Fred A. Hamprecht
Fred A. Hamprecht
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Cited by
Cited by
Development and assessment of new exchange-correlation functionals
FA Hamprecht, AJ Cohen, DJ Tozer, NC Handy
The Journal of chemical physics 109 (15), 6264-6271, 1998
Ilastik: Interactive learning and segmentation toolkit
C Sommer, C Straehle, U Koethe, FA Hamprecht
2011 IEEE international symposium on biomedical imaging: From nano to macro …, 2011
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
BH Menze, BM Kelm, R Masuch, U Himmelreich, P Bachert, W Petrich, ...
BMC bioinformatics 10 (1), 1-16, 2009
Ilastik: interactive machine learning for (bio) image analysis
S Berg, D Kutra, T Kroeger, CN Straehle, BX Kausler, C Haubold, ...
Nature Methods 16 (12), 1226-1232, 2019
An objective comparison of cell-tracking algorithms
V Ulman, M Maška, KEG Magnusson, O Ronneberger, C Haubold, ...
Nature methods 14 (12), 1141-1152, 2017
Three-dimensional quantitative similarity− activity relationships (3d qsiar) from seal similarity matrices
H Kubinyi, FA Hamprecht, T Mietzner
Journal of medicinal chemistry 41 (14), 2553-2564, 1998
On oblique random forests
BH Menze, BM Kelm, DN Splitthoff, U Koethe, FA Hamprecht
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011
Learning steerable filters for rotation equivariant cnns
M Weiler, FA Hamprecht, M Storath
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
On the spectral bias of neural networks
N Rahaman, A Baratin, D Arpit, F Draxler, M Lin, F Hamprecht, Y Bengio, ...
International Conference on Machine Learning, 5301-5310, 2019
A comparative study of modern inference techniques for discrete energy minimization problems
J Kappes, B Andres, F Hamprecht, C Schnorr, S Nowozin, D Batra, S Kim, ...
Proceedings of the IEEE conference on computer vision and pattern …, 2013
Learning to count with regression forest and structured labels
L Fiaschi, U Köthe, R Nair, FA Hamprecht
Proceedings of the 21st International Conference on Pattern Recognition …, 2012
A comparative study of modern inference techniques for structured discrete energy minimization problems
JH Kappes, B Andres, FA Hamprecht, C Schnörr, S Nowozin, D Batra, ...
International Journal of Computer Vision 115 (2), 155-184, 2015
Multi-modal brain tumor segmentation using deep convolutional neural networks
G Urban, M Bendszus, F Hamprecht, J Kleesiek
MICCAI BraTS (brain tumor segmentation) challenge. Proceedings, winning …, 2014
Robust prediction of the MASCOT score for an improved quality assessment in mass spectrometric proteomics
T Koenig, BH Menze, M Kirchner, F Monigatti, KC Parker, T Patterson, ...
Journal of proteome research 7 (9), 3708-3717, 2008
Visualizing a homogeneous blend in bulk heterojunction polymer solar cells by analytical electron microscopy
M Pfannmöller, H Flügge, G Benner, I Wacker, C Sommer, ...
Nano letters 11 (8), 3099-3107, 2011
Essentially no barriers in neural network energy landscape
F Draxler, K Veschgini, M Salmhofer, F Hamprecht
International conference on machine learning, 1309-1318, 2018
Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images
A Kreshuk, CN Straehle, C Sommer, U Koethe, M Cantoni, G Knott, ...
PloS one 6 (10), e24899, 2011
Imagining the future of bioimage analysis
E Meijering, AE Carpenter, H Peng, FA Hamprecht, JC Olivo-Marin
Nature biotechnology 34 (12), 1250-1255, 2016
Segmentation of SBFSEM volume data of neural tissue by hierarchical classification
B Andres, U Köthe, M Helmstaedter, W Denk, FA Hamprecht
Joint Pattern Recognition Symposium, 142-152, 2008
Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras
M Frank, M Plaue, H Rapp, U Köthe, B Jähne, FA Hamprecht
Optical Engineering 48 (1), 013602, 2009
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