references.bib

@article{olah2017feature,
  author = {Olah, Chris and Mordvintsev, Alexander and Schubert, Ludwig},
  title = {Feature Visualization},
  journal = {Distill},
  year = {2017},
  note = {https://distill.pub/2017/feature-visualization},
  doi = {10.23915/distill.00007}
}

@ARTICLE{2016arXiv160307285D,
       author = {{Dumoulin}, Vincent and {Visin}, Francesco},
        title = "{A guide to convolution arithmetic for deep learning}",
      journal = {arXiv e-prints},
     keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
         year = 2016,
        month = mar,
          eid = {arXiv:1603.07285},
        pages = {arXiv:1603.07285},
archivePrefix = {arXiv},
       eprint = {1603.07285},
 primaryClass = {stat.ML},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2016arXiv160307285D},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}


@article{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},
  eprinttype = {arXiv},
  eprint    = {1505.04597},
  timestamp = {Mon, 13 Aug 2018 16:46:52 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/RonnebergerFB15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{abs-1801-04381,
  author    = {Mark Sandler and
               Andrew G. Howard and
               Menglong Zhu and
               Andrey Zhmoginov and
               Liang{-}Chieh Chen},
  title     = {Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification,
               Detection and Segmentation},
  journal   = {CoRR},
  volume    = {abs/1801.04381},
  year      = {2018},
  url       = {http://arxiv.org/abs/1801.04381},
  eprinttype = {arXiv},
  eprint    = {1801.04381},
  timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1801-04381.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@ARTICLE{2017arXiv171009412Z,
       author = {{Zhang}, Hongyi and {Cisse}, Moustapha and {Dauphin}, Yann N. and {Lopez-Paz}, David},
        title = "{mixup: Beyond Empirical Risk Minimization}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
         year = 2017,
        month = oct,
          eid = {arXiv:1710.09412},
        pages = {arXiv:1710.09412},
archivePrefix = {arXiv},
       eprint = {1710.09412},
 primaryClass = {cs.LG},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2017arXiv171009412Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@book{ISLR2,
    title={An Introduction to Statistical Learning: with Applications in R},
    author={Gareth James and Daniela Witten and Trevor Hastie and Robert Tibshirani},
    publisher={Springer},
    year={2021}
}

@article{10.5555/2627435.2670313,
    author = {Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
    title = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
    year = {2014},
    issue_date = {January 2014},
    publisher = {JMLR.org},
    volume = {15},
    number = {1},
    issn = {1532-4435},
    journal = {J. Mach. Learn. Res.},
    month = {jan},
    pages = {1929–1958},
    numpages = {30},
    keywords = {neural networks, model combination, regularization, deep learning}
}

@misc{ioffe2015batch,
      title={Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},       author={Sergey Ioffe and Christian Szegedy},
      year={2015},
      eprint={1502.03167},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{Smith15a,
  author    = {Leslie N. Smith},
  title     = {No More Pesky Learning Rate Guessing Games},
  journal   = {CoRR},
  volume    = {abs/1506.01186},
  year      = {2015},
  url       = {http://arxiv.org/abs/1506.01186},
  archivePrefix = {arXiv},
  eprint    = {1506.01186},
  timestamp = {Mon, 13 Aug 2018 16:47:53 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/Smith15a.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{LoshchilovH16a,
  author    = {Ilya Loshchilov and
               Frank Hutter},
  title     = {{SGDR:} Stochastic Gradient Descent with Restarts},
  journal   = {CoRR},
  volume    = {abs/1608.03983},
  year      = {2016},
  url       = {http://arxiv.org/abs/1608.03983},
  archivePrefix = {arXiv},
  eprint    = {1608.03983},
  timestamp = {Mon, 13 Aug 2018 16:48:29 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/LoshchilovH16a.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{abs-1708-07120,
  author    = {Leslie N. Smith and
               Nicholay Topin},
  title     = {Super-Convergence: Very Fast Training of Residual Networks Using Large
               Learning Rates},
  journal   = {CoRR},
  volume    = {abs/1708.07120},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.07120},
  archivePrefix = {arXiv},
  eprint    = {1708.07120},
  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1708-07120.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{HeZRS15,
  author    = {Kaiming He and
               Xiangyu Zhang and
               Shaoqing Ren and
               Jian Sun},
  title     = {Deep Residual Learning for Image Recognition},
  journal   = {CoRR},
  volume    = {abs/1512.03385},
  year      = {2015},
  url       = {http://arxiv.org/abs/1512.03385},
  eprinttype = {arXiv},
  eprint    = {1512.03385},
  timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{hochreiter1997long,
  added-at = {2016-11-15T08:49:43.000+0100},
  author = {Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  biburl = {https://www.bibsonomy.org/bibtex/2a4a80026d24955b267cae636aa8abe4a/dallmann},
  interhash = {0692b471c4b9ae65d00affebc09fb467},
  intrahash = {a4a80026d24955b267cae636aa8abe4a},
  journal = {Neural computation},
  keywords = {lstm rnn},
  number = 8,
  pages = {1735--1780},
  publisher = {MIT Press},
  timestamp = {2016-11-15T08:49:43.000+0100},
  title = {Long short-term memory},
  volume = 9,
  year = 1997
}

@article{ChoMGBSB14,
  author    = {Kyunghyun Cho and
               Bart van Merrienboer and
               {\c{C}}aglar G{\"{u}}l{\c{c}}ehre and
               Fethi Bougares and
               Holger Schwenk and
               Yoshua Bengio},
  title     = {Learning Phrase Representations using {RNN} Encoder-Decoder for Statistical
               Machine Translation},
  journal   = {CoRR},
  volume    = {abs/1406.1078},
  year      = {2014},
  url       = {http://arxiv.org/abs/1406.1078},
  eprinttype = {arXiv},
  eprint    = {1406.1078},
  timestamp = {Mon, 13 Aug 2018 16:46:44 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/ChoMGBSB14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{abs-1804-03209,
  author    = {Pete Warden},
  title     = {Speech Commands: {A} Dataset for Limited-Vocabulary Speech Recognition},
  journal   = {CoRR},
  volume    = {abs/1804.03209},
  year      = {2018},
  url       = {http://arxiv.org/abs/1804.03209},
  eprinttype = {arXiv},
  eprint    = {1804.03209},
  timestamp = {Mon, 13 Aug 2018 16:48:32 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1804-03209.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{2019EA000740,
    author = {Cho, Dongjin and Yoo, Cheolhee and Im, Jungho and Cha, Dong-Hyun},
    title = {Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas},
    journal = {Earth and Space Science},
    volume = {7},
    number = {4},
    pages = {e2019EA000740},
    keywords = {Air temperature forecast, bias correction, random forest, support vector regression, artificial neural networks, multi-model ensemble},
    doi = {https://doi.org/10.1029/2019EA000740},
    url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019EA000740},
    eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019EA000740},
    note = {e2019EA000740 2019EA000740},
    year = {2020}
}

@book{Trefethen,
    title={Numerical linear algebra},
    author={Lloyd N. Trefethen and David Bau},
    publisher={SIAM},
    year={1997}
}

@book{Osgood,
    title={Lectures on the Fourier Transform and Its Applications},
    author={Brad Osgood},
    publisher={American Mathematical Society},
    year={2019}
}


@book{waves,
    title={Physics of Oscillations and Waves. With use of Matlab and Python},
    author={Arnt Inge Vistnes},
    publisher={Springer},
    year={2018}
}



@article{abs-2104-13478,
  author    = {Michael M. Bronstein and
               Joan Bruna and
               Taco Cohen and
               Petar Velickovic},
  title     = {Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges},
  journal   = {CoRR},
  volume    = {abs/2104.13478},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.13478},
  eprinttype = {arXiv},
  eprint    = {2104.13478},
  timestamp = {Tue, 04 May 2021 15:12:43 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-13478.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}