neurons.. ICONIP, page 1299-1302. IOA Press, (1998)Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators., , , , and . NeurIPS, (2022)A Classification Scheme for Applications with Ambiguous Data., and . IJCNN (6), page 296-301. IEEE Computer Society, (2000)0-7695-0619-4.Visualizing Hierarchical Representation in a Multilayered Restricted RBF Network., , and . ICANN, volume 8681 of Lecture Notes in Computer Science, page 339-346. Springer, (2014)Topographic representation adds robustness to supervised learning., and . J. Intell. Fuzzy Syst., 36 (4): 3249-3262 (2019)Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study., , , , , , , , and . Comput. Medical Imaging Graph., (2019)