Schedule for Spring 2015

Jan 12

  • Planning meeting
    • Following previous semester, this semester, we will read more about some of the ML techniques that people use for their work. They will lead the discussion on that particular topic.

Jan 19

Jan 26

Feb 2

  • Compositional Vector Grammar : Overview (Shashi)
    • http://nlp.stanford.edu/pubs/SocherBauerManningNg_ACL2013.pdf

Feb 9

Feb 16

  • Networking Social
Feb 23

  • Cancelled (ACL deadline)

Mar 02

Mar 09

Mar 16

Mar 23

Mar 30

Apr 6

  • Cancelled (Easter Monday)

Apr 13

Apr 20

Apr 27

May 4

  • Tools Session: General (Siva, Nathan, Bharat)
    • Latex tricks, bash commands, pdf tools, ML tools

May 11

  • Tolls Session: Neural Networks (Daniel)
    • Theano for Neural Networks

Misc. other topics

  • Neural Nets : Hessian-free optimization (Daniel)
  • Neural Nets : Bayesian Models for training (Daniel)
  • Sequence models through Neural Networks: Theano (Daniel)
  • Un-supervised grammar induction
  • Unsupervised sequence segmentation : Bayesian vs. Neural Nets (Lea & Daniel)
  • Shay's Bayesian NLP book
  • Discriminative Models : Perceptron (Bharat)
  • Discriminative Models : Stochastic Gradient Descent

Schedule for Monsoon 2014

Sep 18

  • Planning meeting
    • Following previous semester, this semester, we will read more about some of the ML techniques that people use for their work. They will lead the discussion on that particular topic.

Sep 25

  • Paper discussion (Lea)
    • Demystifying Information-Theoretic Clustering (Ver Steeg et al, 2014)
      • http://jmlr.org/proceedings/papers/v32/steeg14.pdf

Sep 29

  • no meeting

Oct 6

  • Unsupervised Grammar induction / Adaptor Grammars (Introductory paper : Annie)
    • Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models
      • http://homepages.inf.ed.ac.uk/sgwater/papers/nips07-adaptor.pdf
    • Slides:
      • http://web.science.mq.edu.au/~mjohnson/papers/Johnson-learning-rules-10-talk.pdf

Oct 13

Oct 20

Oct 27

Nov 3

  • no meeting

Nov 10

Nov 17

Nov 24

Dec 1

  • Long Short Term Memory (LSTM) Neural Nets (Daniel and XingXing )

Dec 8

Misc. other topics
  • Perceptron
  • "Learning to Search"
  • (Incremental) (Bayesian) learning in high-dimensional space
  • (Introductory) Topic Modeling / Gibbs Sampling
  • MERT/MIRA
  • Analyzing the Errors of Unsupervised Learning (Liang and Klein, 2008)
  • Graph Theory
  • Inference on Graphs (Philip)

Previous Meetings

Useful Information

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

Other Reading Groups


This topic: MLforNLP > WebHome > PreviousMeetings201415
Topic revision: r1 - 12 Oct 2015 - 12:09:20 - Main.snaraya2
 
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