References

Alp10

E Alpaydin. Introduction to Machine Learning. MIT Press, 2 edition, 2010. ISBN 978-0-262-01243-0.

BKH16

Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. 2016. arXiv:1607.06450.

BCB15

Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. International Conference on Learning Representations, ICLR, 2015. arXiv:1409.0473.

BMR+20

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, 1877–1901. Curran Associates, Inc., 2020. URL: https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf.

CvMG+14

Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. 2014. cite arxiv:1406.1078Comment: EMNLP 2014. URL: http://arxiv.org/abs/1406.1078.

CCK+18

Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). June 2018.

CV95

C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:273–297, 1995.

DCLT19

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. URL: https://aclanthology.org/N19-1423, doi:10.18653/v1/N19-1423.

DN21

Prafulla Dhariwal and Alex Nichol. Diffusion models beat gans on image synthesis. CoRR, 2021. URL: https://arxiv.org/abs/2105.05233, arXiv:2105.05233.

DV16

Vincent Dumoulin and Francesco Visin. A guide to convolution arithmetic for deep learning. mar 2016. arXiv:1603.07285.

Ebd15

Mark Ebden. Gaussian Processes: A Quick Introduction. arxiv, may 2015. URL: https://arxiv.org/abs/1505.02965v2, arXiv:1505.02965.

Ert09

Wolfgang Ertel. Grundkurs Künstliche Intelligenz: eine praxisorientierte Einführung. Number ISBN 978-3-8348-0783-0. Vieweg + Teubner, Wiesbaden, second edition, 2009. URL: http://d-nb.info/994758561.

GPAM+14

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS'14, 2672–2680. Cambridge, MA, USA, 2014. MIT Press.

HZRS16

K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. 2016. doi:10.1109/CVPR.2016.90.

HZRS15

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. CoRR, 2015. URL: http://arxiv.org/abs/1512.03385, arXiv:1512.03385.

HJA20

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20. Red Hook, NY, USA, 2020. Curran Associates Inc.

IS15

Sergey Ioffe and Christian Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR, 2015. URL: http://arxiv.org/abs/1502.03167.

KSH

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks.

MO14

Mehdi Mirza and Simon Osindero. Conditional generative adversarial nets. 2014. cite arxiv:1411.1784. URL: http://arxiv.org/abs/1411.1784.

MKS+13

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A Riedmiller. Playing Atari with Deep Reinforcement Learning. CoRR, 2013. URL: http://arxiv.org/abs/1312.5602, arXiv:1312.5602.

RMC16

Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. 2016. arXiv:1511.06434.

RN18

missing booktitle in radford2018

RWC+19

missing journal in radford2019

RE16

Colin Raffel and Daniel P W Ellis. Feed-forward networks with attention can solve some long-term memory problems. In Workshop track-ICLR 2016 FEED-FORWARD NETWORKS WITH ATTENTION CAN SOLVE SOME LONG-TERM MEMORY PROBLEMS.pdf:pdf. 2016. arXiv:1512.08756v5.

RW

C E Rasmussen and C K I Williams. Gaussian Processes for Machine Learning. URL: www.GaussianProcess.org/gpml.

RFB15

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: convolutional networks for biomedical image segmentation. 2015. URL: https://arxiv.org/abs/1505.04597, doi:10.48550/ARXIV.1505.04597.

RN10

Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 3 edition, 2010.

SHM+16

David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. Mastering the game of go with deep neural networks and tree search. Nature, 529(7587):484–489, 2016. doi:10.1038/nature16961.

SDWMG15

Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In Francis Bach and David Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, 2256–2265. Lille, France, 07–09 Jul 2015. PMLR. URL: https://proceedings.mlr.press/v37/sohl-dickstein15.html.

SVL14

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, volume 4, 3104–3112. Neural information processing systems foundation, 2014. arXiv:1409.3215.

SB18

Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, second edition, 2018. URL: http://incompleteideas.net/book/the-book-2nd.html.

SLJ+14

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. 2014. arXiv:1409.4842.

VSP+17

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All you Need. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M Wallach, Rob Fergus, S V N Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, \USA\, 6000–6010. 2017. URL: http://papers.nips.cc/paper/7181-attention-is-all-you-need.

ZPIE17

Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. 2017 IEEE International Conference on Computer Vision (ICCV), pages 2242–2251, 2017.