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Person

Fred Hamprecht

Name	Fred Hamprecht
Ort	Deutschland
Geschlecht	männlich
Akademischer Grad bzw. Titel	Prof. Dr.
Variante	Fred A. Hamprecht
Beziehung	Universität Heidelberg. Interdisziplinäres Zentrum für Wissenschaftliches Rechnen Statistik (Thema)
Weitere Angaben	Forschungsgebiete: Bildverarbeitung, Datenanalyse, Mustererkennung und maschinelles Lernen; seit 2001 Professor for Multidimensional Image Processing, University of Heidelberg
Datensatztyp	Person
GND-Nummer	1020505605
Datensatz-ID	1020505605
Beteiligt an alle anzeigen Designing Deep Learning Frameworks for Plant Biology Discovering structure without labels Learning Instance Segmentation from Sparse Supervision Temporal control of the integrated stress response by a stochastic molecular switch Bayesian Neural Networks for Probabilistic Machine Learning Scalable Instance Segmentation for Microscopy ilastik: interactive machine learning for (bio)image analysis Machine Learning for Instance Segmentation End-to-End Learning of Decision Trees and Forests Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron Microscopy Novel Machine Learning Approaches for Neurophysiological Data Analysis Multicut Algorithms for Neurite Segmentation Scalable Inference for Multi-Target Tracking of Proliferating Cells Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors From experimental setup to bioinformatics: An RNAi screening platform to identify host factors involved in HIV‐1 replication

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Katalog der Deutschen Nationalbibliothek

Fred Hamprecht

Designing Deep Learning Frameworks for Plant Biology

Discovering structure without labels

Learning Instance Segmentation from Sparse Supervision

Temporal control of the integrated stress response by a stochastic molecular switch

Bayesian Neural Networks for Probabilistic Machine Learning

Scalable Instance Segmentation for Microscopy

ilastik: interactive machine learning for (bio)image analysis

Machine Learning for Instance Segmentation

End-to-End Learning of Decision Trees and Forests

Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures

Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron Microscopy

Novel Machine Learning Approaches for Neurophysiological Data Analysis

Multicut Algorithms for Neurite Segmentation

Scalable Inference for Multi-Target Tracking of Proliferating Cells

Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors

From experimental setup to bioinformatics: An RNAi screening platform to identify host factors involved in HIV‐1 replication