CSE 290 : Machine Learning Methods for NLP
Term: Winter Qtr 2019 |
Course Description
The course involves reading and discussing current research papers, once a week. This course assumes background in basic machine learning. Prior NLP experience is helpful, but not required.Theme
The theme for this quarter is generalization.Enrollment
Grading
Date | Topic/Readings | Discussion Leader |
---|---|---|
Jan 9 | Paper 0 | |
Yosinski, NIPS 2014 How transferable are features in deep neural networks? | All | |
Jan 16 | Paper 1 | |
S. Thrun, NIPS 1995 Is learning the n-th thing any easier than learning the first? | Ndapa Nakashole | |
Jan 23 | Paper 2 | |
Finn et al. ICML 2017 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | Bodhisattwa Majumder | |
Jan 30 | Paper 3 | |
Ganin et al. JMLR 2016 Domain-Adversarial Training of Neural Networks | Khalil Mrini & Yutong Shao | |
Feb 6 | Paper 4 | |
Luo et al. NIPS 2017 Label Efficient Learning of Transferable Representations across Domains and Tasks | Aishma Raghu | |
Feb 13 | Paper 5 | |
Zhang et al. TACL 2017 Aspect-augmented Adversarial Networks for Domain Adaptation | Yuguang Lin | |
Feb 20 | Paper 6 | |
Collobert et al. JMLR 2011 Natural Language Processing (Almost) from Scratch | Aman, Achpal | |
Feb 27 | Paper 7 | |
Daume ACL 2007 Frustratingly Easy Domain Adaptation | Aashi Jain | |
Mar 06 | Paper 8 | |
Pan et al. TKDE 2010 Survey on Transfer Learning | Yifan Zhou | |
Mar 12 | Paper 9 | |
Stewart & Ermon. AAAI 2017 Label-Free Supervision of Neural Networks with Physics and Domain Knowledge | Hao Liu & Jingwu Xu |