Philipp Grüning, M.Sc.

Raum 1.018

Institut für Neuro- und Bioinformatik
Ratzeburger Allee 160 (Geb. 64)
23562 Lübeck

Email:
Phone:
+49 451 3101 5515
Fax:
+49 451 3101 5504

Publikationen

2020

  • Grüning, Philipp and Palumbo, Alex and Zille, Marietta and Barth, Erhardt and Mamlouk, Amir Madany: A task-dependent active learning method for axon segmentation with CNNs. Proceedings on Automation in Medical Engineering, no. 1, pp. 025-025, 2020
    BibTeX Link
    @article{GrPaZiBaMa20,
    title={A task-dependent active learning method for axon segmentation with CNNs},
    author={Gr{\"u}ning, Philipp and Palumbo, Alex and Zille, Marietta and Barth, Erhardt and Mamlouk, Amir Madany},
    journal={Proceedings on Automation in Medical Engineering},
    volume={1},
    number={1},
    pages={025--025},
    year={2020},
    url = {http://webmail.inb.uni-luebeck.de/inb-publications/pdfs/GrPaZiBaMa20.pdf},
    }
    
    
  • Grüning, Philipp and Mamlouk, Amir Madany: Deep Neural-Gas Clustering for Instance Segmentation across Imaging Experiments. in 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2020
    BibTeX
    @inproceedings{GrMa20,
      title={Deep {N}eural-{G}as {C}lustering for {I}nstance {S}egmentation across {I}maging {E}xperiments},
      author={Gr{\"u}ning, Philipp and Mamlouk, Amir Madany},
      booktitle={2020 International Joint Conference on Neural Networks (IJCNN)},
      pages={1--8},
      year={2020},
      organization={IEEE},
    }
    
    
  • Grüning, Philipp and Martinetz, Thomas and Barth, Erhardt: Feature Products Yield Efficient Networks. arXiv preprint arXiv:2008.07930, 2020
    BibTeX Link
    @article{GrMaBa20,
      title={Feature Products Yield Efficient Networks},
      author={Gr{\"u}ning, Philipp and Martinetz, Thomas and Barth, Erhardt},
      journal={arXiv preprint arXiv:2008.07930},
      year={2020},
      url = {https://webmail.inb.uni-luebeck.de/inb-publications/htmls/GrMaBa20.pdf},
    }
    
    
  • Grüning, Philipp and Martinetz, Thomas and Barth, Erhardt: Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networks. in Artificial Neural Networks and Machine Learning - ICANN 2020, pp. 79-91, Springer International Publishing, Cham, 2020
    BibTeX
    @inproceedings{GrMaBa20a,
    author={Gr{\"u}ning, Philipp and Martinetz, Thomas and Barth, Erhardt},
    editor={Farka{\v{s}}, Igor and Masulli, Paolo and Wermter, Stefan},
    title={Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networks},
    booktitle={Artificial Neural Networks and Machine Learning -- ICANN 2020},
    year={2020},
    publisher={Springer International Publishing},
    address={Cham},
    pages={79--91},
    abstract={We introduce Logarithm-Networks (Log-Nets), a novel bio-inspired type of network architecture based on logarithms of feature maps followed by convolutions. Log-Nets are capable of surpassing the performance of traditional convolutional neural networks (CNNs) while using fewer parameters. Performance is evaluated on the Cifar-10 and ImageNet benchmarks.},
    isbn={978-3-030-61616-8},
    }