VAE and GAN papers

Date	Presenter	Paper
	Hoa	Wasserstein Generative Adversarial Networks:
		Lucas Theis, Aäron van den Oord, Matthias Bethge. A note on the evaluation of generative models. ICLR 2016
		Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger Grosse. On the Quantitative Analysis of Decoder-Based Generative Models. ICLR 2017
		Martin Arjovsky, Léon Bottou. Towards Principled Methods for Training Generative Adversarial Networks. Arxiv 2017
		Martin Arjovsky, Soumith Chintala, Leon Bottou. Wasserstein Generative Adversarial Networks. ICML 2017
		Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville. Improved Training of Wasserstein GANs. NIPS 2017
		Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Carl-Johann Simon-Gabriel, Bernhard Schoelkopf. From optimal transport to generative modeling: the VEGAN cookbook. Arxiv 2017
		Wasserstein Auto-Encoders:
		Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schoelkopf. Wasserstein Auto-Encoders. ICLR 2018
		Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin. Learning Disentangled Representations with Wasserstein Auto-Encoders. Workshop ICLR 2018
		Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin. Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders. Workshop ICLR 2018
		Paul K. Rubenstein, Bernhard Scholkopf, Ilya Tolstikhin. On the Latent Space of Wasserstein Auto-Encoders. Arxiv 2018
		Nicolas Courty, Rémi Flamary, Mélanie Ducoffe. Learning Wasserstein Embeddings. ICLR 2018
		Hareesh Bahuleyan, Lili Mou, Kartik Vamaraju, Hao Zhou, Olga Vechtomova. Probabilistic Natural Language Generation with Wasserstein Autoencoders. Arxiv 2018
		Variational Auto-Encoders:
		Matthew D. Hoffman and Matthew J. Johnson. ELBO surgery: yet another way to carve up the variational evidence lower bound. NIPS 2016 Workshop
		Shengjia Zhao, Jiaming Song, Stefano Ermon. InfoVAE: Balancing Learning and Inference in Variational Autoencoders. Arxiv 2017
		Alex Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, Kevin Murphy. Fixing a Broken ELBO. ICML 2018
		Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M. Rush, Yann LeCun. Adversarially Regularized Autoencoders. ICML 2018
		Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush. Semi-Amortized Variational Autoencoders. ICML 2018
		Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, Alexander M. Rush. Latent Alignment and Variational Attention. Preprint 2018
		Alex Graves, Jacob Menick, Aaron van den Oord. Associative Compression Networks for Representation Learning. Arxiv 2018
		Maximum Mean Discrepancy:
		Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola. A Kernel Two-Sample Test. NIPS 2007
		Yujia Li, Kevin Swersky, Richard Zemel. Generative Moment Matching Networks. ICML 2015
		Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani. Training generative neural networks via Maximum Mean Discrepancy optimization. Arxiv 2015