references.bib

%TODO: use inbook for various references to goodfellow
@book{goodfellow,
  title={Deep Learning},
  author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
  publisher={MIT Press},
  note={\url{http://www.deeplearningbook.org}},
  year={2016}
}
@article{universal-approximator,
  author    = "Moshe Leshno, Vladimir Ya. Lin, Allan Pinkus and Shimon Schocken",
  title     = "Multilayer Feedforward Networks With a Nonpolynomial
    Activation Function Can Approximate Any Function",
  journal   = "Neural Networks",
  ?_volume   = "6",
  ?_number   = "",
  ?_pages    = "861-867",
  year      = "1993",
  ?_month    = "March",
  ?_note     = ""
}
@article{noise-hidden-layers,
  author    = {Ben Poole and
    Jascha Sohl{-}Dickstein and
      Surya Ganguli},
  title     = {Analyzing noise in autoencoders and deep networks},
  journal   = {CoRR},
  volume    = {abs/1406.1831},
  year      = {2014},
  url       = {http://arxiv.org/abs/1406.1831},
  archivePrefix = {arXiv},
  eprint    = {1406.1831},
  timestamp = {Mon, 13 Aug 2018 16:48:58 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/PooleSG14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}

}
@article{batchnorm,
  author    = {Sergey Ioffe and
               Christian Szegedy},
  title     = {Batch Normalization: Accelerating Deep Network Training by Reducing
               Internal Covariate Shift},
  journal   = {CoRR},
  volume    = {abs/1502.03167},
  year      = {2015},
  url       = {http://arxiv.org/abs/1502.03167},
  archivePrefix = {arXiv},
  eprint    = {1502.03167},
  timestamp = {Mon, 13 Aug 2018 16:47:06 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/IoffeS15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{dropout,
  author  = {Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov},
  title   = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
  journal = {Journal of Machine Learning Research},
  year    = {2014},
  volume  = {15},
  number  = {56},
  pages   = {1929-1958},
  url     = {http://jmlr.org/papers/v15/srivastava14a.html}
}
@article{resnet,
  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},
  archivePrefix = {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{vae,
  author    = {Diederik P. Kingma and
    Max Welling},
  title     = {An Introduction to Variational Autoencoders},
  journal   = {CoRR},
  volume    = {abs/1906.02691},
  year      = {2019},
  url       = {http://arxiv.org/abs/1906.02691},
  archivePrefix = {arXiv},
  eprint    = {1906.02691},
  timestamp = {Thu, 13 Jun 2019 13:36:00 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1906-02691.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{attention,
  author    = {Ashish Vaswani and
               Noam Shazeer and
               Niki Parmar and
               Jakob Uszkoreit and
               Llion Jones and
               Aidan N. Gomez and
               Lukasz Kaiser and
               Illia Polosukhin},
  title     = {Attention Is All You Need},
  journal   = {CoRR},
  volume    = {abs/1706.03762},
  year      = {2017},
  url       = {http://arxiv.org/abs/1706.03762},
  archivePrefix = {arXiv},
  eprint    = {1706.03762},
  timestamp = {Mon, 13 Aug 2018 16:48:37 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/VaswaniSPUJGKP17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{bert,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.04805},
  archivePrefix = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@misc{gpt3,
    title={Language Models are Few-Shot Learners},
    author={Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert-Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei},
    year={2020},
    eprint={2005.14165},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
@InProceedings{bishop-regularization,
  author = {Bishop, Christopher},
  title = {Regularization and Complexity Control in Feed-forward Networks},
  booktitle = {Proceedings International Conference on Artificial Neural Networks ICANN'95},
  year = {1995},
  month = {January},
  abstract = {In this paper we consider four alternative approaches to complexity control in feed-forward networks based respectively on architecture selection, regularization, early stopping, and training with noise. We show that there are close similarities between these approaches and we argue that, for most practical applications, the technique of regularization should be the method of choice.},
  publisher = {EC2 et Cie},
  url = {https://www.microsoft.com/en-us/research/publication/regularization-and-complexity-control-in-feed-forward-networks/},
  pages = {141-148},
  volume = {1},
  edition = {Proceedings International Conference on Artificial Neural Networks ICANN'95},
}
@InProceedings{magnetic-positioning,
  author={W. {Storms} and J. {Shockley} and J. {Raquet}},
  booktitle={2010 Ubiquitous Positioning Indoor Navigation and Location Based Service}, 
  title={Magnetic field navigation in an indoor environment}, 
  year={2010},
  volume={},
  number={},
  pages={1-10},
}
@article{particle-filter1,
  author={J. M. {Pak} and C. K. {Ahn} and Y. S. {Shmaliy} and M. T. {Lim}},
  journal={IEEE Transactions on Industrial Informatics}, 
  title={Improving Reliability of Particle Filter-Based Localization in Wireless Sensor Networks via Hybrid Particle/FIR Filtering}, 
  year={2015},
  volume={11},
  number={5},
  pages={1089-1098},
}
@article{particle-filter2,
  author={P. {Yang} and W. {Wu}},
  journal={IEEE Transactions on Industrial Electronics}, 
  title={Efficient Particle Filter Localization Algorithm in Dense Passive RFID Tag Environment}, 
  year={2014},
  volume={61},
  number={10},
  pages={5641-5651},
}
@online{apple-google,
  author = {Apple and Google},
  title = {Privacy-Preserving Contact Tracing},
  year = 2020,
  url = {https://www.google.com/covid19/exposurenotifications/},
}
@online{immuni,
  author = {Ministero della Salute},
  title = {Immuni},
  year = 2020,
  url = {https://github.com/immuni-app/immuni-documentation},
}
@online{flutter,
  author = {Google},
  title = {Flutter SDK},
  url = {https://flutter.dev/},
}
@online{tensorflow-lite,
  author = {Google},
  title = {TensorFlow Lite},
  url = {https://www.tensorflow.org/lite},
}
%TODO: add tensorflow, colab and flutter references