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Fred A. Hamprecht

HCI/IWR at Heidelberg University

Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice


Oct 05, 2021
Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht

* Oral at ICCV 2021 

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On UMAP's true loss function


Apr 22, 2021
Sebastian Damrich, Fred A. Hamprecht

* 20 pages, 15 figures; minor changes, added run-times and error bars 

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UMAP does not reproduce high-dimensional similarities due to negative sampling


Mar 26, 2021
Sebastian Damrich, Fred A. Hamprecht

* 18 pages, 14 figures 

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MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons


Nov 26, 2020
Florin C. Walter, Sebastian Damrich, Fred A. Hamprecht

* Submitted to ISBI 2021 

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Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks


Sep 10, 2020
Alberto Bailoni, Constantin Pape, Steffen Wolf, Anna Kreshuk, Fred A. Hamprecht

* Presented at GCPR 2020 

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Instance Separation Emerges from Inpainting


Feb 28, 2020
Steffen Wolf, Fred A. Hamprecht, Jan Funke


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The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation


Dec 29, 2019
Steffen Wolf, Yuyan Li, Constantin Pape, Alberto Bailoni, Anna Kreshuk, Fred A. Hamprecht


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Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning


Nov 06, 2019
Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht

* To be published in NeurIPS2019 

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DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging Analysis


Aug 22, 2019
Elke Kirschbaum, Alberto Bailoni, Fred A. Hamprecht


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A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation


Jun 27, 2019
Alberto Bailoni, Constantin Pape, Steffen Wolf, Thorsten Beier, Anna Kreshuk, Fred A. Hamprecht

* 19 pages, 8 figures, 6 tables 

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Deep Active Learning with Adaptive Acquisition


Jun 27, 2019
Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir

* Accepted at IJCAI 2019 

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End-to-End Learned Random Walker for Seeded Image Segmentation


May 22, 2019
Lorenzo Cerrone, Alexander Zeilmann, Fred A. Hamprecht


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The Mutex Watershed and its Objective: Efficient, Parameter-Free Image Partitioning


Apr 25, 2019
Steffen Wolf, Alberto Bailoni, Constantin Pape, Nasim Rahaman, Anna Kreshuk, Ullrich Köthe, Fred A. Hamprecht


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On the Spectral Bias of Neural Networks


Oct 17, 2018
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron Courville


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Essentially No Barriers in Neural Network Energy Landscape


Jun 25, 2018
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, Fred A. Hamprecht

* Proceedings of the 35th International Conference on Machine Learning, PMLR 80:1308-1317, 2018 
* In Proceedings of 35th International Conference on Machine Learning (ICML 2018) 

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Sampling-Free Variational Inference of Bayesian Neural Nets


May 19, 2018
Melih Kandemir, Manuel Haussmann, Fred A. Hamprecht


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Learning Steerable Filters for Rotation Equivariant CNNs


Mar 19, 2018
Maurice Weiler, Fred A. Hamprecht, Martin Storath

* Camera ready version, accepted for CVPR 2018 

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Diverse M-Best Solutions by Dynamic Programming


Mar 15, 2018
Carsten Haubold, Virginie Uhlmann, Michael Unser, Fred A. Hamprecht

* Haubold, C., Uhlmann, V., Unser, M., Hamprecht, F. A.: Diverse M-best solutions by dynamic programming. In: Roth, V., Vetter, T. (eds.) Pattern Recognition. GCPR 2017. vol 10496, pp. 255-267. Springer, Cham (2017) 
* Includes supplementary and corrigendum 

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End-to-end Learning of Deterministic Decision Trees


Dec 07, 2017
Thomas Hehn, Fred A. Hamprecht


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A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems


Apr 02, 2014
Jörg H. Kappes, Bjoern 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


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Active Learning with Distributional Estimates


Oct 16, 2012
Jens Roeder, Boaz Nadler, Kevin Kunzmann, Fred A. Hamprecht

* Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012) 

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How to Extract the Geometry and Topology from Very Large 3D Segmentations


Sep 30, 2010
Bjoern Andres, Ullrich Koethe, Thorben Kroeger, Fred A. Hamprecht

* C++ source code, free command line tools and MATLAB mex files are avilable from http://hci.iwr.uni-heidelberg.de/software.php 

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