@book{Goodfellow-et-al-2016,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={\url{http://www.deeplearningbook.org}},
    year={2016}
}

@article{telgarsky2015representation,
  title={Representation benefits of deep feedforward networks},
  author={Telgarsky, Matus},
  journal={arXiv:1509.08101},
  year={2015}
}


@article{gru,
  author    = {Junyoung Chung and
               {\c{C}}aglar G{\"{u}}l{\c{c}}ehre and
               KyungHyun Cho and
               Yoshua Bengio},
  title     = {Empirical Evaluation of Gated Recurrent Neural Networks on Sequence
               Modeling},
  journal   = {CoRR},
  volume    = {abs/1412.3555},
  year      = {2014},
  url       = {http://arxiv.org/abs/1412.3555},
  eprinttype = {arXiv},
  eprint    = {1412.3555},
  timestamp = {Mon, 13 Aug 2018 16:47:38 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/ChungGCB14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{vaes,
  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},
  eprinttype = {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{diffusion,
  author    = {Jonathan Ho and
               Ajay Jain and
               Pieter Abbeel},
  title     = {Denoising Diffusion Probabilistic Models},
  journal   = {CoRR},
  volume    = {abs/2006.11239},
  year      = {2020},
  url       = {https://arxiv.org/abs/2006.11239},
  eprinttype = {arXiv},
  eprint    = {2006.11239},
  timestamp = {Tue, 23 Jun 2020 17:57:22 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2006-11239.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@Article{lstm,
  author      = {Sepp Hochreiter and Jürgen Schmidhuber},
  journal     = {Neural Computation},
  title       = {Long Short-Term Memory},
  year        = {1997},
  number      = {8},
  pages       = {1735--1780},
  volume      = {9},
  optabstract = {Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.},
  optdoi      = {10.1162/neco.1997.9.8.1735},
  opteprint   = {http://dx.doi.org/10.1162/neco.1997.9.8.1735},
  opturl      = {http://dx.doi.org/10.1162/neco.1997.9.8.1735},
}

@article{vit,
  author    = {Alexey Dosovitskiy and
               Lucas Beyer and
               Alexander Kolesnikov and
               Dirk Weissenborn and
               Xiaohua Zhai and
               Thomas Unterthiner and
               Mostafa Dehghani and
               Matthias Minderer and
               Georg Heigold and
               Sylvain Gelly and
               Jakob Uszkoreit and
               Neil Houlsby},
  title     = {An Image is Worth 16x16 Words: Transformers for Image Recognition
               at Scale},
  journal   = {CoRR},
  volume    = {abs/2010.11929},
  year      = {2020},
  url       = {https://arxiv.org/abs/2010.11929},
  eprinttype = {arXiv},
  eprint    = {2010.11929},
  timestamp = {Fri, 20 Nov 2020 14:04:05 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2010-11929.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{lecun-mnisthandwrittendigit-2010,
  added-at = {2010-06-28T21:16:30.000+0200},
  author = {LeCun, Yann and Cortes, Corinna},
  biburl = {https://www.bibsonomy.org/bibtex/2935bad99fa1f65e03c25b315aa3c1032/mhwombat},
  groups = {public},
  howpublished = {http://yann.lecun.com/exdb/mnist/},
  interhash = {21b9d0558bd66279df9452562df6e6f3},
  intrahash = {935bad99fa1f65e03c25b315aa3c1032},
  keywords = {MSc _checked character_recognition mnist network neural},
  lastchecked = {2016-01-14 14:24:11},
  timestamp = {2016-07-12T19:25:30.000+0200},
  title = {{MNIST} handwritten digit database},
  url = {http://yann.lecun.com/exdb/mnist/},
  username = {mhwombat},
  year = 2010
}

@article{mishkin2015all,
  title={All you need is a good init},
  author={Mishkin, Dmytro and Matas, Jiri},
  journal={arXiv preprint arXiv:1511.06422},
  year={2015}
}

@TECHREPORT{krizhevsky2009learning,
    author = {Alex Krizhevsky},
    title = {Learning multiple layers of features from tiny images},
    institution = {University of Toronto},
    type={Technical report},
    year = {2009}
}

@article{torralba200880,
  title={80 million tiny images: A large data set for nonparametric object and scene recognition},
  author={Torralba, Antonio and Fergus, Rob and Freeman, William T},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2008}
}

@inproceedings{bvae,
  title={beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework},
  author={Irina Higgins and Lo{\"i}c Matthey and Arka Pal and Christopher P. Burgess and Xavier Glorot and Matthew M. Botvinick and Shakir Mohamed and Alexander Lerchner},
  booktitle={ICLR},
  year={2017}
}

@article{holtzman2019curious,
  title={The curious case of neural text degeneration},
  author={Holtzman, Ari and Buys, Jan and Du, Li and Forbes, Maxwell and Choi, Yejin},
  journal={ICLR},
  year={2020}
}

@article{brown2020language,
  title={Language models are few-shot learners},
  author={Brown, Tom B},
  journal={NeurIPS},
  year={2020}
}

@misc{frasca2021provably,
  author = {Frasca, Fabrizio and Bronstein, Michael},
  title = {{Provably expressive graph neural networks}},
  howpublished = "\url{https://blog.twitter.com/engineering/en_us/topics/insights/2021/provably-expressive-graph-neural-networks}",
  year = {2021}, 
  note = "[Online]"
}

@article{robinson2021can,
  title={Can contrastive learning avoid shortcut solutions?},
  author={Robinson, Joshua and Sun, Li and Yu, Ke and Batmanghelich, Kayhan and Jegelka, Stefanie and Sra, Suvrit},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={4974--4986},
  year={2021}
}

@article{khosla2020supervised,
  title={Supervised contrastive learning},
  author={Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={18661--18673},
  year={2020}
}

@inproceedings{wang2020hypersphere,
  title={Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere},
  author={Wang, Tongzhou and Isola, Phillip},
  booktitle={International Conference on Machine Learning},
  organization={PMLR},
  pages={9929--9939},
  year={2020}
}


@article{tammes1930origin,
  title={On the origin of number and arrangement of the places of exit on the surface of pollen-grains},
  author={Tammes, Pieter Merkus Lambertus},
  journal={Recueil des travaux botaniques n{\'e}erlandais},
  volume={27},
  number={1},
  pages={1--84},
  year={1930},
  publisher={Koninklijke Nederlandse Botanische Vereniging}
}

@article{zhang2021understanding,
  title={Understanding deep learning (still) requires rethinking generalization},
  author={Zhang, Chiyuan and Bengio, Samy and Hardt, Moritz and Recht, Benjamin and Vinyals, Oriol},
  journal={Communications of the ACM},
  year={2021}
}
