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Deep belief nets experiments and some ideas.

Deep belief nets experiments and some ideas. Karol Gregor NYU/Caltech. Outline. DBN Image database experiments Temporal sequences. Deep belief network. Backprop. Labels. H3. H2. H1. Input. Preprocessing – Bag of words of SIFT. With: Greg Griffin (Caltech). Images.

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Deep belief nets experiments and some ideas.

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  1. Deep belief nets experiments and some ideas. Karol Gregor NYU/Caltech

  2. Outline DBN Image database experiments Temporal sequences

  3. Deep belief network Backprop Labels H3 H2 H1 Input

  4. Preprocessing – Bag of words of SIFT With: Greg Griffin (Caltech) Images Features (using SIFT) Bag of words Image1 Image2 Word1 23 11 Word2 12 55 Word3 92 33 … … … Group them (e.g. K-means)

  5. 13 Scenes Database – Test error

  6. Train error

  7. - Pre-training on larger dataset - Comparison to svm, spm

  8. Explicit representations?

  9. Compatibility between databases Pretraining: Corel database Supervised training: 15 Scenes database

  10. Temporal Sequences

  11. Simple prediction Y t W t-1 t-2 t-3 X Supervised learning

  12. With hidden units(need them for several reasons) G H t-1,t-2,t-3 t t-1,t-2,t-3 t X Y Memisevic, R. F. and Hinton, G. E., Unsupervised Learning of Image Transformations. CVPR-07

  13. Example pred_xyh_orig.m

  14. Additions G H t-1 t t-1 t X Y Sparsity: When inferring the H the first time, keep only the largest n units on Slow H change: After inferring the H the first time, take H=(G+H)/2

  15. Examples pred_xyh.m present_line.m present_cross.m

  16. Hippocampus Cortex+Thalamus Muscles (through sub-cortical structures) Senses e.g. Eye (through retina, LGN) e.g. See: Jeff Hawkins: On Intelligence

  17. Cortical patch: Complex structure(not a single layer RBM) From Alex Thomson and Peter Bannister, (see numenta.com)

  18. Desired properties

  19. 1) Prediction A B C D E F G H J K L E F H

  20. 2) Explicit representations for sequences VISIONRESEARCH time

  21. 3) Invariance discovery e.g. complex cell time

  22. 4) Sequences of variable length VISIONRESEARCH time

  23. 5) Long sequences Layer1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ? ? 2 2 2 2 2 2 2 2 2 2 Layer2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 1 2 3 5 8 13 21 34 55 89 144

  24. 6) Multilayer - Inferred only after some time VISIONRESEARCH time

  25. 7) Smoother time steps

  26. 8) Variable speed - Can fit a knob with small speed range

  27. 9) Add a clock for actual time

  28. Hippocampus Cortex+Thalamus Muscles (through sub-cortical structures) Senses e.g. Eye (through retina, LGN)

  29. Hippocampus Cortex+Thalamus In Addition • Top down attention • Bottom up attention • Imagination • Working memory • Rewards Muscles (through sub-cortical structures) Senses e.g. Eye (through retina, LGN)

  30. Training data • Videos • Of the real world • Simplified: Cartoons (Simsons) • A robot in an environment • Problem: Hard to grasp objects • Artificial environment with 3D objects that are easy to manipulate (e.g. Grand theft auto IV with objects)

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