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12.4.Intro
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19.4.Good Friday - no class
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26.4.Basics: EMNLP tutorialKim et al. 2018
http://nlp.seas.harvard.edu/latent-nlp-tutorial.html
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3.5.Basics: EMNLP tutorialKim et al. 2018
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10.5.Tutorial on Variational AutoencodersDoersch 2016https://arxiv.org/pdf/1606.05908.pdf
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https://towardsdatascience.com/intuitively-understanding-variational-autoencoders-1bfe67eb5daf
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https://www.jeremyjordan.me/variational-autoencoders/
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17.5.Sentence generation with continuous VAEsBowman et al. 2016https://arxiv.org/pdf/1511.06349.pdfSchäfer
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24.5.Dialog modeling with discrete VAEsZhao et al. 2018http://aclweb.org/anthology/P18-1101Skrjanec
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31.5.Dependency parsing with structured discrete VAEsCorro & Titov 2019https://arxiv.org/pdf/1807.09875van Harmelen
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7.6.Crash course: reinforcement learningSutton & Barto Book
http://www.incompleteideas.net/book/the-book-2nd.html
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Basics of RL
https://becominghuman.ai/the-very-basics-of-reinforcement-learning-154f28a79071
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https://karpathy.github.io/2016/05/31/rl/
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Policy gradient methods
https://towardsdatascience.com/policy-gradients-in-a-nutshell-8b72f9743c5d
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https://medium.com/@thechrisyoon/deriving-policy-gradients-and-implementing-reinforce-f887949bd63
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https://danieltakeshi.github.io/2017/03/28/going-deeper-into-reinforcement-learning-fundamentals-of-policy-gradients/
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14.6.Summarization with discrete VAEsMiao & Blunsom 2016https://arxiv.org/pdf/1609.07317.pdf Kuhn
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21.6.Semantic parsing with structured discrete VAEsYin et al. 2018http://aclweb.org/anthology/P18-1070Mina
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28.6.Learning tree models 1Yogatama et al. 2017https://arxiv.org/pdf/1611.09100.pdfAnikina
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5.7.SFB retreat - no class
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12.7.Learning tree models 2Choi et al. 2018https://arxiv.org/pdf/1707.02786.pdfLindemann
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19.7.Learning tree models 3Havrylov et al. 2019
https://paperswithcode.com/paper/cooperative-learning-of-disjoint-syntax-and
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