deep learning & nlp: graphs to the rescue!
DESCRIPTION
Lecture 21 October 2014TRANSCRIPT
Deep Learning & NLPGraphs to the Rescue! (or not yet…)
Roelof Pieters, KTH/CSC, Graph Technologies R&D
Stockholm, Sics, October 21 2014
Twitter: @graphificwww.csc.kth.se/~roelof/
DefinitionsMachine Learning
Improving some task T based on experience E with respect to performance measure P. - T. Mitchell (1997)
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)
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DefinitionsRepresentation learning
Attempts to automatically learn good features or representations
Deep learning
Attempt to learn multiple levels of representation of increasing complexity/abstraction
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Overview
1. From Machine Learning to Deep Learning
2. Natural Language Processing
3. Graph-Based Approaches to DL+NLP
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1. from Machine Learning
to Deep Learning
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Perceptron
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Perceptron
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• Rosenblatt 1957
Perceptron
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• Rosenblatt 1957 • Minsky & Papert 1969
Perceptron
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• Rosenblatt 1957 • Minsky & Papert 1969
The world believed Minsky & Papert…
2th gen Perceptron• Quest to make it non-linear
• no result…
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Until finally…
• Rumelhart, Hinton & Williams, 1986
• Multi-Layered Perceptrons (MLP) !!!
• Backpropagation (Bryson & Ho 1969)(Rumelhart, Hinton & Williams, 1986)
• Forward Propagation :
• Sum inputs, produce activation, feed-forward
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• Back Propagation of Error
• Calculate total error at the top
• Calculate contributions to error at each step going backwards
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Phase 1: PropagationEach propagation involves the following steps:
1. Forward propagation of a training pattern's input through the neural network in order to generate the propagation's output activations.
2. Backward propagation of the propagation's output activations through the neural network using the training pattern target in order to generate the deltas of all output and hidden neurons.
Phase 2: Weight update For each weight-synapse follow the following steps:
1. Multiply its output delta and input activation to get the gradient of the weight.
2. Subtract a ratio (percentage) of the gradient from the weight.
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Perceptron Network: SVM
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• Vapnik et al. 1992; 1995.
• Cortes & Vapnik 1995
Source: Cortes & Vapnik 1995
Perceptron Network: SVM
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• Vapnik et al. 1992; 1995.
• Cortes & Vapnik 1995
Source: Cortes & Vapnik 1995
Kernel SVM
“2006”
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“2006”• Faster machines (GPU’s!)
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“2006”• Faster machines (GPU’s!)
• More data
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“2006”• Faster machines (GPU’s!)
• More data
• New methods for unsupervised pre-training
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“2006”• New methods for unsupervised pre-training
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• Stacked RBM’s (Deep Belief Networks [DBN’s] )
• Hinton, G. E, Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554.
• Hinton, G. E. and Salakhutdinov, R. R, Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
• (Stacked) Autoencoders
• Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. (2007). Greedy Layer-Wise Training of Deep Networks, Advances in Neural Information Processing Systems 19
Pretraining: Stacked RBM’s
• Iterative pre-training construction of Deep Belief Network (DBN) (Hinton et al., 2006)
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from: Larochelle et al. (2007). An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation.
Pretraining: Stacked Denoising Auto-encoder
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• Stacking Auto-Encoders
from: Bengio ICML 2009
Pretraining: Stacked Denoising Auto-encoder
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• (Vincent et al, 2008)
• Good vs Corrupted context
from: Vincent et al 2010
Pretraining: Stacked Denoising Auto-encoder
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• (Vincent et al, 2008)
• Good vs Corrupted context
from: Vincent et al 2010Raw input
Pretraining: Stacked Denoising Auto-encoder
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• (Vincent et al, 2008)
• Good vs Corrupted context
from: Vincent et al 2010Corrupted input Raw input
Pretraining: Stacked Denoising Auto-encoder
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• (Vincent et al, 2008)
• Good vs Corrupted context
from: Vincent et al 2010
Hidden code (representation)
Corrupted input Raw input
Pretraining: Stacked Denoising Auto-encoder
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• (Vincent et al, 2008)
• Good vs Corrupted context
from: Vincent et al 2010
Hidden code (representation)
Corrupted input Raw input reconstruction
Pretraining: Stacked Denoising Auto-encoder
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• (Vincent et al, 2008)
• Good vs Corrupted context
from: Vincent et al 2010
Hidden code (representation)
Corrupted input Raw input reconstruction
KL(reconstruction | raw input)
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Convolutional Neural Networks (CNNs) • Fukushima 1980; LeCun et al. 1998; Behnke 2003; Simard et al. 2003…
• Hinton et al. 2006; Bengio et al. 2007; Ranzato et al. 2007
• Sparse connectivity:
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• MaxPooling
• Shared weights:
(Figures from http://deeplearning.net/tutorial/lenet.html)
Pretraining• Why does Pretraining work so well? (Erhan et al. 2010)
• Better Generalisation
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without unsupervised pretraining with unsupervised pretraining)
Figures from Erhan et al. 2010
Pretraining
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Figures from Erhan et al. 2010
–Andrew Ng
“I’ve worked all my life in Machine Learning, and I’ve never seen one algorithm knock over
benchmarks like Deep Learning”
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The (god)fathers of DL
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The (god)fathers of DL
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The (god)fathers of DL
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DL: (Every)where ?
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DL: (Every)where ?• Language Modeling (2012, Mikolov et al)
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DL: (Every)where ?• Language Modeling (2012, Mikolov et al)
• Image Recognition (Krizhevsky won 2012 ImageNet competition)
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DL: (Every)where ?• Language Modeling (2012, Mikolov et al)
• Image Recognition (Krizhevsky won 2012 ImageNet competition)
• Sentiment Classification (2011, Socher et al)
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DL: (Every)where ?• Language Modeling (2012, Mikolov et al)
• Image Recognition (Krizhevsky won 2012 ImageNet competition)
• Sentiment Classification (2011, Socher et al)
• Speech Recognition (2010, Dahl et al)
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DL: (Every)where ?• Language Modeling (2012, Mikolov et al)
• Image Recognition (Krizhevsky won 2012 ImageNet competition)
• Sentiment Classification (2011, Socher et al)
• Speech Recognition (2010, Dahl et al)
• MNIST hand-written digit recognition (Ciresan et al, 2010)
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So: Why Deep?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
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So: Why Deep?Generalizing better to new tasks & domains
Can learn good intermediate representations shared across tasks
Distributed representations
Unsupervised Learning
Multiple levels of representation
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Diff Levels of Abstraction• 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
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Generalizable Learning• Shared Low Level Representations
• Multi-Task Learning
• Unsupervised Training
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Generalizable Learning• Shared Low Level Representations
• Multi-Task Learning
• Unsupervised Training
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• Partial Feature Sharing
• Mixed Mode Learning
• Composition of Functions
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No More Handcrafted Features !
2. Natural Language Processing
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DL + NLP• Language Modeling
• Bengio et al. (2000, 2003): via Neural network
• Mnih and Hinton (2007): via RBMs
• Pos, Chunking, NER, SRL
• Collobert and Weston 2008
• Socher et al 2011; Socher 2014
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Language Modeling• Word Embeddings (Bengio et al, 2001; Bengio et
al, 2003) based on idea of distributed representations for symbols (Hinton 1986)
• Neural Word embeddings (Turian et al 2010; Collobert et al. 2011)
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Word Embeddings• Collobert & Weston 2008; Collobert et al. 2011
• similar to word vector learning, but uses instead of single scalar score, a Softmax/Maxent classifier
33word embeddings in from lookup table. From Collobert et al. 2011
Word Embeddings• Collobert & Weston 2008; Collobert et al. 2011
• similar to word vector learning, but uses instead of single scalar score, a Softmax/Maxent classifier
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Figure from Socher et al. Tutorial ACL 2012.
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Figure from Socher et al. Tutorial ACL 2012.
• window approach
36source: Collobert & Weston, Deep Learning for Natural Language Processing. 2009 Nips
• sentence approach
• Multi-task learning
37source: Collobert & Weston, Deep Learning for Natural Language Processing. 2009 Nips
38source: Collobert & Weston, Deep Learning for Natural Language Processing. 2009 Nips
General Deep Architecture for NLP
38source: Collobert & Weston, Deep Learning for Natural Language Processing. 2009 Nips
Basic features
General Deep Architecture for NLP
38source: Collobert & Weston, Deep Learning for Natural Language Processing. 2009 Nips
Basic features
Embeddings
General Deep Architecture for NLP
38source: Collobert & Weston, Deep Learning for Natural Language Processing. 2009 Nips
Basic features
Embeddings
Convolution
General Deep Architecture for NLP
38source: Collobert & Weston, Deep Learning for Natural Language Processing. 2009 Nips
Basic features
Embeddings
Convolution
Max pooling
General Deep Architecture for NLP
38source: Collobert & Weston, Deep Learning for Natural Language Processing. 2009 Nips
Basic features
Embeddings
Convolution
Max pooling
“Supervised” learning
General Deep Architecture for NLP
Word Embeddings• Unsupervised Word Representations (Turian et al
2010)
• evaluates Brown clusters, C&W (Collobert and Weston 2008) embeddings, and HLBL (Mnih & Hinton, 2009) embeddings of words -> Brown clusters win out with a small margin on both NER and chunking.
• more info: http://metaoptimize.com/projects/wordreprs/
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t-SNE visualizations of word embeddings. Left: Number Region; Right: Jobs Region. From Turian et al. 2011
41http://metaoptimize.com/projects/wordreprs/
Word Embeddings• Collobert & Weston 2008; Collobert et al. 2011
• Propose a unified neural network architecture, for many NLP tasks:
• part-of-speech tagging, chunking, named entity recognition, and semantic role labeling
• no hand-made input features
• learns internal representations on the basis of vast amounts of mostly unlabeled training data.
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Word Embeddings• Recurrent Neural Network (Mikolov et al. 2010;
Mikolov et al. 2013a)
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W(‘‘woman")−W(‘‘man") ≃ W(‘‘aunt")−W(‘‘uncle") W(‘‘woman")−W(‘‘man") ≃ W(‘‘queen")−W(‘‘king")
Figures from Mikolov, T., Yih, W., & Zweig, G. (2013). Linguistic Regularities in Continuous Space Word Representations
• Mikolov et al. 2013b
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Figures from Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013b). Efficient Estimation of Word Representations in Vector Space
Word Embeddings• Recursive (Tensor) Network (Socher et al. 2011;
Socher 2014)
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Vector Space Model
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3. Graph-Based Approaches to DL+NLP
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• A) NLP “naturally encoded”
• B) Genetic Finite State Machine
• C) Neural net within Graph
Graph-Based NLP
• Graphs have a “natural affinity” with NLP [ feel free to quote me on that ;) ]
• relation-oriented
• index-free adjacency
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Whats in a Graph ?
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Figure from Buerli & Obispo (2012).
Whats in a Graph ?• Graph Databases: Neo4j, OrientDB, InfoGrid, Titan,
FlockDB, ArangoDB, InfiniteGraph, AllegroGraph, DEX, GraphBase, and HyperGraphDB
• Distributed graph processing toolkits (based on MapReduce, HDFS, and custom BSP engines): Bagel, Hama, Giraph, PEGASUS, Faunus, Flink
• in-memory graph packages designed for massive shared-memory (NetworkX, Gephi, MTGL, Boost, uRika, and STINGER)
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A. NLP “naturally encoded”
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• Captures:
• Redundancies
• Gapped Subsequences
• Collapsible Structures From Ganesan 2013
• ie: graph-based opinion summarization (Ganesan et al. 2010; Genevan 2013)
Natural Affinity, Say what?
Summarization Graph
59From Ganesan 2013
Natural Affinity?
• Demo time!
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B. Finite State Graph• Bastani 2014a; 2014b; 2014c
• Probabilistic feature hierarchy
• Grammatical inference by genetic algorithms
61more info: https://github.com/kbastani/graphify
Figure from Bastani 2014a
Finite State Graph
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• Bastani 2014
• training phase:
all figures from Bastani 2014b
Finite State Graph
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• Bastani 2014
• training phase:
all figures from Bastani 2014b
Finite State Graph
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• Bastani 2014
• training phase:
all figures from Bastani 2014b
Finite State Graph
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• Bastani 2014
• training phase:
all figures from Bastani 2014b
• sentimentanalysis
• error: 0.3
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Figure from Bastani 2014c
Conceptual Hierarchical Graph
• Demo time!
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C. Factor Graph• Factor graph in which the factors themselves contain a deep neural net.
• Factor graph:
• bipartite graph representing the factorization of a function (Kschischang et al. 2001; Frey 2002)
• can combine Bayesian networks (BNs) and Markov random fields (MRFs).
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Figure from Frey 2002
Factor Graph• Factor graph with “deep factors” (Mirowski & LeCun 2009)
• Dynamic Time Series modeling
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Energy-Based Graph• LeCun et al. 1998, handwriting recognition
system
• “Graph Transformer Networks”
• Instead of normalised HMM, energy based factor graph (without normalization)
• LeCun et al. 2006.
• Energy-Based Learning
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and Finally…And finally…
What you’ve all been waiting for…
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and Finally…And finally…
What you’ve all been waiting for…
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Which Net is currently the Biggest ?
and Finally…And finally…
What you’ve all been waiting for…
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Which Net is currently the Biggest ?
the Deepest
and Finally…And finally…
What you’ve all been waiting for…
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Which Net is currently the Biggest ?
the Deepest
The most Bad-ass ?
69source: Szegedy et al. Going deeper with convolutions (GoogLeNet ), ILSVRC2014, 19 Sep 2014
Winners of: Large Scale Visual Recognition Challenge 2014
(ILSVRC2014) 19 September 2014
69source: Szegedy et al. Going deeper with convolutions (GoogLeNet ), ILSVRC2014, 19 Sep 2014
Winners of: Large Scale Visual Recognition Challenge 2014
(ILSVRC2014) 19 September 2014
GoogLeNet
Convolution Pooling Softmax Other
69source: 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
70source: 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)
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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
Wanna Play?• 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
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Wanna Play?• Theano - CPU/GPU symbolic expression compiler in python
(from LISA lab at University of Montreal). http://deeplearning.net/software/theano/
• Pylearn2 - Pylearn2 is a library designed to make machine learning research easy. http://deeplearning.net/software/pylearn2/
• Torch - provides a 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/
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(slide partially stolen from: J. Sullivan, Convolutional Neural Networks & Computer Vision, Machine Learning meetup at Spotify, Stockholm, June 9
2014)
Fin.
Questions / Discussion … ?
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Bibliography: Definitions• Mitchell, T. M. (1997). Machine Learning (1st ed.). New York, NY,
USA: McGraw-Hill, Inc.
• Simon, H.A. (1983). Why should machines learn? in: Machine Learning: An Artificial Intelligence Approach, (R. Michalski, J. Carbonell, T. Mitchell, eds) Tioga Press, 25-38.
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Bibliography: History• Rosenblatt, Frank (1957), The Perceptron--a perceiving and recognizing automaton. Report
85-460-1, Cornell Aeronautical Laboratory.
• Minsky & Papert (1969), Perceptrons: an introduction to computational geometry.
• Bryson, A.E.; W.F. Denham; S.E. Dreyfus (1963) Optimal programming problems with inequality constraints. I: Necessary conditions for extremal solutions. AIAA J. 1, 11 2544-2550.
• Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J. (1986). "Learning representations by back-propagating errors". Nature 323 (6088): 533–536.
• Boser, B. E., Guyon, I., and Vapnik, V. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144–152. ACM Press.
• Cortes, C. and Vapnik, V. (1995), Support-vector network. Machine Learning, 20:273–297.
• Larochelle, H., Erhan, D., Courville, A., Bergstra, J., & Bengio, Y. (2007). An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation. In Proceedings of the 24th International Conference on Machine Learning (pp. 473–480). New York, NY, USA: ACM.
• Vincent, P., Larochelle, H., & Lajoie, I. (2010), Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371–3408.
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Bibliography: History - CNN’s• Fukushima, Kunihiko (1980). "Neocognitron: A Self-organizing Neural Network Model for a
Mechanism of Pattern Recognition Unaffected by Shift in Position". Biological Cybernetics 36 (4): 193–202. doi:10.1007/BF00344251. PMID 7370364. Retrieved 16 November 2013.
• LeCun, Yann; Léon Bottou; Yoshua Bengio; Patrick Haffner (1998). "Gradient-based learning applied to document recognition". Proceedings of the IEEE 86 (11): 2278–2324.
• S. Behnke. Hierarchical Neural Networks for Image Interpretation, volume 2766 of Lecture Notes in Computer Science. Springer, 2003.
• Simard, Patrice, David Steinkraus, and John C. Platt. "Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis." In ICDAR, vol. 3, pp. 958-962. 2003.
• Hinton, GE; Osindero, S; Teh, YW (Jul 2006). "A fast learning algorithm for deep belief nets.". Neural computation 18 (7): 1527–54.
• Bengio, Yoshua; Lamblin, Pascal; Popovici, Dan; Larochelle, Hugo (2007). "Greedy Layer-Wise Training of Deep Networks". Advances in Neural Information Processing Systems: 153–160.
• Ranzato, MarcAurelio; Poultney, Christopher; Chopra, Sumit; LeCun, Yann (2007). "Efficient Learning of Sparse Representations with an Energy-Based Model". Advances in Neural Information Processing Systems.
77
Bibliography: DL• Bengio, Y., Ducharme, R., & Vincent, P. (2001). A Neural Probabilistic Language Model.
In T. K. Leen & T. G. Dietterich (Eds.), Advances in Neural Information Processing Systems 13 (NIPS’00). MIT Press.
• Bengio, Y., Ducharme, R., Vincent, P., & Janvin, C. (2003). A Neural Probabilistic Language Model. The Journal of Machine Learning Research, 3, 1137–1155.
• Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. (2007). Greedy Layer-Wise Training of Deep Networks, Advances in Neural Information Processing Systems 19
• Hinton, G. E. (1986). Learning distributed representations of concepts. In Proceedings of the eighth annual conference of the cognitive science society (Vol. 1, p. 12).
• Hinton, G. E. and Salakhutdinov, R. R, (2006) Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
• Hinton, G. E, Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554.
• Erhan, D., Bengio, Y., & Courville, A. (2010). Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 11, 625–660.
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Bibliography: DL• P. Vincent, P., Larochelle, H., Bengio, Y. and Manzagol, P. A. (2008) Extracting and
composing robust features with denoising autoencoders. In ICML.
• Vincent, P., Larochelle, H., & Lajoie, I. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371–3408. Bengui 2009
• Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012) Imagenet classification with deep convolutional neural networks. In NIPS.
• Socher, Richard, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D. Manning. (2011). Semi-supervised recursive autoencoders for predict- ing sentiment distributions. In Proceedings of the 2011 Conference on Empiri- cal Methods in Natural Language Processing (EMNLP).
• Dahl, G. E., Ranzato, M. A., Mohamed, A. and Hinton, G. E. (2010) Phone recognition with the mean-covariance restricted Boltzmann machine. In NIPS.
• Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2010). Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition. CoRR.
• Szegedy et al. (2014) Going deeper with convolutions (GoogLeNet ), ILSVRC2014, 19 Sep 2014
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Bibliography: NLP• Turian, J., Ratinov, L., & Bengio, Y. (2010). Word Representations: A Simple and
General Method for Semi-supervised Learning. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (pp. 384–394). Stroudsburg, PA, USA: Association for Computational Linguistics.
• Collobert, R., & Weston, J. (2008). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th International Conference ….
• Collobert, R., Weston, J., & Bottou, L. (2011). Natural language processing (almost) from scratch. The Journal of Machine Learning Research, 12:2493-2537.
• Collobert & Weston, Deep Learning for Natural Language Processing (2009) Nips Tutorial
• Mikolov, T., Yih, W., & Zweig, G. (2013a). Linguistic Regularities in Continuous Space Word Representations. HLT-NAACL, (June), 746–751.
• Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013b). Efficient Estimation of Word Representations in Vector Space, 1–12. Computation and Language.
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• Bengio, Y. and Bengio, S (2000) Modeling high- dimensional discrete data with multi-layer neural networks. In Proceedings of NIPS 12
• Mnih, A. and Hinton, G. E. (2007) Three New Graphical Models for Statistical Language Modelling. International Conference on Machine Learning, Corvallis, Oregon.
• Socher, R., Bengio, Y., & Manning, C. (2012). Deep Learning for NLP (without Magic). Tutorial Abstracts of ACL 2012.
• Socher, R. (2014). recursive deep learning for natural language processing and computer vision. Dissertation.
81
Bibliography: NLP
Bibliography: Graph-Based Approaches
• Frey, B. (2002). Extending factor graphs so as to unify directed and undirected graphical models. Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence 19 (UAI 03), Morgan Kaufmann, CA, Acapulco, Mexico, 257–264.
• F. R. Kschischang, B. J. Frey, H. A. L. (2001). Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47(2), 498–519.
• Mirowski, P., & LeCun, Y. (2009). Dynamic factor graphs for time series modeling. Machine Learning and Knowledge Discovery.
• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE November 1998.
• LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M. A., & Huang, F. J. (2006). A Tutorial on Energy-Based Learning 1 Introduction : Energy-Based Models, 1–59.
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Bibliography: Graph-Based Approaches• Buerli, M., & Obispo, C. (2012). The current state of graph databases.
Department of Computer Science, Cal Poly San Luis Obispo
• Ganesan, K., Zhai, C., & Han, J. (2010). Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), (August), 340–348.
• Ganesan, K. (2013). Opinion Driven Decision Support System. PhD Dissertation, University of Illinois.
• Bastani, K. 2014a, Hierarchical Pattern Recognition, Blog: Meaning Of, June 17, 2014
• Bastani, K. 2014b, Using a Graph Database for Deep Learning Text Classification, Blog: Meaning Of, August 26, 2014
• Bastani, K. 2014c, Deep Learning Sentiment Analysis for Movie Reviews using Neo4j, Blog: Meaning Of, September 15, 2014
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