CSE 290 : Machine Learning Methods for NLP

Term: Winter Qtr 2019
Instructor: Ndapa Nakashole, CSE 4108
Lecture: Wednesdays 3:30pm-4:50pm, CSE 4258
Credits: 1



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.


The theme for this quarter is generalization.
Papers will fall under topics such as:



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