[talks] Jordan Ash will present his general exam on Tuesday, May 9th, 2017 at 10am in CS 401.

Nicki Gotsis ngotsis at CS.Princeton.EDU
Tue May 2 10:12:31 EDT 2017

Jordan Ash will present his general exam on Tuesday, May 9th, 2017 at 10am in CS 401. 

The members of his committee are: Barbara Engelhardt (adviser), Sebastian Seung, and Tom Funkhouser. 

Everyone is invited to attend his talk, and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. 

Domain adaptation addresses the problem created when training data is generated by a so-called source distribution, but test data is generated by a significantly different target distribution. In this work, we present approximate label matching (ALM), a new unsupervised domain adaptation technique that creates and leverages a rough labeling on the test samples, then uses these noisy labels to learn a transformation that aligns the source and target samples. We show that the transformation estimated by ALM has favorable properties compared to transformations estimated by other methods, which do not use any kind of target labeling. Our model is regularized by requiring that a classifier trained to discriminate source from transformed target samples cannot distinguish between the two. We experiment with ALM on simulated and real data, and show that it outperforms techniques commonly used in the field. 

Keywords: Generative Adversarial Networks, Domain Adaptation, Deep Learning 

Reading List: 

    1. Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. "Learning representations by back-propagating errors." Cognitive modeling (1988) 
    2. LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324. 
    3. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444. 
    4. Ganin, Yaroslav, et al. "Domain-adversarial training of neural networks." Journal of Machine Learning Research 17.59 (2016): 1-35. 
    5. Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014. 
    6. Denton, Emily L., Soumith Chintala, and Rob Fergus. "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks." Advances in neural information processing systems. 2015. 
    7. Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015). 
    8. Reed, Scott, et al. "Learning to disentangle factors of variation with manifold interaction." Proceedings of the 31st International Conference on Machine Learning (ICML-14). 2014. 
    9. Liu, Ming-Yu, and Oncel Tuzel. "Coupled generative adversarial networks." Advances in Neural Information Processing Systems. 2016. 
    10. Mathieu, Michael, Camille Couprie, and Yann LeCun. "Deep multi-scale video prediction beyond mean square error." arXiv preprint arXiv:1511.05440 (2015). 
    11. Lamb, Alex M., et al. "Professor forcing: A new algorithm for training recurrent networks." Advances In Neural Information Processing Systems. 2016.

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