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Simple Introduction to

  AutoEncoder
             Lang Jun
Deep Learning Study Group, HLT, I2R
         17 August, 2012
Outline
1. What is AutoEncoder?
   Input = decoder(encoder(input))

2. How to train AutoEncoder?

  pre-training

3. What can it be used for?

  reduce dimensionality
                                     2/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          3/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          4/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          5/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          6/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          7/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          8/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          9/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          10/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          11/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          12/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          13/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          14/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          15/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          16/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          17/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          18/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          19/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          20/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          21/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          22/34
1. What is AutoEncoder?
➢   Multilayer neural net simple review




                                          23/34
1. What is AutoEncoder?
➢   Multilayer neural net with target output = input
➢   Reconstruction=decoder(encoder(input))




➢   Minimizing reconstruction error
➢   Probable inputs have small reconstruction error
                                                       24/34
2. How to train AutoEncoder?
       Hinton (2006) Science Paper

Restricted Boltzmann Machine
(RBM)




                                     25/34
2. How to train AutoEncoder?
                      Hinton (2006) Science Paper
restricted Boltzmann machine




                                                    26/34
Effective deep learning became
possible through unsupervised pre-
              training
  Purely supervised neural net                 With unsupervised pre‐training
                                        (with RBMs and Denoising Auto-Encoders)




                                                                           27/34
           0–9 handwritten digit recognition error rate (MNIST data)
Why is unsupervised pre-training working so well?

Regularization hypothesis:
   Representations good
for P(x) are good for P(y|x)
Optimization hypothesis:
   Unsupervised initializations
start near better local minimum
 of supervised training error
      Minima otherwise not
achievable by random
initialization




Erhan, Courville, Manzagol, Vincent, Bengio (JMLR, 2010)
                                                           28/34
3. What can it be used for?
     illustration for images




                               29/34
3. What can it be used for?
                  document retrieval
                            output
2000 reconstructed counts   vector
                                     • We train the neural network
    500 neurons                        to reproduce its input vector
                                       as its output
                                     • This forces it to compress as
      250 neurons                      much information as possible
                                       into the 10 numbers in the
                                       central bottleneck.
           10                        • These 10 numbers are then a
                                       good way to compare
                                       documents.
      250 neurons
                                        – See Ruslan
                                           Salakhutdinov’s talk
     500 neurons

                            input                                30/34
  2000 word counts          vector
3. What can it be used for?
                     visualize documents
                                                                  output
                                      2000 reconstructed counts   vector
•   Instead of using codes to
    retrieve documents, we can            500 neurons
    use 2-D codes to visualize sets
    of documents.
     – This works much better               250 neurons
       than 2-D PCA

                                                  2


                                            250 neurons


                                           500 neurons

                                                                  input 31/34
                                        2000 word counts          vector
First compress all documents to 2 numbers using a type of PCA
                   Then use different colors for different
document categories




                                                                32/34
First compress all documents to 2 numbers with an autoencoder
              Then use different colors for different document
categories




                                                                 33/34
3. What can it be used for?
       transliteration




                              34/34
Thanks for your attendance


      Looking forward to present
     Recursive AutoEncoder



                                   35/34

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Simple Introduction to AutoEncoder

  • 1. Simple Introduction to AutoEncoder Lang Jun Deep Learning Study Group, HLT, I2R 17 August, 2012
  • 2. Outline 1. What is AutoEncoder? Input = decoder(encoder(input)) 2. How to train AutoEncoder? pre-training 3. What can it be used for? reduce dimensionality 2/34
  • 3. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 3/34
  • 4. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 4/34
  • 5. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 5/34
  • 6. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 6/34
  • 7. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 7/34
  • 8. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 8/34
  • 9. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 9/34
  • 10. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 10/34
  • 11. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 11/34
  • 12. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 12/34
  • 13. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 13/34
  • 14. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 14/34
  • 15. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 15/34
  • 16. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 16/34
  • 17. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 17/34
  • 18. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 18/34
  • 19. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 19/34
  • 20. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 20/34
  • 21. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 21/34
  • 22. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 22/34
  • 23. 1. What is AutoEncoder? ➢ Multilayer neural net simple review 23/34
  • 24. 1. What is AutoEncoder? ➢ Multilayer neural net with target output = input ➢ Reconstruction=decoder(encoder(input)) ➢ Minimizing reconstruction error ➢ Probable inputs have small reconstruction error 24/34
  • 25. 2. How to train AutoEncoder? Hinton (2006) Science Paper Restricted Boltzmann Machine (RBM) 25/34
  • 26. 2. How to train AutoEncoder? Hinton (2006) Science Paper restricted Boltzmann machine 26/34
  • 27. Effective deep learning became possible through unsupervised pre- training Purely supervised neural net With unsupervised pre‐training (with RBMs and Denoising Auto-Encoders) 27/34 0–9 handwritten digit recognition error rate (MNIST data)
  • 28. Why is unsupervised pre-training working so well? Regularization hypothesis: Representations good for P(x) are good for P(y|x) Optimization hypothesis: Unsupervised initializations start near better local minimum of supervised training error Minima otherwise not achievable by random initialization Erhan, Courville, Manzagol, Vincent, Bengio (JMLR, 2010) 28/34
  • 29. 3. What can it be used for? illustration for images 29/34
  • 30. 3. What can it be used for? document retrieval output 2000 reconstructed counts vector • We train the neural network 500 neurons to reproduce its input vector as its output • This forces it to compress as 250 neurons much information as possible into the 10 numbers in the central bottleneck. 10 • These 10 numbers are then a good way to compare documents. 250 neurons – See Ruslan Salakhutdinov’s talk 500 neurons input 30/34 2000 word counts vector
  • 31. 3. What can it be used for? visualize documents output 2000 reconstructed counts vector • Instead of using codes to retrieve documents, we can 500 neurons use 2-D codes to visualize sets of documents. – This works much better 250 neurons than 2-D PCA 2 250 neurons 500 neurons input 31/34 2000 word counts vector
  • 32. First compress all documents to 2 numbers using a type of PCA Then use different colors for different document categories 32/34
  • 33. First compress all documents to 2 numbers with an autoencoder Then use different colors for different document categories 33/34
  • 34. 3. What can it be used for? transliteration 34/34
  • 35. Thanks for your attendance Looking forward to present Recursive AutoEncoder 35/34