presentation given at the Deep Learning Meetup Stockholm, 20 April 2015

1. @graphific
Roelof Pieters
Deep Learning:
a (non-techy) birds-eye
view
20
April
2015
Stockholm
Deep Learning
Slides at:
http://www.slideshare.net/roelofp/deep-learning-a-birdseye-view

2. • Deals with “construction and study of systems that can
learn from data”
Refresher: Machine Learning ???
A computer program is said to learn from
experience (E) with respect to some class
of tasks (T) and performance measure
(P), if its performance at tasks in T, as
measured by P, improves with experience E
— T. Mitchell 1997
2

3. Improving some task T
based on experience E with
respect to performance
measure P.
Deep Learning = Machine Learning
Learning denotes changes in the system
that are adaptive in the sense that they
enable the system to do the same task
(or tasks drawn from a population of
similar tasks) more effectively the next
time.
— H. Simon 1983
"Why Should Machines Learn?” in Mitchell 1997
— T. Mitchell 1997
3

4. Representation learning
Attempts to automatically learn
good features or
representations
Deep learning
Attempt to learn multiple levels
of representation of increasing
complexity/abstraction
Deep Learning: What?
4

5. Deep Learning ??
5

6. Machine Learning ??
Traditional Programming:
Data
Program
Output
6
Computer

7. Machine Learning ??
Traditional Programming:
Data
Program
Output
Data
Program
Output
Machine Learning:
7
(labels)
(“weights”/model)
Computer
Computer

8. Machine Learning ??
8
• Most machine learning
methods work well because of
human-designed/hand-
engineered features
(representations)
• machine learning ->
optimising weights to best
make a final prediction

9. Typical ML Regression
Deep Learning: Why?

10. Neural NetTypical ML Regression
Deep Learning: Why?

11. Machine ->Deep Learning ??

12. Machine ->Deep Learning ??
DEEP NET
(DEEP LEARNING)

14. Biological Inspiration
14

15. Deep Learning: Why?

16. Deep Learning is everywhere…

18. Deep Learning in the News

19. (source: Google Trends)
19

20. (source: arXiv bookworm, Culturomics)
Scientific Articles:

25. Why Now?
• Inspired by the architectural depth of the brain,
researchers wanted for decades to train deep
multi-layer neural networks.
• No successful attempts were reported before 2006
…Exception: convolutional neural networks,
LeCun 1998
• SVM: Vapnik and his co-workers developed the
Support Vector Machine (1993) (shallow
architecture).
• Breakthrough in 2006!
25

26. Renewed Interest: 1990s
• Learning multiple layers
• “Back propagation”
• Can “theoretically” learn any function!
But…
• Very slow and inefficient
• SVMs, random forests, etc. SOTA
26

27. 2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
27

28. 2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
28

29. 2006 Breakthrough
29

30. 2006 Breakthrough
30
Growth of datasets

31. 2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
31

32. 2006 Breakthrough
32
vs

33. Rise of Raw Computation Power

34. 2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep
architectures
34

35. 2006 Breakthrough
Stacked Restricted Boltzman Machines* (RBM)
Hinton, G. E, Osindero, S., and Teh, Y. W. (2006).
A fast learning algorithm for deep belief nets.
Neural Computation, 18:1527-1554.
Stacked Autoencoders (AE)
Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. (2007).
Greedy Layer-Wise Training of Deep Networks,
Advances in Neural Information Processing Systems 19
* called Deep Belief Networks (DBN) 35

36. Deep Learning for the Win!

37. • 1.2M images with 1000 object categories
• AlexNet of uni Toronto: 15% error rate vs 26% for
2th placed (traditional CV)
Impact on Computer Vision
ImageNet Challenge 2012

38. Impact on Computer Vision
(from Clarifai)

39. Impact on Computer Vision

40. 40
Classification results on ImageNet 2012
Team Year Place Error (top-5) Uses external
data
SuperVision 2012 - 16.4% no
SuperVision 2012 1st 15.3% ImageNet 22k
Clarifai 2013 - 11.7% no
Clarifai 2013 1st 11.2% ImageNet 22k
MSRA 2014 3rd 7.35% no
VGG 2014 2nd 7.32% no
GoogLeNet 2014 1st 6.67% no
Final Detection Results
Team Year Place mAP e x t e r n a l
data
ensemble c o n t e x t u a l
model
approach
UvA-Euvision 2013 1st 22.6% none
? yes F i s h e r
vectors
Deep Insight 2014 3rd 40.5% I L S V R C 1 2
Classification
+ Localization
3 models yes ConvNet
C U H K
DeepID-Net
2014 2nd 40.7% I L S V R C 1 2
Classification
+ Localization
? no ConvNet
GoogLeNet 2014 1st 43.9% I L S V R C 1 2
Classification
6 models no ConvNet
Detection results
source: Szegedy et al. Going deeper with convolutions (GoogLeNet ), ILSVRC2014, 19 Sep 2014

41. 41source: Szegedy et al. Going deeper with convolutions (GoogLeNet ), ILSVRC2014, 19 Sep 2014
GoogLeNet
Convolution
Pooling
Softmax
Other
Winners of:
Large Scale Visual Recognition Challenge 2014
(ILSVRC2014)
19 September 2014
GoogLeNet
Convolution
Pooling
Softmax
Other

42. 42source: Szegedy et al. Going deeper with convolutions (GoogLeNet ), ILSVRC2014, 19 Sep 2014
Inception
Width of inception modules ranges from 256 filters (in early modules) to 1024 in top inception
modules.
Can remove fully connected layers on top completely
Number of parameters is reduced to 5 million
256 480 480
512
512 512
832 832 1024
Computional cost is increased by
less than 2X compared to
Krizhevsky’s network. (<1.5Bn
operations/evaluation)

43. Impact on Computer Vision
Latest State of the Art:

44. Computer Vision: Current State of the Art

45. Impact on Audio Processing
45
First public Breakthrough with Deep Learning in 2010
Dahl et al. (2010)

46. Impact on Audio Processing
46
First public Breakthrough with Deep Learning in 2010
Dahl et al. (2010)
-33%! -32%!

47. Impact on Audio Processing
47
Speech Recognition

48. Impact on Audio Processing
48
TIMIT Speech Recognition
(from: Clarifai)

49. Impact on Audio Processing

50. C&W 2011
Impact on Natural Language Processing
Pos: Toutanova et al.
2003)
Ner: Ando & Zhang
2005
C&W 2011

51. Impact on Natural Language Processing
Named Entity Recognition:

52. Deep Learning: Who’s to blame?

53. 53
Deep Learning: Who’s to blame?

54. Deep Architectures can be representationally efficient
• Fewer computational units for same function
Deep Representations might allow for a hierarchy or
representation
• Allows non-local generalisation
• Comprehensibility
Multiple levels of latent variables allow combinatorial
sharing of statistical strength
54
Deep Learning: Why?

55. — Andrew Ng
“I’ve worked all my life in
Machine Learning, and I’ve
never seen one algorithm knock
over benchmarks like Deep
Learning”
Deep Learning: Why?
55

56. Biological Justification
Deep Learning = Brain “inspired”
Audio/Visual Cortex has multiple stages == Hierarchical

57. Different Levels of Abstraction
57

58. Hierarchical Learning
• Natural progression
from low level to high
level structure as seen
in natural complexity
Different Levels of Abstraction
Feature Representation
58

59. Hierarchical Learning
• Natural progression
from low level to high
level structure as seen
in natural complexity• Easier to monitor what
is being learnt and to
guide the machine to
better subspaces
Different Levels of Abstraction
Feature Representation
59

60. Hierarchical Learning
• Natural progression
from low level to high
level structure as seen
in natural complexity• Easier to monitor what
is being learnt and to
guide the machine to
better subspaces
• A good lower level
representation can be
used for many distinct
tasks
Different Levels of Abstraction
Feature Representation
60

61. Hierarchical Learning
• Natural progression
from low level to high
level structure as seen
in natural complexity• Easier to monitor what
is being learnt and to
guide the machine to
better subspaces
• A good lower level
representation can be
used for many distinct
tasks
Different Levels of Abstraction
Feature Representation
61

62. Different Levels of Abstraction

63. Classic Deep Architecture
Input layer
Hidden layers
Output layer

64. Modern Deep Architecture
Input layer
Hidden layers
Output layer
movie time:
http://www.cs.toronto.edu/~hinton/adi/index.htm

65. Hierarchies
Efficient
Generalization
Distributed
Sharing
Unsupervised*
Black Box
Training Time
Major PWNAGE!
Much Data
Why go Deep ?
65

66. No More Handcrafted Features !
66

67. [Kudos to Richard Socher, for this eloquent summary :) ]
• Manually designed features are often over-specified, incomplete
and take a long time to design and validate
• Learned Features are easy to adapt, fast to learn
• Deep learning provides a very flexible, (almost?) universal,
learnable framework for representing world, visual and
linguistic information.
• Deep learning can learn unsupervised (from raw text/audio/
images/whatever content) and supervised (with specific labels
like positive/negative)
Why Deep Learning ?

68. Deep Learning: Future Developments
Currently an explosion of developments
• Hessian-Free networks (2010)
• Long Short Term Memory (2011)
• Large Convolutional nets, max-pooling (2011)
• Nesterov’s Gradient Descent (2013)
Currently state of the art but...
• No way of doing logical inference (extrapolation)
• No easy integration of abstract knowledge
• Hypothetic space bias might not conform with reality
68

69. Deep Learning: Future Challenges
a
69
Szegedy, C., Wojciech, Z., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R. (2013) Intriguing
properties of neural networks
L: correctly identified, Center: added noise x10, R: “Ostrich”

72. as PhD candidate KTH/CSC:
“Always interested in discussing
Machine Learning, Deep
Architectures, Graphs, and
Language Technology”
In touch!
roelof@kth.se
www.csc.kth.se/~roelof/
Data Science ConsultancyAcademic/Research
roelof@gve-systems.com
www.gve-systems.com
72
Gve Systems
Graph Technologies

73. • Theano - CPU/GPU symbolic expression compiler in
python (from LISA lab at University of Montreal).
http://deeplearning.net/software/theano/
• Pylearn2 - library designed to make machine learning
research easy. http://deeplearning.net/software/
pylearn2/
• Torch - Matlab-like environment for state-of-the-art
machine learning algorithms in lua (from Ronan
Collobert, Clement Farabet and Koray Kavukcuoglu)
http://torch.ch/
• more info: http://deeplearning.net/software links/
Wanna Play ?
Wanna Play ? General Deep Learning
73

74. • RNNLM (Mikolov)
http://rnnlm.org
• NB-SVM
https://github.com/mesnilgr/nbsvm
• Word2Vec (skipgrams/cbow)
https://code.google.com/p/word2vec/ (original)
http://radimrehurek.com/gensim/models/word2vec.html (python)
• GloVe
http://nlp.stanford.edu/projects/glove/ (original)
https://github.com/maciejkula/glove-python (python)
• Socher et al / Stanford RNN Sentiment code:
http://nlp.stanford.edu/sentiment/code.html
• Deep Learning without Magic Tutorial:
http://nlp.stanford.edu/courses/NAACL2013/
Wanna Play ? NLP
74

75. • cuda-convnet2 (Alex Krizhevsky, Toronto) (c++/
CUDA, optimized for GTX 580)
https://code.google.com/p/cuda-convnet2/
• Caffe (Berkeley) (Cuda/OpenCL, Theano, Python)
http://caffe.berkeleyvision.org/
• OverFeat (NYU)
http://cilvr.nyu.edu/doku.php?id=code:start
Wanna Play ? Computer Vision
75