Bach, Stephen H., Bryan He, Alexander Ratner, and Christopher Ré. 2017.
“Learning the Structure of Generative Models Without Labeled Data.” In
Proceedings of the 34th International Conference on Machine Learning.
International Conference on Machine Learning, Sydney, Australia.
http://arxiv.org/abs/1703.00854.
Delalleau, Olivier, Yoshua Bengio, and Nicolas Le Roux. 2005.
“Efficient Nonparametric Function Induction in Semi-Supervised Learning.” In
In Proc. Artificial Intelligence and Statistics.
Citeseer.
http://www.iro.umontreal.ca/~lisa/bib/pub_subject/unsupervised/pointeurs/semisup_aistats2005.pdf.
Fonseca, Eduardo, Manoj Plakal, Daniel P. W. Ellis, Frederic Font, Xavier Favory, and Xavier Serra. 2019.
“Learning Sound Event Classifiers from Web Audio with Noisy Labels.” January 4, 2019.
http://arxiv.org/abs/1901.01189.
Jung, Alexander, Alfred O. Hero III, Alexandru Mara, and Saeed Jahromi. 2016.
“Semi-Supervised Learning via Sparse Label Propagation.” December 5, 2016.
http://arxiv.org/abs/1612.01414.
Karpathy, Andrej, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei. 2014.
“Large-Scale Video Classification with Convolutional Neural Networks.” In
Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 1725–32.
CVPR ’14.
Washington, DC, USA:
IEEE Computer Society.
https://doi.org/10.1109/CVPR.2014.223.
Kumar, Anurag, and Bhiksha Raj. 2016.
“Audio Event Detection Using Weakly Labeled Data.” In
Proceedings of the 2016 ACM on Multimedia Conference, 1038–47.
MM ’16.
New York, NY, USA:
ACM.
https://doi.org/10.1145/2964284.2964310.
———. 2017.
“Deep CNN Framework for Audio Event Recognition Using Weakly Labeled Web Data.” July 9, 2017.
http://arxiv.org/abs/1707.02530.
Kügelgen, Julius, Alexander Mey, Marco Loog, and Bernhard Schölkopf. 2020.
“Semi-Supervised Learning, Causality, and the Conditional Cluster Assumption.” In
Conference on Uncertainty in Artificial Intelligence, 1–10.
PMLR.
http://proceedings.mlr.press/v124/kugelgen20a.html.
Li, Z., and J. Tang. 2015.
“Weakly Supervised Deep Metric Learning for Community-Contributed Image Retrieval.” IEEE Transactions on Multimedia 17 (11): 1989–99.
https://doi.org/10.1109/TMM.2015.2477035.
Misra, Ishan, C. Lawrence Zitnick, Margaret Mitchell, and Ross Girshick. 2015.
“Seeing Through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels.” In
Proceedings of CVPR.
http://arxiv.org/abs/1512.06974.
Papandreou, George, Liang-Chieh Chen, Kevin Murphy, and Alan L. Yuille. n.d.
“Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation.” Accessed July 18, 2017.
http://www.cs.jhu.edu/~ayuille/Pubs15/PapandreouChen_WeaklySemiSupervised_v2%20(1).pdf.
Ratner, Alexander J, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré. 2016.
“Data Programming: Creating Large Training Sets, Quickly.” In
Advances in Neural Information Processing Systems 29, edited by D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, 3567–75.
Curran Associates, Inc. http://papers.nips.cc/paper/6523-data-programming-creating-large-training-sets-quickly.pdf.
Ratner, Alexander, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017.
“Snorkel: Rapid Training Data Creation with Weak Supervision.” Proceedings of the VLDB Endowment 11 (3): 269–82.
https://doi.org/10.14778/3157794.3157797.
Varma, Paroma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin, and Christopher Ré. 2017.
“Inferring Generative Model Structure with Static Analysis.” In
Advances In Neural Information Processing Systems.
http://arxiv.org/abs/1709.02477.
Wu, F., Z. Wang, Z. Zhang, Y. Yang, J. Luo, W. Zhu, and Y. Zhuang. 2015.
“Weakly Semi-Supervised Deep Learning for Multi-Label Image Annotation.” IEEE Transactions on Big Data 1 (3): 109–22.
https://doi.org/10.1109/TBDATA.2015.2497270.
Zhou, Dengyong, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf. 2003.
“Learning with Local and Global Consistency.” In
Proceedings of the 16th International Conference on Neural Information Processing Systems, 321–28.
NIPS’03.
Cambridge, MA, USA:
MIT Press.
http://papers.nips.cc/paper/2506-learning-with-local-and-global-consistency.pdf.
Zhu, Xiaojin, and Zoubin Ghahramani. 2002.
“Learning from Labeled and Unlabeled Data with Label Propagation.” http://pages.cs.wisc.edu/ jerryzhu/pub/CMU-CALD-02-107.pdf.
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