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@graphificRoelof Pieters
Deep Learning:a (non-techy) birds-eye
view20 April 2015
Stockholm Deep Learning
Slides at:http://www.slideshare.net/roelofp/deep-learning-a-birdseye-view
• 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
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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
Representation learningAttempts to automatically learn
good features or representations
Deep learningAttempt to learn multiple levels of representation of increasing
complexity/abstraction
Deep Learning: What?
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Deep Learning ??
5
Machine Learning ??
Traditional Programming:
Data
ProgramOutput
6
Computer
Machine Learning ??
Traditional Programming:
Data
ProgramOutput
Data ProgramOutput
Machine Learning:
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(labels)(“weights”/model)
Computer
Computer
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
Typical ML Regression
Deep Learning: Why?
Neural NetTypical ML Regression
Deep Learning: Why?
Machine -> Deep Learning ??
Machine -> Deep Learning ??
DEEP NET (DEEP LEARNING)
Biological Inspiration
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Deep Learning: Why?
Deep Learning is everywhere…
Deep Learning in the News
(source: Google Trends)19
(source: arXiv bookworm, Culturomics)
Scientific Articles:
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!
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Renewed Interest: 1990s
• Learning multiple layers• “Back propagation”• Can “theoretically” learn any function!
But…• Very slow and inefficient• SVMs, random forests, etc. SOTA
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2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
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2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
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2006 Breakthrough
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2006 Breakthrough
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Growth of datasets
2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
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2006 Breakthrough
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vs
Rise of Raw Computation Power
2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
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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
Deep Learning for the Win!
• 1.2M images with 1000 object categories
• AlexNet of uni Toronto: 15% error rate vs 26% for 2th placed (traditional CV)
Impact on Computer VisionImageNet Challenge 2012
Impact on Computer Vision
(from Clarifai)
Impact on Computer Vision
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
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
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)
Impact on Computer Vision
Latest State of the Art:
Computer Vision: Current State of the Art
Impact on Audio Processing
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First public Breakthrough with Deep Learning in 2010
Dahl et al. (2010)
Impact on Audio Processing
46
First public Breakthrough with Deep Learning in 2010
Dahl et al. (2010)
-33%! -32%!
Impact on Audio Processing
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Speech Recognition
Impact on Audio Processing
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TIMIT Speech Recognition
(from: Clarifai)
Impact on Audio Processing
C&W 2011
Impact on Natural Language Processing
Pos: Toutanova et al. 2003)Ner: Ando & Zhang 2005
C&W 2011
Impact on Natural Language Processing
Named Entity Recognition:
Deep Learning: Who’s to blame?
53
Deep Learning: Who’s to blame?
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?
— 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?
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Biological JustificationDeep Learning = Brain “inspired”Audio/Visual Cortex has multiple stages == Hierarchical
Different Levels of Abstraction
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Hierarchical Learning• Natural progression
from low level to high level structure as seen in natural complexity
Different Levels of AbstractionFeature Representation
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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 AbstractionFeature Representation
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
• A good lower level representation can be used for many distinct tasks
Different Levels of AbstractionFeature Representation
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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 AbstractionFeature Representation
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Different Levels of Abstraction
Classic Deep Architecture
Input layer
Hidden layers
Output layer
Modern Deep Architecture
Input layer
Hidden layers
Output layer
movie time:http://www.cs.toronto.edu/~hinton/adi/index.htm
Hierarchies
Efficient
Generalization
Distributed
Sharing
Unsupervised*
Black Box
Training Time
Major PWNAGE!
Much Data
Why go Deep ?
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No More Handcrafted Features !
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[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 ?
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
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Deep Learning: Future Challenges
a
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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”
as PhD candidate KTH/CSC:“Always interested in discussing
Machine Learning, Deep Architectures, Graphs, and
Language Technology”
In touch!
[email protected]/~roelof/
Data Science ConsultancyAcademic/Research
72
Gve Systems Graph Technologies
• 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
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• 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)
• GloVehttp://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
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• 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
