Fred A. Hamprecht

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Anna KreshukGroup Leader, EMBL HeidelbergPatvirtintas el. paštas embl.de
Bjoern MenzeUniversität ZürichPatvirtintas el. paštas uzh.ch
Thorsten BeierQuantStackPatvirtintas el. paštas quantstack.net
Christoph-Nikolas StraehleResearch Scientist, Bosch Center for Artificial IntelligencePatvirtintas el. paštas de.bosch.com
Bjoern AndresTU DresdenPatvirtintas el. paštas andres.sc
Martin SchieggResearch Scientist, Bosch Center for AIPatvirtintas el. paštas bosch.com
Carsten HauboldPhD Student, University of HeidelbergPatvirtintas el. paštas iwr.uni-heidelberg.de
Hanno SteenProfessor of Pathology, Harvard Medical SchoolPatvirtintas el. paštas childrens.harvard.edu
Bernhard RenardHasso Plattner InstitutePatvirtintas el. paštas hpi.de
Bernhard X. KauslerPatvirtintas el. paštas kausler.net
Judith A. Jebanathirajah SteenAssociate Professor of Neurology, Harvard Medical SchoolPatvirtintas el. paštas childrens.harvard.edu
Melih KandemirAssociate Professor of Machine Learning at the University of Southern DenmarkPatvirtintas el. paštas imada.sdu.dk
Christoph SommerIST AustriaPatvirtintas el. paštas ist.ac.at
Michael HanselmannTechnology Owner & Senior Manager (AI, Automated Driving), Robert Bosch GmbHPatvirtintas el. paštas de.bosch.com
Jörg Hendrik KappesHeidelberg EngineeringPatvirtintas el. paštas math.uni-heidelberg.de
Graham KnottSenior Scientist, EPFLPatvirtintas el. paštas epfl.ch
Adrian WolnyMachine Learning Research Scientist, Bayer AGPatvirtintas el. paštas bayer.com
Steffen WolfMRC Laboratory of Molecular Biology, CambridgePatvirtintas el. paštas mrc-lmb.cam.ac.uk
Xinghua Lou, Ph.D.Google DeepMindPatvirtintas el. paštas deepmind.com
Ferran DiegoTelefónica ResearchPatvirtintas el. paštas telefonica.com

Stebėti

Fred A. Hamprecht

Professor, Heidelberg University

Patvirtintas el. paštas iwr.uni-heidelberg.de - Pagrindinis puslapis

Scientific AI Quantum Chemistry Machine Learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	2190	2019
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	1786	1998
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	1699	2019
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, 1-16, 2009	1405	2009
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	1398	2011
An objective comparison of cell-tracking algorithms V Ulman, M Maška, KEG Magnusson, O Ronneberger, C Haubold, ... Nature methods 14 (12), 1141-1152, 2017	620	2017
Essentially no barriers in neural network energy landscape F Draxler, K Veschgini, M Salmhofer, F Hamprecht International conference on machine learning, 1309-1318, 2018	480	2018
Learning steerable filters for rotation equivariant cnns M Weiler, FA Hamprecht, M Storath Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018	455	2018
On oblique random forests BH Menze, BM Kelm, DN Splitthoff, U Koethe, FA Hamprecht Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011	337	2011
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	304	1998
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	301	2014
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	275	2012
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	255	2008
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, 155-184, 2015	248	2015
Accurate and versatile 3D segmentation of plant tissues at cellular resolution A Wolny, L Cerrone, A Vijayan, R Tofanelli, AV Barro, M Louveaux, ... Elife 9, e57613, 2020	247	2020
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	225	2013
Common limitations of image processing metrics: A picture story A Reinke, MD Tizabi, CH Sudre, M Eisenmann, T Rädsch, M Baumgartner, ... arXiv preprint arXiv:2104.05642, 2021	219	2021
Visualizing a homogeneous blend in bulk heterojunction polymer solar cells by analytical electron microscopy M Pfannmoller, H Flugge, G Benner, I Wacker, C Sommer, M Hanselmann, ... Nano letters 11 (8), 3099-3107, 2011	213	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	209	2016
Multicut brings automated neurite segmentation closer to human performance T Beier, C Pape, N Rahaman, T Prange, S Berg, DD Bock, A Cardona, ... Nature methods 14 (2), 101-102, 2017	180	2017

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