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@article{DBLP:journals/corr/GoyalDGNWKTJH17,
  author    = {Priya Goyal and
               Piotr Doll{\'{a}}r and
               Ross B. Girshick and
               Pieter Noordhuis and
               Lukasz Wesolowski and
               Aapo Kyrola and
               Andrew Tulloch and
               Yangqing Jia and
               Kaiming He},
  title     = {Accurate, Large Minibatch {SGD:} Training ImageNet in 1 Hour},
  journal   = {CoRR},
  volume    = {abs/1706.02677},
  year      = {2017},
  url       = {http://arxiv.org/abs/1706.02677},
  archivePrefix = {arXiv},
  eprint    = {1706.02677},
  timestamp = {Mon, 03 Jul 2017 13:29:02 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/GoyalDGNWKTJH17},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{DBLP:journals/corr/abs-1708-02983,
  author    = {Yang You and
               Aydin Bulu{\c{c}} and
               James Demmel},
  title     = {Scaling Deep Learning on {GPU} and Knights Landing clusters},
  journal   = {CoRR},
  volume    = {abs/1708.02983},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.02983},
  archivePrefix = {arXiv},
  eprint    = {1708.02983},
  timestamp = {Tue, 05 Sep 2017 10:03:46 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/abs-1708-02983},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{DBLP:journals/corr/abs-1709-05011,
  author    = {Yang You and
               Zhao Zhang and
               Cho{-}Jui Hsieh and
               James Demmel},
  title     = {100-epoch ImageNet Training with AlexNet in 24 Minutes},
  journal   = {CoRR},
  volume    = {abs/1709.05011},
  year      = {2017},
  url       = {http://arxiv.org/abs/1709.05011},
  archivePrefix = {arXiv},
  eprint    = {1709.05011},
  timestamp = {Thu, 05 Oct 2017 09:42:47 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/abs-1709-05011},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{DBLP:journals/corr/JanuszewskiMLKD16,
  author    = {Michal Januszewski and
               Jeremy Maitin{-}Shepard and
               Peter Li and
               J{\"{o}}rgen Kornfeld and
               Winfried Denk and
               Viren Jain},
  title     = {Flood-Filling Networks},
  journal   = {CoRR},
  volume    = {abs/1611.00421},
  year      = {2016},
  url       = {http://arxiv.org/abs/1611.00421},
  archivePrefix = {arXiv},
  eprint    = {1611.00421},
  timestamp = {Wed, 07 Jun 2017 14:42:04 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/JanuszewskiMLKD16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article {Januszewski200675,
      author = {Januszewski, Micha{\l} and Kornfeld, J{\"o}rgen and Li, Peter H and Pope, Art and Blakely, Tim and Lindsey, Larry and Maitin-Shepard, Jeremy B and Tyka, Mike and Denk, Winfried and Jain, Viren},
      title = {High-Precision Automated Reconstruction of Neurons with Flood-filling Networks},
      year = {2017},
      doi = {10.1101/200675},
      publisher = {Cold Spring Harbor Laboratory},
      abstract = {Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites. Automated approaches have been developed to perform the tracing, but without costly human proofreading their error rates are too high to obtain reliable circuit diagrams. We present a method for automated segmentation that, like the majority of previous efforts, employs convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of the reconstructed shape of individual neural processes. We used this technique, which we call flood-filling networks, to trace neurons in a data set obtained by serial block-face electron microscopy from a male zebra finch brain. Our method achieved a mean error-free neurite path length of 1.1 mm, an order of magnitude better than previously published approaches applied to the same dataset. Only 4 mergers were observed in a neurite test set of 97 mm path length.},
      URL = {https://www.biorxiv.org/content/early/2017/10/09/200675},
      eprint = {https://www.biorxiv.org/content/early/2017/10/09/200675.full.pdf},
      journal = {bioRxiv}
}

@article{DBLP:journals/corr/RonnebergerFB15,
  author    = {Olaf Ronneberger and
               Philipp Fischer and
               Thomas Brox},
  title     = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
  journal   = {CoRR},
  volume    = {abs/1505.04597},
  year      = {2015},
  url       = {http://arxiv.org/abs/1505.04597},
  archivePrefix = {arXiv},
  eprint    = {1505.04597},
  timestamp = {Wed, 07 Jun 2017 14:40:33 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/RonnebergerFB15},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{DBLP:journals/corr/CicekALBR16,
  author    = {{\"{O}}zg{\"{u}}n {\c{C}}i{\c{c}}ek and
               Ahmed Abdulkadir and
               Soeren S. Lienkamp and
               Thomas Brox and
               Olaf Ronneberger},
  title     = {3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation},
  journal   = {CoRR},
  volume    = {abs/1606.06650},
  year      = {2016},
  url       = {http://arxiv.org/abs/1606.06650},
  archivePrefix = {arXiv},
  eprint    = {1606.06650},
  timestamp = {Wed, 07 Jun 2017 14:41:35 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/CicekALBR16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{KASTHURI2015648,
title = "Saturated Reconstruction of a Volume of Neocortex",
journal = "Cell",
volume = "162",
number = "3",
pages = "648 - 661",
year = "2015",
issn = "0092-8674",
doi = "https://doi.org/10.1016/j.cell.2015.06.054",
url = "http://www.sciencedirect.com/science/article/pii/S0092867415008247",
author = "Narayanan Kasthuri and Kenneth Jeffrey Hayworth and Daniel Raimund Berger and Richard Lee Schalek and José Angel Conchello and Seymour Knowles-Barley and Dongil Lee and Amelio Vázquez-Reina and Verena Kaynig and Thouis Raymond Jones and Mike Roberts and Josh Lyskowski Morgan and Juan Carlos Tapia and H. Sebastian Seung and William Gray Roncal and Joshua Tzvi Vogelstein and Randal Burns and Daniel Lewis Sussman and Carey Eldin Priebe and Hanspeter Pfister and Jeff William Lichtman"
}

@ARTICLE{2013arXiv1303.7186K,
   author = {{Kaynig}, V. and {Vazquez-Reina}, A. and {Knowles-Barley}, S. and 
      {Roberts}, M. and {Jones}, T.~R. and {Kasthuri}, N. and {Miller}, E. and 
      {Lichtman}, J. and {Pfister}, H.},
    title = "{Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1303.7186},
 primaryClass = "q-bio.NC",
 keywords = {Quantitative Biology - Neurons and Cognition, Computer Science - Computer Vision and Pattern Recognition},
     year = 2013,
    month = mar,
   adsurl = {http://adsabs.harvard.edu/abs/2013arXiv1303.7186K},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{segEM,
author = "ManuelBerning and Kevin Boergens and Moritz Helmstaedter",
title = "SegEM: Efficient Image Analysis for High-Resolution Connectomics",
journal = "Neuron",
issn = "Volume 87, Issue 6, 23 September 2015, Pages 1193-1206"
}

@article{DBLP:journals/corr/AbadiBCCDDDGIIK16,
  author    = {Mart{\'{\i}}n Abadi and
               Paul Barham and
               Jianmin Chen and
               Zhifeng Chen and
               Andy Davis and
               Jeffrey Dean and
               Matthieu Devin and
               Sanjay Ghemawat and
               Geoffrey Irving and
               Michael Isard and
               Manjunath Kudlur and
               Josh Levenberg and
               Rajat Monga and
               Sherry Moore and
               Derek Gordon Murray and
               Benoit Steiner and
               Paul A. Tucker and
               Vijay Vasudevan and
               Pete Warden and
               Martin Wicke and
               Yuan Yu and
               Xiaoqiang Zhang},
  title     = {TensorFlow: {A} system for large-scale machine learning},
  journal   = {CoRR},
  volume    = {abs/1605.08695},
  year      = {2016},
  url       = {http://arxiv.org/abs/1605.08695},
  archivePrefix = {arXiv},
  eprint    = {1605.08695},
  timestamp = {Wed, 07 Jun 2017 14:41:30 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/AbadiBCCDDDGIIK16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{horovod,
title = "Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow",
author = "Alex Sergeev and Mike Del Balso",
url = "https://eng.uber.com/horovod/"
}

@inproceedings{40808,
title = {An Empirical study of learning rates in deep neural networks for speech recognition},
author  = {Andrew Senior and Georg Heigold and Marc'aurelio Ranzato and Ke Yang},
year  = {2013},
booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
address = {Vancouver, CA}
}

@ARTICLE{10.3389/fnana.2015.00142,
  
AUTHOR={Arganda-Carreras, Ignacio and Turaga, Srinivas C. and Berger, Daniel R. and Cireşan, Dan and Giusti, Alessandro and Gambardella, Luca M. and Schmidhuber, JÃŒrgen and Laptev, Dmitry and Dwivedi, Sarvesh and Buhmann, Joachim M. and Liu, Ting and Seyedhosseini, Mojtaba and Tasdizen, Tolga and Kamentsky, Lee and Burget, Radim and Uher, Vaclav and Tan, Xiao and Sun, Changming and Pham, Tuan D. and Bas, Erhan and Uzunbas, Mustafa G. and Cardona, Albert and Schindelin, Johannes and Seung, H. Sebastian},   
   
TITLE={Crowdsourcing the creation of image segmentation algorithms for connectomics},      
  
JOURNAL={Frontiers in Neuroanatomy},      
  
VOLUME={9},      

PAGES={142},     
  
YEAR={2015},      
    
URL={https://www.frontiersin.org/article/10.3389/fnana.2015.00142},       
  
DOI={10.3389/fnana.2015.00142},      
  
ISSN={1662-5129},   
   
ABSTRACT={To stimulate progress in automating the reconstruction of neural circuits,
we organized the first international challenge on 2D segmentation
of electron microscopic (EM) images of the brain. Participants submitted
boundary maps predicted for a test set of images, and were scored
based on their agreement with ground truth from human experts. The
winning team had no prior experience with EM images, and employed
a convolutional network. This ``deep learning'' approach has since
become accepted as a standard for segmentation of EM images. The challenge
has continued to accept submissions, and the best so far has resulted
from cooperation between two teams. The challenge has probably saturated,
as algorithms cannot progress beyond limits set by ambiguities inherent
in 2D scoring. Retrospective evaluation of the challenge scoring system
reveals that it was not sufficiently robust to variations in the widths
of neurite borders. We propose a solution to this problem, which should
be useful for a future 3D segmentation challenge.}
}

@ARTICLE{2014arXiv1412.6980K,
   author = {{Kingma}, D.~P. and {Ba}, J.},
    title = "{Adam: A Method for Stochastic Optimization}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1412.6980},
 primaryClass = "cs.LG",
 keywords = {Computer Science - Learning},
     year = 2014,
    month = dec,
   adsurl = {http://adsabs.harvard.edu/abs/2014arXiv1412.6980K},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}