Graves, A.; Mohamed, A.-R.; and Hinton, G. 2013. Speech
Recognition with Deep Recurrent Neural Networks. In 2013
IEEE International Conference on Acoustics, Speech and Signal
Processing, 6645–6649. Piscataway, NJ: Institute for Electrical
and Electronics Engineers. doi.org/10.1109/ICAS-
Gulrajani, I.; Ahmed, F.; Arjovsky, M.; Dumoulin, V.; and
Courville, A. 2017. Improved Training of Wasserstein Gans.
Unpublished arXiv preprint. arXiv:1704.00028 [cs.LG].
Ithaca, NY: Cornell University Library.
Huang, D.; Yao, S.; Wang, Y.; and De La Torre, F. 2014.
Sequential Max-Margin Event Detectors. In 13th European
Conference on Computer Vision, Lecture Notes on Computer
Science 8689, 410–424. Berlin: Springer. doi.org/10.1007/
Kingma, D. P.; Mohamed, S.; Rezende, D. J.; and Welling, M.
2014. Semi-Supervised Learning with Deep Generative
Models. In Advances in Neural Information Processing Systems
27, 3581–3589. December 8-13, Montreal, Quebec, Canada.
Koller, D., and Friedman, N. 2009. Probabilistic Graphical
Models: Principles and Techniques. Cambridge, MA: The MIT
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012. Ima-genet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems
26, 1097–1105. December 5-8, 2013, Lake Tahoe, Nevada.
Lenat, D. B. 1995. Cyc: A Large-Scale Investment in Knowledge Infrastructure. Communications of the ACM 38( 11): 33–
Li, Y.; Swersky, K.; and Zemel, R. 2015. Generative Moment
Matching Networks. In Proceedings of the 32nd International
Conference on Machine Learning (ICML’ 15), 1718–1727. Edinburgh, UK: PMLR.
Li, C.; Zhu, J.; and Zhang, B. 2016. Max-Margin Deep Generative Models for (Semi) Supervised Learning. Unpublished
arXiv preprint. arXiv:1611.07119 [ cs.CV]. Ithaca, NY: Cornell University Library.
Miyato, T.; Maeda, S.-i.; Koyama, M.; and Ishii, S. 2017. Virtual Adversarial Training: A Regularization Method for
Supervised and Semisupervised Learning. Unpublished arXiv preprint. arXiv:1704.03976 [ stat.ML]. Ithaca, NY: Cornell
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.;
Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A.
K.; Ostrovski, G; Peterson, S.; Beattie, C.; Sadik, A.;
Antonoglou, I.; King, H.; Kumaran, D.; Wierstra, D.; Legg,
S.; and Demis, H.; et al. 2015. Human-Level Control
Through Deep Reinforcement Learning. Nature 518(7540):
Mohamed, S., and Lakshminarayanan, B. 2016. Learning in
Implicit Generative Models. Unpublished arXiv preprint.
arXiv:1610.03483 [ stat.ML]. Ithaca, NY: Cornell University
Owens, T.; Saenko, K.; Chakrabarti, A.; Xiong, Y.; Zickler, T.;
and Darrell, T. 2011. Learning Object Color Models from
Multi-View Constraints. In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 169–176. Piscataway, NJ: Institute for Electrical and Electronics Engineers.
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016. Improved Techniques for Training Gans. In Advances in Neural Information Processing Systems
29, 2234–2242. December 5-10, 2016, Barcelona, Spain.
Shcherbatyi, I., and Andres, B. 2016. Convexification of
Learning from Constraints. Unpublished arXiv preprint.
arXiv:1602.06746 [cs.LG]. Ithaca, NY: Cornell University
Stewart, R., and Ermon, S. 2017. Label-Free Supervision of
Neural Networks with Physics and Domain Knowledge. In
Proceedings of the Thirty-First AAAI Conference on Artificial
Intelligence, 2576–2582. Palo Alto, CA: AAAI Press.
Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014. Sequence to
Sequence Learning with Neural Networks. In Advances in
Neural Information Processing Systems 27, 3104–3112. December 8-13, Montreal, Quebec, Canada.
Taskar, B.; Chatalbashev, V.; Koller, D.; and Guestrin, C.
2005. Learning Structured Prediction Models: A Large Margin Approach. In Proceedings of the 22nd International Conference on Machine Learning, 896–903. New York: Association
for Computing Machinery. doi.org/10.1145/1102351.
Yang, Y., and Ramanan, D. 2013. Articulated Human Detection with Flexible Mixtures of Parts. IEEE Transactions on
Pattern Analysis and Machine Intelligence 35( 12): 2878–2890.
Hongyu Ren is a senior undergraduate student from the
School of Electronics Engineering and Computer Science,
Peking University, China. He is interested in deep generative models and their applications in semisupervised/unsu-pervised learning settings. Currently, he is visiting Stanford
University as a research scholar.
Russell Stewart is a PhD student in the Department of
Computer Science at Stanford University. He is advised by
Stefano Ermon. His research focuses on supervising neural
networks without labels. Previously, he worked as a software
engineer at Microsoft on the Kinect and as a research advisor for MetaMind and Mathpix.
Jiaming Song is a second-year PhD student at Stanford University. He is advised by Stefano Ermon. His current research
interests are deep generative models, Bayesian inference,
and reinforcement learning. Previously he was an undergraduate student at Tsinghua University, Beijing, China.
Volodymyr Kuleshov is a post-doctoral scholar in the
Department of Computer Science at Stanford University.
His work explores applications of artificial intelligence in
healthcare and personalized medicine, as well as core
machine learning problems that arise in this field, touching
the areas of probabilistic modeling, reasoning under uncertainty, and deep learning.
Stefano Ermon is an assistant professor in the Department
of Computer Science at Stanford University. His research is
centered on probabilistic inference, statistical modeling of
data, large-scale combinatorial optimization, and robust
decision making under uncertainty, and is motivated by a
range of applications, in particular ones in the emerging
field of computational sustainability.