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ORCIDFred A. Hamprecht
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- affiliation: University of Heidelberg, Interdisciplinary Center for Scientific Computing, Germany
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2020 – today
- 2025
[i38]Roman Remme, Tobias Kaczun, Tim Ebert, Christof A. Gehrig, Dominik Geng, Gerrit Gerhartz, Marc K. Ickler, Manuel V. Klockow, Peter Lippmann, Johannes S. Schmidt, Simon Wagner, Andreas Dreuw, Fred A. Hamprecht:
Stable and Accurate Orbital-Free DFT Powered by Machine Learning. CoRR abs/2503.00443 (2025)- 2024
- [c102]Moritz Sturm
, Lorenzo Cerrone, Fred A. Hamprecht:
SynCellFactory: Generative Data Augmentation for Cell Tracking. MICCAI (12) 2024: 304-313 - [c101]Lennart Bürger, Fred A. Hamprecht, Boaz Nadler:
Truth is Universal: Robust Detection of Lies in LLMs. NeurIPS 2024 - [i37]Enrique Fita Sanmartín, Christoph Schnörr, Fred A. Hamprecht:
The Central Spanning Tree Problem. CoRR abs/2404.06447 (2024) - [i36]Moritz Sturm, Lorenzo Cerrone, Fred A. Hamprecht:
SynCellFactory: Generative Data Augmentation for Cell Tracking. CoRR abs/2404.16421 (2024) - [i35]Peter Lippmann, Gerrit Gerhartz, Roman Remme, Fred A. Hamprecht:
Tensor Frames - How To Make Any Message Passing Network Equivariant. CoRR abs/2405.15389 (2024) - [i34]Lennart Bürger, Fred A. Hamprecht, Boaz Nadler:
Truth is Universal: Robust Detection of Lies in LLMs. CoRR abs/2407.12831 (2024) - [i33]Enrique Fita Sanmartín, Christoph Schnörr, Fred A. Hamprecht:
Expectation and Variance of the Degree of a Node in Random Spanning Trees. CoRR abs/2409.13472 (2024) - [i32]Dmitry Kobak, Fred A. Hamprecht, Smita Krishnaswamy, Gal Mishne, Sebastian Damrich:
Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122). Dagstuhl Reports 14(3): 92-115 (2024) - 2023
- [c100]Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak:
From $t$-SNE to UMAP with contrastive learning. ICLR 2023 - [c99]Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht:
Geometric Autoencoders - What You See is What You Decode. ICML 2023: 25834-25857 - [i31]Roman Remme, Tobias Kaczun, Maximilian Scheurer, Andreas Dreuw, Fred A. Hamprecht:
KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory. CoRR abs/2305.13316 (2023) - [i30]Philipp Nazari, Sebastian Damrich, Fred A. Hamprecht:
Geometric Autoencoders - What You See is What You Decode. CoRR abs/2306.17638 (2023) - 2022
- [j26]Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, Laurent Najman, Fred A. Hamprecht:
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. Bioinform. 38(Supplement_1): i316-i324 (2022) - [c98]Alberto Bailoni, Constantin Pape, Nathan Hütsch, Steffen Wolf, Thorsten Beier, Anna Kreshuk, Fred A. Hamprecht:
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation. CVPR 2022: 11635-11645 - [c97]Lorenzo Cerrone, Athul Vijayan, Tejasvinee Mody, Kay Schneitz, Fred A. Hamprecht:
CellTypeGraph: A New Geometric Computer Vision Benchmark. CVPR 2022: 20865-20875 - [c96]Enrique Fita Sanmartín, Sebastian Damrich, Fred A. Hamprecht:
The Algebraic Path Problem for Graph Metrics. ICML 2022: 19178-19204 - [c95]Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht:
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources. NeurIPS 2022 - [i29]Lorenzo Cerrone, Athul Vijayan, Tejasvinee Mody, Kay Schneitz, Fred A. Hamprecht:
CellTypeGraph: A New Geometric Computer Vision Benchmark. CoRR abs/2205.08166 (2022) - [i28]Sebastian Damrich, Jan Niklas Böhm, Fred A. Hamprecht, Dmitry Kobak:
Contrastive learning unifies t-SNE and UMAP. CoRR abs/2206.01816 (2022) - [i27]Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht:
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources. CoRR abs/2210.07702 (2022) - 2021
- [j25]Steffen Wolf, Alberto Bailoni, Constantin Pape, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht:
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning. IEEE Trans. Pattern Anal. Mach. Intell. 43(10): 3724-3738 (2021) - [c94]Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht:
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice. ICCV 2021: 4582-4591 - [c93]Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht:
Multistar: Instance Segmentation Of Overlapping Objects With Star-Convex Polygons. ISBI 2021: 295-298 - [c92]Sebastian Damrich, Fred A. Hamprecht:
On UMAP's True Loss Function. NeurIPS 2021: 5798-5809 - [c91]Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht:
Directed Probabilistic Watershed. NeurIPS 2021: 20076-20088 - [i26]Quentin Garrido, Sebastian Damrich, Alexander Jäger, Dario Cerletti, Manfred Claassen, Laurent Najman, Fred A. Hamprecht:
Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. CoRR abs/2102.05892 (2021) - [i25]Sebastian Damrich, Fred A. Hamprecht:
UMAP does not reproduce high-dimensional similarities due to negative sampling. CoRR abs/2103.14608 (2021) - [i24]Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht:
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice. CoRR abs/2110.02750 (2021) - 2020
- [j24]Thomas M. Hehn, Julian F. P. Kooij, Fred A. Hamprecht:
End-to-End Learning of Decision Trees and Forests. Int. J. Comput. Vis. 128(4): 997-1011 (2020) - [c90]Steffen Wolf, Fred A. Hamprecht, Jan Funke:
Inpainting Networks Learn to Separate Cells in Microscopy Images. BMVC 2020 - [c89]Alberto Bailoni, Constantin Pape, Steffen Wolf, Anna Kreshuk, Fred A. Hamprecht:
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks. GCPR 2020: 331-344 - [c88]Steffen Wolf, Yuyan Li, Constantin Pape, Alberto Bailoni, Anna Kreshuk, Fred A. Hamprecht:
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation. ECCV (6) 2020: 208-224 - [c87]Elke Kirschbaum, Alberto Bailoni, Fred A. Hamprecht:
DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging. MICCAI (5) 2020: 151-162 - [i23]Steffen Wolf, Fred A. Hamprecht, Jan Funke:
Instance Separation Emerges from Inpainting. CoRR abs/2003.00891 (2020) - [i22]Alberto Bailoni, Constantin Pape, Steffen Wolf, Anna Kreshuk, Fred A. Hamprecht:
Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks. CoRR abs/2009.04998 (2020) - [i21]Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht:
MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons. CoRR abs/2011.13228 (2020)
2010 – 2019
- 2019
- [c86]Lorenzo Cerrone, Alexander Zeilmann, Fred A. Hamprecht:
End-To-End Learned Random Walker for Seeded Image Segmentation. CVPR 2019: 12559-12568 - [c85]Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A. Hamprecht:
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR (Poster) 2019 - [c84]Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville:
On the Spectral Bias of Neural Networks. ICML 2019: 5301-5310 - [c83]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Deep Active Learning with Adaptive Acquisition. IJCAI 2019: 2470-2476 - [c82]Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht:
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. NeurIPS 2019: 2776-2787 - [c81]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI 2019: 563-573 - [i20]Steffen Wolf, Alberto Bailoni, Constantin Pape, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht:
The Mutex Watershed and its Objective: Efficient, Parameter-Free Image Partitioning. CoRR abs/1904.12654 (2019) - [i19]Lorenzo Cerrone, Alexander Zeilmann, Fred A. Hamprecht:
End-to-End Learned Random Walker for Seeded Image Segmentation. CoRR abs/1905.09045 (2019) - [i18]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Deep Active Learning with Adaptive Acquisition. CoRR abs/1906.11471 (2019) - [i17]Alberto Bailoni, Constantin Pape, Steffen Wolf, Thorsten Beier, Anna Kreshuk, Fred A. Hamprecht:
A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation. CoRR abs/1906.11713 (2019) - [i16]Elke Kirschbaum, Alberto Bailoni, Fred A. Hamprecht:
DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging Analysis. CoRR abs/1908.07957 (2019) - [i15]Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht:
Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. CoRR abs/1911.02921 (2019) - [i14]Steffen Wolf, Yuyan Li, Constantin Pape, Alberto Bailoni, Anna Kreshuk, Fred A. Hamprecht:
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation. CoRR abs/1912.12717 (2019) - 2018
- [j23]Virginie Uhlmann, Carsten Haubold, Fred A. Hamprecht, Michael Unser:
DiversePathsJ: diverse shortest paths for bioimage analysis. Bioinform. 34(3): 538-540 (2018) - [j22]Nikola Krasowski, Thorsten Beier, Graham Knott, Ullrich Köthe, Fred A. Hamprecht, Anna Kreshuk:
Neuron Segmentation With High-Level Biological Priors. IEEE Trans. Medical Imaging 37(4): 829-839 (2018) - [c80]Maurice Weiler, Fred A. Hamprecht, Martin Storath:
Learning Steerable Filters for Rotation Equivariant CNNs. CVPR 2018: 849-858 - [c79]Thomas M. Hehn, Fred A. Hamprecht:
End-to-End Learning of Deterministic Decision Trees. GCPR 2018: 612-627 - [c78]Steffen Wolf, Constantin Pape, Alberto Bailoni, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht:
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV (4) 2018: 571-587 - [c77]Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht:
Essentially No Barriers in Neural Network Energy Landscape. ICML 2018: 1308-1317 - [i13]Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht:
Essentially No Barriers in Neural Network Energy Landscape. CoRR abs/1803.00885 (2018) - [i12]Carsten Haubold, Virginie Uhlmann, Michael Unser, Fred A. Hamprecht:
Diverse M-Best Solutions by Dynamic Programming. CoRR abs/1803.05748 (2018) - [i11]Melih Kandemir, Manuel Haußmann, Fred A. Hamprecht:
Sampling-Free Variational Inference of Bayesian Neural Nets. CoRR abs/1805.07654 (2018) - [i10]Nasim Rahaman, Devansh Arpit, Aristide Baratin, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville:
On the Spectral Bias of Deep Neural Networks. CoRR abs/1806.08734 (2018) - 2017
- [c76]Manuel Haußmann, Fred A. Hamprecht, Melih Kandemir:
Variational Bayesian Multiple Instance Learning with Gaussian Processes. CVPR 2017: 810-819 - [c75]Carsten Haubold, Virginie Uhlmann, Michael Unser, Fred A. Hamprecht:
Diverse M-Best Solutions by Dynamic Programming. GCPR 2017: 255-267 - [c74]Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht:
Learned Watershed: End-to-End Learning of Seeded Segmentation. ICCV 2017: 2030-2038 - [c73]Sven Peter, Ferran Diego, Fred A. Hamprecht, Boaz Nadler:
Cost efficient gradient boosting. NIPS 2017: 1551-1561 - [c72]Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht:
Sparse convolutional coding for neuronal assembly detection. NIPS 2017: 3675-3685 - [i9]Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred A. Hamprecht:
Learned Watershed: End-to-End Learning of Seeded Segmentation. CoRR abs/1704.02249 (2017) - [i8]Maurice Weiler, Fred A. Hamprecht, Martin Storath:
Learning Steerable Filters for Rotation Equivariant CNNs. CoRR abs/1711.07289 (2017) - [i7]Thomas Hehn, Fred A. Hamprecht:
End-to-end Learning of Deterministic Decision Trees. CoRR abs/1712.02743 (2017) - 2016
- [c71]Melih Kandemir, Manuel Haußmann, Ferran Diego, Kumar T. Rajamani, Jeroen van der Laak, Fred A. Hamprecht:
Variational Weakly Supervised Gaussian Processes. BMVC 2016 - [c70]Ferran Diego, Fred A. Hamprecht:
Structured Regression Gradient Boosting. CVPR 2016: 1459-1467 - [c69]Matthias von Borstel, Melih Kandemir, Philip Schmidt, Madhavi K. Rao, Kumar T. Rajamani, Fred A. Hamprecht:
Gaussian Process Density Counting from Weak Supervision. ECCV (1) 2016: 365-380 - [c68]Carsten Haubold, Janez Ales, Steffen Wolf, Fred A. Hamprecht:
A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets. ECCV (7) 2016: 566-582 - [c67]Martin Schiegg, Ferran Diego, Fred A. Hamprecht:
Learning Diverse Models: The Coulomb Structured Support Vector Machine. ECCV (3) 2016: 585-599 - [c66]Thorsten Beier, Björn Andres, Ullrich Köthe, Fred A. Hamprecht:
An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem. ECCV (2) 2016: 715-730 - 2015
- [j21]Martin Schiegg, Philipp Hanslovsky, Carsten Haubold, Ullrich Köthe, Lars Hufnagel, Fred A. Hamprecht:
Graphical model for joint segmentation and tracking of multiple dividing cells. Bioinform. 31(6): 948-956 (2015) - [j20]Melih Kandemir, Fred A. Hamprecht:
Computer-aided diagnosis from weak supervision: A benchmarking study. Comput. Medical Imaging Graph. 42: 44-50 (2015) - [j19]Jörg H. Kappes, Björn Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother:
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. Int. J. Comput. Vis. 115(2): 155-184 (2015) - [c65]Thorsten Beier, Fred A. Hamprecht, Jörg H. Kappes:
Fusion moves for correlation clustering. CVPR 2015: 3507-3516 - [c64]Martin Schiegg, Ben Heuer, Carsten Haubold, Steffen Wolf, Ullrich Köthe, Fred A. Hamprecht:
Proof-reading guidance in cell tracking by sampling from tracking-by-assignment models. ISBI 2015: 394-398 - [c63]Nikola Krasowski, Thorsten Beier, Graham W. Knott, Ullrich Köthe, Fred A. Hamprecht, Anna Kreshuk:
Improving 3D EM data segmentation by joint optimization over boundary evidence and biological priors. ISBI 2015: 536-539 - [c62]Jan Funke, Fred A. Hamprecht, Chong Zhang:
Learning to Segment: Training Hierarchical Segmentation under a Topological Loss. MICCAI (3) 2015: 268-275 - [c61]Melih Kandemir, Christian Wojek, Fred A. Hamprecht:
Cell Event Detection in Phase-Contrast Microscopy Sequences from Few Annotations. MICCAI (3) 2015: 316-323 - [c60]Anna Kreshuk, Jan Funke, Albert Cardona, Fred A. Hamprecht:
Who Is Talking to Whom: Synaptic Partner Detection in Anisotropic Volumes of Insect Brain. MICCAI (1) 2015: 661-668 - [c59]Melih Kandemir, Fred A. Hamprecht:
The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors. FE@NIPS 2015: 145-159 - 2014
- [j18]Xinghua Lou, Martin Schiegg, Fred A. Hamprecht:
Active Structured Learning for Cell Tracking: Algorithm, Framework, and Usability. IEEE Trans. Medical Imaging 33(4): 849-860 (2014) - [c58]Thorsten Beier, Thorben Kröger, Jörg H. Kappes, Ullrich Köthe, Fred A. Hamprecht:
Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning. CVPR 2014: 73-80 - [c57]Luca Fiaschi, Ferran Diego Andilla, Konstantin Gregor, Martin Schiegg, Ullrich Köthe, Marta Zlatic, Fred A. Hamprecht:
Tracking Indistinguishable Translucent Objects over Time Using Weakly Supervised Structured Learning. CVPR 2014: 2736-2743 - [c56]Amnon Drory, Carsten Haubold, Shai Avidan, Fred A. Hamprecht:
Semi-Global Matching: A Principled Derivation in Terms of Message Passing. GCPR 2014: 43-53 - [c55]Thorben Kröger, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht:
Asymmetric Cuts: Joint Image Labeling and Partitioning. GCPR 2014: 199-211 - [c54]Christoph N. Straehle, Melih Kandemir, Ullrich Köthe, Fred A. Hamprecht:
Multiple Instance Learning with Response-Optimized Random Forests. ICPR 2014: 3768-3773 - [c53]Faik Boray Tek, Thorben Kröger, Shawn Mikula, Fred A. Hamprecht:
Automated cell nucleus detection for large-volume electron microscopy of neural tissue. ISBI 2014: 69-72 - [c52]Chong Zhang, Florian Huber, Michael Knop, Fred A. Hamprecht:
Yeast cell detection and segmentation in bright field microscopy. ISBI 2014: 1267-1270 - [c51]Melih Kandemir, Annette Feuchtinger, Axel Walch, Fred A. Hamprecht:
Digital pathology: Multiple instance learning can detect Barrett's cancer. ISBI 2014: 1348-1351 - [c50]Chong Zhang, Julian Yarkony, Fred A. Hamprecht:
Cell Detection and Segmentation Using Correlation Clustering. MICCAI (1) 2014: 9-16 - [c49]Melih Kandemir, José C. Rubio, Ute Schmidt, Christian Wojek, Johannes Welbl, Björn Ommer, Fred A. Hamprecht:
Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures. MICCAI (2) 2014: 154-161 - [c48]Melih Kandemir, Chong Zhang, Fred A. Hamprecht:
Empowering Multiple Instance Histopathology Cancer Diagnosis by Cell Graphs. MICCAI (2) 2014: 228-235 - [c47]Ferran Diego Andilla, Fred A. Hamprecht:
Sparse Space-Time Deconvolution for Calcium Image Analysis. NIPS 2014: 64-72 - [c46]Julian Yarkony, Thorsten Beier, Pierre Baldi, Fred A. Hamprecht:
Parallel Multicut Segmentation via Dual Decomposition. NFMCP 2014: 56-68 - [c45]Melih Kandemir, Fred A. Hamprecht:
Instance Label Prediction by Dirichlet Process Multiple Instance Learning. UAI 2014: 380-389 - [i6]Jörg H. Kappes, Björn Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother:
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR abs/1404.0533 (2014) - 2013
- [j17]Mario Frank, Fred A. Hamprecht:
Image-based supervision of a periodically working machine. Pattern Anal. Appl. 16(3): 407-416 (2013) - [c44]Jörg H. Kappes, Björn Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Jan Lellmann, Nikos Komodakis, Carsten Rother:
A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems. CVPR 2013: 1328-1335 - [c43]Christoph N. Straehle, Sven Peter, Ullrich Köthe, Fred A. Hamprecht:
K-Smallest Spanning Tree Segmentations. GCPR 2013: 375-384 - [c42]Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht:
Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria. ICCV 2013: 1849-1856 - [c41]Martin Schiegg, Philipp Hanslovsky, Bernhard X. Kausler, Lars Hufnagel, Fred A. Hamprecht:
Conservation Tracking. ICCV 2013: 2928-2935 - [c40]Luca Fiaschi, Gregor Konstantin, Bruno Afonso, Marta Zlatic, Fred A. Hamprecht:
Keeping count: Leveraging temporal context to count heavily overlapping objects. ISBI 2013: 656-659 - [c39]Ferran Diego Andilla, Susanne Reichinnek, Martin Both, Fred A. Hamprecht:
Automated identification of neuronal activity from calcium imaging by sparse dictionary learning. ISBI 2013: 1058-1061 - [c38]Thorben Kröger, Shawn Mikula, Winfried Denk, Ullrich Köthe, Fred A. Hamprecht:
Learning to Segment Neurons with Non-local Quality Measures. MICCAI (2) 2013: 419-427 - [c37]Ferran Diego Andilla, Fred A. Hamprecht:
Learning Multi-level Sparse Representations. NIPS 2013: 818-826 - 2012
- [j16]Björn Andres, Ullrich Köthe, Thorben Kröger, Moritz Helmstaedter, Kevin L. Briggman, Winfried Denk, Fred A. Hamprecht:
3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries. Medical Image Anal. 16(4): 796-805 (2012) - [c36]Christoph N. Straehle, Ullrich Köthe, Graham Knott, Kevin L. Briggman, Winfried Denk, Fred A. Hamprecht:
Seeded watershed cut uncertainty estimators for guided interactive segmentation. CVPR 2012: 765-772 - [c35]Jan Funke, Björn Andres, Fred A. Hamprecht, Albert Cardona, Matthew Cook:
Efficient automatic 3D-reconstruction of branching neurons from EM data. CVPR 2012: 1004-1011 - [c34]Xinghua Lou, Ullrich Köthe, Jochen Wittbrodt, Fred A. Hamprecht:
Learning to segment dense cell nuclei with shape prior. CVPR 2012: 1012-1018 - [c33]Bernhard X. Kausler, Martin Schiegg, Björn Andres, Martin S. Lindner, Ullrich Köthe, Heike Leitte, Jochen Wittbrodt, Lars Hufnagel, Fred A. Hamprecht:
A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness. ECCV (3) 2012: 144-157 - [c32]Björn Andres, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht:
The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models. ECCV (7) 2012: 154-166 - [c31]Björn Andres, Thorben Kröger, Kevin L. Briggman, Winfried Denk, Natalya Korogod, Graham Knott, Ullrich Köthe, Fred A. Hamprecht:
Globally Optimal Closed-Surface Segmentation for Connectomics. ECCV (3) 2012: 778-791 - [c30]Xinghua Lou, Fred A. Hamprecht:
Structured Learning from Partial Annotations. ICML 2012 - [c29]Christoph Sommer, Luca Fiaschi, Fred A. Hamprecht, Daniel Gerlich:
Learning-based mitotic cell detection in histopathological images. ICPR 2012: 2306-2309 - [c28]Luca Fiaschi, Ullrich Köthe, Rahul Nair, Fred A. Hamprecht:
Learning to count with regression forest and structured labels. ICPR 2012: 2685-2688 - [c27]Xinghua Lou, Luca Fiaschi, Ullrich Köthe, Fred A. Hamprecht:
Quality Classification of Microscopic Imagery with Weakly Supervised Learning. MLMI 2012: 176-183 - [c26]Jens Röder, Boaz Nadler, Kevin Kunzmann, Fred A. Hamprecht:
Active Learning with Distributional Estimates. UAI 2012: 715-725 - [i5]Jens Röder, Boaz Nadler, Kevin Kunzmann, Fred A. Hamprecht:
Active Learning with Distributional Estimates. CoRR abs/1210.4909 (2012) - 2011
- [j15]Björn Voss, Michael Hanselmann, Bernhard Y. Renard, Martin S. Lindner, Ullrich Köthe, Marc Kirchner, Fred A. Hamprecht:
SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists. Bioinform. 27(7): 987-993 (2011) - [j14]Frederik O. Kaster, Bernd Merkel, Oliver Nix, Fred A. Hamprecht:
An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements. Comput. Sci. Res. Dev. 26(1-2): 65-85 (2011) - [c25]Björn Andres, Jörg H. Kappes, Thorsten Beier, Ullrich Köthe, Fred A. Hamprecht:
Probabilistic image segmentation with closedness constraints. ICCV 2011: 2611-2618 - [c24]Anna Kreshuk, Christoph N. Straehle, Christoph Sommer, Ullrich Köthe, Graham Knott, Fred A. Hamprecht:
Automated segmentation of synapses in 3D EM data. ISBI 2011: 220-223 - [c23]Christoph Sommer, Christoph N. Straehle, Ullrich Köthe, Fred A. Hamprecht:
Ilastik: Interactive learning and segmentation toolkit. ISBI 2011: 230-233 - [c22]Xinghua Lou, Frederik O. Kaster, Martin S. Lindner, Bernhard X. Kausler, Ullrich Köthe, Burkhard Hockendorf, Jochen Wittbrodt, Heike Jänicke, Fred A. Hamprecht:
Deltr: Digital embryo lineage tree reconstructor. ISBI 2011: 1557-1560 - [c21]Christoph N. Straehle, Ullrich Köthe, Graham Knott, Fred A. Hamprecht:
Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images. MICCAI (1) 2011: 653-660 - [c20]Xinghua Lou, Fred A. Hamprecht:
Structured Learning for Cell Tracking. NIPS 2011: 1296-1304 - [c19]Bjoern H. Menze, B. Michael Kelm, Daniel Nicolas Splitthoff, Ullrich Köthe, Fred A. Hamprecht:
On Oblique Random Forests. ECML/PKDD (2) 2011: 453-469 - [c18]Sven Wanner, Christoph Sommer, Roland Rocholz, Michael Jung, Fred A. Hamprecht, Bernd Jähne:
A Framework for Interactive Visualization and Classification of Dynamical Processes at the Water Surface. VMV 2011: 199-206 - [i4]Jan Funke, Björn Andres, Fred A. Hamprecht, Albert Cardona, Matthew Cook:
Multi-Hypothesis CRF-Segmentation of Neural Tissue in Anisotropic EM Volumes. CoRR abs/1109.2449 (2011) - 2010
- [j13]Marc Kirchner, Bernhard Y. Renard, Ullrich Köthe, Darryl J. Pappin, Fred A. Hamprecht, Hanno Steen, Judith A. J. Steen:
Computational protein profile similarity screening for quantitative mass spectrometry experiments. Bioinform. 26(1): 77-83 (2010) - [j12]Xinghua Lou, Marc Kirchner, Bernhard Y. Renard, Ullrich Köthe, Sebastian Boppel, Christian Graf, Chung-Tien Lee, Judith A. J. Steen, Hanno Steen, Matthias P. Mayer, Fred A. Hamprecht:
Deuteration distribution estimation with improved sequence coverage for HX/MS experiments. Bioinform. 26(12): 1535-1541 (2010) - [c17]Frederik O. Kaster, Stephan Kassemeyer, Bernd Merkel, Oliver Nix, Fred A. Hamprecht:
An Object-oriented Library for Systematic Training and Comparison of Classifiers for Computer-assisted Tumor Diagnosis from MRSI Measurements. Bildverarbeitung für die Medizin 2010: 97-101 - [c16]Björn Andres, Jörg H. Kappes, Ullrich Köthe, Christoph Schnörr, Fred A. Hamprecht:
An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. DAGM-Symposium 2010: 353-362 - [c15]Frederik O. Kaster, Bjoern H. Menze, Marc-André Weber, Fred A. Hamprecht:
Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations. MCV 2010: 74-85 - [c14]Ullrich Köthe, Björn Andres, Thorben Kröger, Fred A. Hamprecht:
Geometric Analysis of 3D Electron Microscopy Data. WADGMM 2010: 93-108 - [i3]Björn Andres, Ullrich Köthe, Thorben Kröger, Fred A. Hamprecht:
Runtime-Flexible Multi-dimensional Arrays and Views for C++98 and C++0x. CoRR abs/1008.2909 (2010) - [i2]Björn Andres, Jörg H. Kappes, Ullrich Köthe, Fred A. Hamprecht:
The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search. CoRR abs/1009.4102 (2010) - [i1]Björn Andres, Ullrich Köthe, Thorben Kröger, Fred A. Hamprecht:
How to Extract the Geometry and Topology from Very Large 3D Segmentations. CoRR abs/1009.6215 (2010)
2000 – 2009
- 2009
- [j11]Bjoern H. Menze, B. Michael Kelm, Ralf Masuch, Uwe Himmelreich, Peter Bachert, Wolfgang Petrich, Fred A. Hamprecht:
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform. 10 (2009) - [j10]Stefan Trittler, Fred A. Hamprecht:
Near optimum sampling design and an efficient algorithm for single tone frequency estimation. Digit. Signal Process. 19(4): 628-639 (2009) - [j9]Mark Jäger, Fred A. Hamprecht:
Principal Component Imagery for the Quality Monitoring of Dynamic Laser Welding Processes. IEEE Trans. Ind. Electron. 56(4): 1307-1313 (2009) - [j8]B. Michael Kelm, Bjoern H. Menze, Oliver Nix, Christian M. Zechmann, Fred A. Hamprecht:
Estimating Kinetic Parameter Maps From Dynamic Contrast-Enhanced MRI Using Spatial Prior Knowledge. IEEE Trans. Medical Imaging 28(10): 1534-1547 (2009) - [c13]Björn Andres, Ullrich Köthe, Andreea Bonea, Boaz Nadler, Fred A. Hamprecht:
Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times. DAGM-Symposium 2009: 502-511 - [c12]Michael Hanselmann, Ullrich Köthe, Bernhard Y. Renard, Marc Kirchner, Ron M. A. Heeren, Fred A. Hamprecht:
Multivariate Watershed Segmentation of Compositional Data. DGCI 2009: 180-192 - 2008
- [j7]Bernhard Y. Renard, Marc Kirchner, Hanno Steen, Judith A. J. Steen, Fred A. Hamprecht:
NITPICK: peak identification for mass spectrometry data. BMC Bioinform. 9 (2008) - [j6]H. Rapp, Mario Frank, Fred A. Hamprecht, Bernd Jähne:
A theoretical and experimental investigation of the systematic errors and statistical uncertainties of Time-Of-Flight-cameras. Int. J. Intell. Syst. Technol. Appl. 5(3/4): 402-413 (2008) - [j5]Mark Jäger, Silke Humbert, Fred A. Hamprecht:
Sputter Tracking for the Automatic Monitoring of Industrial Laser-Welding Processes. IEEE Trans. Ind. Electron. 55(5): 2177-2184 (2008) - [j4]Mark Jäger, Christian Knoll, Fred A. Hamprecht:
Weakly Supervised Learning of a Classifier for Unusual Event Detection. IEEE Trans. Image Process. 17(9): 1700-1708 (2008) - [c11]Björn Andres, Claudia Kondermann, Daniel Kondermann, Ullrich Köthe, Fred A. Hamprecht, Christoph S. Garbe:
On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation. CVPR 2008 - [c10]Björn Andres, Ullrich Köthe, Moritz Helmstaedter, Winfried Denk, Fred A. Hamprecht:
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification. DAGM-Symposium 2008: 142-152 - 2007
- [c9]Björn Andres, Fred A. Hamprecht, Christoph S. Garbe:
Selection of Local Optical Flow Models by Means of Residual Analysis. DAGM-Symposium 2007: 72-81 - [c8]L. Görlitz, Bjoern H. Menze, Marc-André Weber, B. Michael Kelm, Fred A. Hamprecht:
Semi-supervised Tumor Detection in Magnetic Resonance Spectroscopic Images Using Discriminative Random Fields. DAGM-Symposium 2007: 224-233 - [e1]Fred A. Hamprecht, Christoph Schnörr, Bernd Jähne:
Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings. Lecture Notes in Computer Science 4713, Springer 2007, ISBN 978-3-540-74933-2 [contents] - 2006
- [c7]Bjoern H. Menze, B. Michael Kelm, Daniel Heck, Matthias Lichy, Fred A. Hamprecht:
Machine-Based Rejection of Low-Quality Spectra and Estimation of Brain Tumor Probabilities from Magnetic Resonance Spectroscopic Images. Bildverarbeitung für die Medizin 2006: 31-35 - [c6]B. Michael Kelm, Bjoern H. Menze, Thomas Neff, Christian M. Zechmann, Fred A. Hamprecht:
CLARET: A Tool for Fully Automated Evaluation of MRSI with Pattern Recognition Methods. Bildverarbeitung für die Medizin 2006: 51-55 - [c5]B. Michael Kelm, Natalie Mueller, Bjoern H. Menze, Fred A. Hamprecht:
Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM. CVPR Workshops 2006: 96 - [c4]Bjoern H. Menze, B. Michael Kelm, Fred A. Hamprecht:
From Eigenspots to Fisherspots - Latent Spaces in the Nonlinear Detection of Spot Patterns in a Highly Varying Background. GfKl 2006: 255-262 - 2005
- [j3]Hans R. Künsch, Erik Agrell, Fred A. Hamprecht:
Optimal lattices for sampling. IEEE Trans. Inf. Theory 51(2): 634-647 (2005) - 2004
- [c3]Sören Hader, Fred A. Hamprecht:
Two-stage Classification with Automatic Feature Selection for an Industrial Application. GfKl 2004: 137-144 - [c2]Bjoern H. Menze, Michael Wormit, Peter Bachert, Matthias Lichy, Heinz-Peter Schlemmer, Fred A. Hamprecht:
Classification of In Vivo Magnetic Resonance Spectra. GfKl 2004: 362-369 - 2002
- [j2]Fred A. Hamprecht, Walter Thiel, Wilfred F. van Gunsteren:
Chemical Library Subset Selection Algorithms: A Unified Derivation Using Spatial Statistics. J. Chem. Inf. Comput. Sci. 42(2): 414-428 (2002) - [c1]Heiko Eisele, Fred A. Hamprecht:
A New Approach for Defect Detection in X-ray CT Images. DAGM-Symposium 2002: 345-352
1990 – 1999
- 1998
- [j1]Michael Bühl, Fred A. Hamprecht:
Theoretical investigations of NMR chemical shifts and reactivities of oxovanadium(v) compounds. J. Comput. Chem. 19(2): 113-122 (1998)
Coauthor Index
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