Neural Nets for NLP 2017 - Convolutional Networks for Text

Neural Nets for NLP 2017 - Convolutional Networks for Text

Graham Neubig via YouTube Direct link

An Aside: Nonlinear Functions • Proper choice of a non-linear function is essential in stacked networks

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13 of 17

An Aside: Nonlinear Functions • Proper choice of a non-linear function is essential in stacked networks

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Classroom Contents

Neural Nets for NLP 2017 - Convolutional Networks for Text

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  1. 1 Intro
  2. 2 An Example Prediction Problem: Sentence Classification
  3. 3 A First Try: Bag of Words (BOW)
  4. 4 Continuous Bag of Words (CBOW) movie
  5. 5 What do Our Vectors Represent?
  6. 6 Why Bag of n-grams?
  7. 7 What Problems w/ Bag of n-grams?
  8. 8 Time Delay Neural Networks (Waibel et al. 1989)
  9. 9 Convolutional Networks (LeCun et al. 1997)
  10. 10 Standard conv2d Function
  11. 11 Stacked Convolution
  12. 12 Dilated Convolution (e.g. Kalchbrenner et al. 2016)
  13. 13 An Aside: Nonlinear Functions • Proper choice of a non-linear function is essential in stacked networks
  14. 14 Why (Dilated) Convolution for Modeling Sentences? • In contrast to recurrent neural networks (next class)
  15. 15 Example: Dependency Structure
  16. 16 Why Model Sentence Pairs?
  17. 17 Siamese Network (Bromley et al. 1993)

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