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2020 – today
- 2023
[c5]Mehrdad Mohannazadeh Bakhtiari, Daniel Staps, Thomas Villmann:
Learning Vector Quantization in Context of Information Bottleneck Theory. ESANN 2023- [c4]Ronny Schubert, Lynn V. Reuss, Daniel Staps, Marika Kaden, Thomas Villmann, Robert Hasler, Robin Herz, Till Tiemann, Wolfram Richardt:
A White-Box Workflow for the Prediction of Food Content From Near-Infrared Data Based on Fourier-Transformation. WHISPERS 2023: 1-5 - [c3]Daniel Staps, Marika Kaden, Jan Auth, Florian Zaussinger, Thomas Villmann:
Compression of Particle Images for Inspection of Microgravity Experiments by Means of a Symmetric Structural Auto-Encoder. WHISPERS 2023: 1-5 - 2022
- [c2]Thomas Villmann
, Daniel Staps, Jensun Ravichandran, Sascha Saralajew, Michael Biehl, Marika Kaden:
A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation. IDA 2022: 354-364 - [c1]Daniel Staps, Ronny Schubert, Marika Kaden, Alexander Lampe, Wieland Hermann, Thomas Villmann:
Prototype-based One-Class-Classification Learning Using Local Representations. IJCNN 2022: 1-8
2010 – 2019
- 2019
- [i1]Julia Schulte, Daniel Staps, Alexander Lampe:
A feasibility study of deep neural networks for the recognition of banknotes regarding central bank requirements. CoRR abs/1907.07890 (2019)
Coauthor Index
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