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

UCSD CSE

Announcements

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.
Papers will fall under topics such as:

Enrollment

Grading


Syllabus
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 TBD
Feb 13 Paper 5
Zhang et al. TACL 2017 Aspect-augmented Adversarial Networks for Domain Adaptation TBD
Feb 20 Paper 6
Girshick et al. CVPR 2014 Rich feature hierarchies for accurate object detection and semantic segmentation TBD
Feb 27 Paper 7
Saenko et al. ECCV 2010 Adapting Visual Category Models to New Domains TBD