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Machine Learning for Natural Language Processing (ML-for-NLP)

This reading group focuses on Machine Learning techniques that may be applied to the field of Natural Language Processing. Participants are encouraged to suggest topics, papers, or tutorials (which need not involve any current application in NLP) by adding them to the lists below. Suggesting a paper does not constitute any sort of commitment to presenting that paper.

Meetings are approximately every week on Mondays. Meetings will be in 2.33 at 3pm unless otherwise stated. Announcements for this group will be made by email, and it is possible to sign up to the mailing list here.

Schedule for Fall 2017

December 12
  • TBD (Rafael)
December 5
  • TBD (Esma)
November 28
  • A Decomposable Attention Model for Natural Language Inference (Imigo)
November 21
  • Jointly Combining Implicit Constraints Improves Temporal Ordering (Nate)
November 14
  • Policy distillation (Mihai)
November 7
  • Distilling the Knowledge in a Neural Network (Naomi)
October 31
October 24
  • Learning hidden unit contributions for unsupervised acoustic model adaptation (Joachim)
October 17
October 10
  • A fully bayesian approach to unsupervised POS tagging (Craig)
October 3

  • Beam sampling with Infinite HMM (Joana)

September 26

  • Introduction to Bayesian statistics (Shay)

September 19

Schedule for Spring 2016

May 30

  • Crash-course on Clustering (Akash)

May 23

May 2

  • Composing Neural Networks (Shashi)
    • Learning to compose neural networks for question answering, Jacob Andreas, Marcus Rohrbach, Trevor Darrell and Dan Klein. NAACL 2016. http://arxiv.org/pdf/1601.01705v1.pdf

Apr 25

Apr 18

  • Planning meeting (Herman and Shashi)

Apr 11

  • A quick introduction to Torch (Stef)
    • Differences to Theano and Tensorflow

Apr 4
  • A quick introduction to TensorFlow (Rafael and Clara)
Mar 28

Mar 21

  • Generative Bayesian-ish Neural Network Stuffs (Lea)
    • "Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks" from Huang and Rao :link
Mar 7

  • Generative Bayesian-ish Neural Network Stuffs (Jianpeng = JP)
Feb 29

Feb 22

Feb 15

Feb 8

Feb 1

Jan 25

Jan 18

Jan 11

Schedule for Fall 2015

Dec 7

Nov 30

Nov 23

  • Neural Networks: RNNs (Herman)
    • Deep Learning, An MIT Press book in preparation. Ian Goodfellow, Aaron Courville, and Yoshua Bengio. http://goodfeli.github.io/dlbook/ Section 10.2.2 in the RNN chapter and Section 8.2.5 in the optimization chapter.

Nov 16

Nov 9

Nov 2

Oct 26

Oct 19

Oct 12

Oct 5

Previous Meetings

Visit Previous meetings for a list of previous meetings.

Useful Information

Visit Useful Information for information about useful tools and paper recommendations from previous meetings.

Other Reading Groups

Cambridge http://www.wiki.cl.cam.ac.uk/rowiki/NaturalLanguage/ReadingGroup

Heriot-Watt https://sites.google.com/site/hwmlreadinggroup/

Toronto http://learning.cs.toronto.edu/mlreading.html

  • Planning Meeting 2017:
    20170109_154856.jpg
Topic attachments
I Attachment Action Size Date Who Comment
jpgjpg 20170109_154856.jpg manage 5153.2 K 02 Feb 2017 - 08:46 Main.s1467463 Planning Meeting 2017
pdfpdf bkj-VBwalkthrough.pdf manage 3428.9 K 11 Oct 2012 - 15:56 Main.s0894398 Variational EM
jpgjpg image.jpg manage 1749.3 K 12 Oct 2015 - 12:13 Main.snaraya2 planning-15-16-1
jpgjpg image_1.jpg manage 1652.0 K 12 Oct 2015 - 12:14 Main.snaraya2 planning-15-16-2
jpgjpg jan16-1.jpg manage 1752.7 K 14 Jan 2016 - 15:53 Main.snaraya2 Jan 2016: planning meeting A
jpgjpg jan16-2.jpg manage 1760.8 K 14 Jan 2016 - 15:54 Main.snaraya2  
pdfpdf lstm_intro.pdf manage 347.8 K 24 Nov 2014 - 16:03 Main.s0830457 Slides for the LSTM intro
pdfpdf lstm_intro_2015.pdf manage 356.9 K 30 Nov 2015 - 09:21 Main.s0830457 Updated version of LSTM introduction slides
pdfpdf planning_fall2016.pdf manage 264.0 K 19 Sep 2016 - 15:26 Main.s1333293  
pdfpdf variational_inference_primer.pdf manage 137.2 K 26 Feb 2016 - 09:34 Main.s1314728  
Topic revision: r110 - 02 Feb 2017 - 08:46:12 - Main.s1467463
 
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