Presentation on learned representations for the M.Sc Lancaster data science class on features engineering

1. Learned Representations
@ejlbell

2. Me
2

3. Lyst
3

4. 4
Feature engineering

5. 5
Type of transform Examples
Unary exp, log, sqrt
Normalisation mean, variance
Aggregation count, sum, mean
Dimensional reduction PCA, clustering, manifold
Text tagging, parsing, stemming
Image
histograms, key points, super pixels,
segmentation
Others temporal / spatial

6. AI must fundamentally understand the
world around us and this can only be
achieved if it can learn to identify and
disentangle the underlying explanatory
factors hidden in the observed milieu of
low-level sensory data.
2014 - Representation Learning: A Review and New Perspectives. - Bengio et al.

7. Human ingenuity and prior knowledge
Feature Engineering
Representation Learning
Sufficiently powerful models that learn “good” feature transforms

8. 8
Deep Learning

9. Image Filters
-1
-2
-1
0
0
0
+1
+2
+1
-1
0
+1
-2
0
+2
-1
0
+1

10. VGG
Representations
2015 - Very Deep Convolutional Networks for
Large-Scale Image Recognition. Simonyan and
Zisserman

11. Regularization
11

12. 12
Representations

13. 13
Cat
Dog
Male
Female

14. 14
Cat
Dog
Male
Female

15. 15
Cat
Dog
Male
Female

16. Content

17. Similar to ‘dress’

20. 20
Applications

22. 22
a group of young girls standing next
to each other on the beach
A clock tower with a clock on top of it

23. A bunch of bananas hanging from a tree

24. 24

25. 25

26. 26
+ =

27. 27

29. 29

30. 30

31. 31

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34. 34

35. 35
1 0 0
1 0 0
0 1 0
0.7 -0.6 -8
0 0 1
0 0 1
0 1 0
-8 -0.6 0.7

36. But … not a magic bullet
36
* Expensive in terms of hardware
* Expensive in terms of time
* Expensive in terms of expertise
* Expensive in terms of labelled data
* Blackbox, can’t do inference

37. Thanks
especially to all the people I stole this content from

38. Questions?
@ejlbell