Wednesday, 5 February 2014

Deep Learning

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A NOTE. The following has some relevance in connection with the emergence of the New Paradigm of Transfinancial Economics. Hence, its inclusion. Artificial Intelligence (AI) per se will also probably have some relevance to the economics of the future
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Various deep learning architectures such as deep neural networks, convolutional deep neural networks, and deep belief networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, and music/audio signal recognition where they have shown to produce state-of-the-art results on various tasks.


Deep learning architectures, specifically those built from artificial neural networks (ANN), date back at least to the Neocognitron introduced by Kunihiko Fukushima in 1980.[2] The ANNs themselves date back even further. In 1989, Yann LeCun et. al were able to apply the standard backpropagation algorithm, which had been around since 1974,[3] to a deep neural network with the purpose of recognizing handwritten zip codes on mail. Despite the success of applying the algorithm, the time to train the network on this dataset was approximately 3 days, making it impractical for general use.[4] Many factors contribute to the slow speed, one being due to the so-called vanishing gradient problem analyzed in 1991 by Jürgen Schmidhuber's student Sepp Hochreiter.[5][6] In combination with speed issues, ANNs fell out of favor in practical machine learning and simpler models such as support vector machines (SVMs) became the popular choice of the field in the 1990s and 2000s.
The term "deep learning" gained traction in the mid-2000s after a publication by Geoffrey Hinton and Ruslan Salakhutdinov showed how a many-layered feedforward neural network could be effectively pre-trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning.[7] In 1992, Schmidhuber had already implemented a very similar idea for the more general case of unsupervised deep hierarchies of recurrent neural networks, and also experimentally shown its benefits for speeding up supervised learning [8][9]
Since the resurgence of deep learning, it has shown to be part of many state-of-the-art systems in different disciplines, particularly that of computer vision and automatic speech recognition (ASR). Results on commonly used evaluation sets such as TIMIT (ASR) and MNIST (image classification) are constantly being improved with new applications of deep learning. Currently, it has been shown that deep learning architectures in the form of convolution neural networks have been best performing, however, these are more widely used in computer vision than in ASR.
Advances in hardware have also been an important enabling factor for the renewed interest of deep learning. In particular, powerful graphics processing units (GPUs) are highly suited for the kind of number crunching, matrix/vector math involved in machine learning. GPUs have been shown to speed up training algorithms by orders of magnitude, bringing running times of weeks back to days.[10][11]

Fundamental concepts[edit]

Deep learning algorithms are based on distributed representations, a concept used in machine learning. The underlying assumption behind distributed representations is that observed data is generated by the interactions of many different factors on different levels. Deep learning adds the assumption that these factors are organized into multiple levels, corresponding to different levels of abstraction or composition. Varying numbers of layers and layer sizes can be used to provide different amounts of abstraction.[1]
Deep learning algorithms in particular exploit this idea of hierarchical explantory factors. Different concepts are learned from other concepts, with the more abstract, higher level concepts being learned from the lower level ones. These architectures are often constructed with a greedy layer-by-layer method that models this idea. Deep learning helps to disentangle these abstractions and pick out which features are useful for learning.[1]
Many deep learning algorithms are actually framed as unsupervised learning problems. Because of this, these algorithms can make use of the unlabeled data that other algorithms cannot. Unlabeled data is usually more abundant than labeled data, making this a very important benefit of these algorithms. The deep belief network introduced in the next section is an example of a deep structure that can be trained in an unsupervised manner.[1]

Deep learning in artificial neural networks[edit]

Some of the most successful deep learning methods involve artificial neural networks. Artificial neural networks are inspired by the 1959 biological model proposed by Nobel laureates David H. Hubel & Torsten Wiesel, who found two types of cells in the visual primary cortex: simple cells and complex cells. Many artificial neural networks can be viewed as cascading models[12] of cell types inspired by these biological observations.
Fukushima's Neocognitron introduced convolutional neural networks partially trained by unsupervised learning. Yann LeCun et al (1989) applied supervised backpropagation to such architectures.[13]
With the advent of the back-propagation algorithm in the 1970s, many researchers tried to train supervised deep artificial neural networks from scratch, initially with little success. Sepp Hochreiter's diploma thesis of 1991[14][15] formally identified the reason for this failure in the "vanishing gradient problem," which not only affects many-layered feedforward networks, but also recurrent neural networks. The latter are trained by unfolding them into very deep feedforward networks, where a new layer is created for each time step of an input sequence processed by the network. As errors propagate from layer to layer, they shrink exponentially with the number of layers.
To overcome this problem, several methods were proposed. One is Jürgen Schmidhuber's multi-level hierarchy of networks (1992) pre-trained one level at a time through unsupervised learning, fine-tuned through backpropagation.[8] Here each level learns a compressed representation of the observations that is fed to the next level.
Another method is the long short term memory (LSTM) network of 1997 by Hochreiter & Schmidhuber.[16] In 2009, deep multidimensional LSTM networks demonstrated the power of deep learning with many nonlinear layers, by winning three ICDAR 2009 competitions in connected handwriting recognition, without any prior knowledge about the three different languages to be learned.[17][18]
Sven Behnke relied only on the sign of the gradient (Rprop) when training his Neural Abstraction Pyramid[19] to solve problems like image reconstruction and face localization.
Other methods also use unsupervised pre-training to structure a neural network, making it first learn generally useful feature detectors. Then the network is trained further by supervised back-propagation to classify labeled data. The deep model of Hinton et al. (2006) involves learning the distribution of a high level representation using successive layers of binary or real-valued latent variables. It uses a restricted Boltzmann machine (Smolensky, 1986[20]) to model each new layer of higher level features. Each new layer guarantees an increase on the lower-bound of the log likelihood of the data, thus improving the model, if trained properly. Once sufficiently many layers have been learned the deep architecture may be used as a generative model by reproducing the data when sampling down the model (an "ancestral pass") from the top level feature activations.[21] Hinton reports that his models are effective feature extractors over high-dimensional, structured data.[22]
The Google Brain team led by Andrew Ng and Jeff Dean created a neural network that learned to recognize higher-level concepts, such as cats, only from watching unlabeled images taken from YouTube videos.[23] [24]
Other methods rely on the sheer processing power of modern computers, in particular, GPUs. In 2010 it was shown by Dan Ciresan and colleagues[10] in Jürgen Schmidhuber's group at the Swiss AI Lab IDSIA that despite the above-mentioned "vanishing gradient problem," the superior processing power of GPUs makes plain back-propagation feasible for deep feedforward neural networks with many layers. The method outperformed all other machine learning techniques on the old, famous MNIST handwritten digits problem of Yann LeCun and colleagues at NYU.
As of 2011, the state of the art in deep learning feedforward networks alternates convolutional layers and max-pooling layers,[25][26] topped by several pure classification layers. Training is usually done without any unsupervised pre-training. Since 2011, GPU-based implementations[25] of this approach won many pattern recognition contests, including the IJCNN 2011 Traffic Sign Recognition Competition,[27] the ISBI 2012 Segmentation of neuronal structures in EM stacks challenge,[28] and others.
Such supervised deep learning methods also were the first artificial pattern recognizers to achieve human-competitive performance on certain tasks.[29]

Deep learning architectures[edit]

It is worth explaining some common architectures used for deep learning. Basic models of deep neural networks and deep belief networks are given below.

Deep neural networks[edit]

A deep neural network (DNN) is defined to be an artificial neural network with more than one hidden layer of units between the input and output layers. Similar to shallow ANNs, it can model complex non-linear relationships. The extra layers give it added levels of abstraction, thus increasing its modeling capability. DNNs are typically designed as feedforward networks, but recent research has successfully applied the deep learning architecture to recurrent neural networks for applications such as language modeling.[30] Convolutional deep neural networks (CNNs) are used in computer vision where their success is well-documented.[31] More recently, CNNs have been applied to acoustic modeling for automatic speech recognition (ASR), where they have shown success over previous models.[32] For simplicity, a look at training DNNs is given here.
A DNN can be discriminatively trained with the standard backpropagation algorithm. The weight updates can be done via stochastic gradient descent using the following equation:
 \Delta w_{ij}(t + 1) = \Delta w_{ij}(t) + \eta\frac{\partial C}{\partial w_{ij}}
Here,  \eta is the learning rate, and  C is the cost function. The choice of the cost function depends on factors such as the learning type (supervised, unsupervised, reinforcement, etc) and the activation function. For example, when performing supervised learning on a multiclass classification problem, common choices for the activation function and cost function are the softmax function and cross entropy function, respectively. The softmax function is defined as  p_j = \frac{\exp(x_j)}{\sum_k \exp(x_k)} where  p_j represents the class probability and  x_j and  x_k represent the total input to units  j and  k respectively. Cross-entropy is defined as  C = -\sum_j d_j \log(p_j) where  d_j represents the target probability for output unit  j and  p_j is the probability output for  j after applying the activation function.[33]

Issues with deep neural networks[edit]

Like with ANNs, many issues can arise with DNNs if they are naively trained. Two common issues are overfitting and computation time.
Overfitting occurs when the DNN is allowed to model rare dependencies between units that can occur in training data. DNNs are very prone to overfitting because of their added layers of abstraction, which can allow them to model rare dependencies in the training data. Regularization methods such as weight decay ( \ell_2 -regularization) or sparsity ( \ell_1 -regularization) can be applied during training to help combat overfitting.[34] Another regularization method that has been more recently applied to DNNs is "dropout" regularization. In dropout, some number of units are randomly omitted from the hidden layers during training. This helps to break the rare dependencies that can occur in the training data [35]
Backpropagation and gradient descent have been the preferred method for training these structures due to the ease of implementation and their tendency to converge to better local optima in comparison with other training methods. However, these methods can be very computationally expensive, especially when being used to train DNNs. There are many parameters to be considered with a DNN, such as the size (number of layers and number of units per layer), the learning rate, initial weights, etc. Sweeping through the parameters may not be feasible in many tasks due to the time cost. Various 'tricks' such as using mini-batching (computing the gradient on several training examples at once rather than individual examples) [36] have been shown to speed up computation. The sheer processing power of GPUs arguably has shown the most significant speed up, mainly due to the matrix and vector computations required being well suited for GPUs. However, it is hard to make use of large cluster machines for training DNNs, so better methods of parallelizing training will undoubtedly give a significant speed up as well.

Deep belief networks[edit]

An restricted Boltzmann machine (RBM _with fully connected visible and hidden units. Note there are no hidden-hidden or visible-visible connections.
A deep belief network (DBN) is a probabilistic, generative model made up of multiple layers of hidden units. It can be looked at as a composition of simple learning modules that make up each layer.[37]
A DBN can be used for generatively pre-training a DNN by using the learned weights as the initial weights. Backpropagation or other discriminative algorithms can then be applied for fine-tuning of these weights. This is particularly helpful in siutations where limited training data is available, as poorly initialized weights can have significant impact on the performance of the final model. These pre-trained weights are in a region of the weight space that is closer to the optimal weights (as compared to just random initialization). This allows for both improved modeling capability and faster convergence of the fine-tuning phase.[38]
A DBN can be efficiently trained in an unsupervised, layer-by-layer manner where the layers are typically made of restricted Boltzmann machines (RBM). A description of training a DBN via RBMs is provided below. An RBM is an undirected, generative energy-based model with an input layer and single hidden layer. Connections only exist between the visible units of the input layer and the hidden units of the hidden layer; there are no visible-visible or hidden-hidden connections.
The training method for RBMs was initially proposed by Geoffrey Hinton for use with training "Product of Expert" models and is known as constrastive divergence (CD).[39] CD provides an approximation to the maximum likelihood method that would ideally be applied for learning the weights of the RBM.[36]
In training a single RBM, weight updates are performed with gradient ascent via the following equation:  \Delta w_{ij}(t+1) = w_{ij}(t) + \eta\frac{\partial \log(p(v))}{\partial w_{ij}} . Here, p(v) is the probability of a visible vector, which is given by p(v) = \frac{1}{Z}\sum_he^{-E(v,h)}.  Z is the partition function (used for normalizing) and E(v,h) is the energy function assigned to the state of the network. A lower energy indicates the network is in a more "desirable" configuration. The gradient \frac{\partial \log(p(v))}{\partial w_{ij}} has the simple form \langle v_ih_j\rangle_\text{data} - \langle v_ih_j\rangle_\text{model} where \langle\cdots\rangle_p represent averages with respect to distribution p. The issue arises in sampling \langle v_ih_j\rangle_\text{model} as this requires running alternating Gibbs sampling for a long time. CD replaces this step by running alternating Gibbs sampling for n steps (values of n = 1 have empirically been shown to perform well). After n steps, the data is sampled and that sample is used in place of \langle v_ih_j\rangle_\text{model}. The CD procedure works as follows:[36]
  1. Initialize the visible units to a training vector.
  2. Update the hidden units in parallel given the visible units: p(h_j = 1 \mid \textbf{V}) = \sigma(b_j + \sum_i v_iw_{ij}). \sigma represents the sigmoid function and b_j is the bias of h_j.
  3. Update the visible units in parallel given the hidden units: p(v_i = 1 \mid \textbf{H}) = \sigma(a_i + \sum_j h_jw_{ij}). a_i is the bias of v_i. This is called the "reconstruction" step.
  4. Reupdate the hidden units in parallel given the reconstructed visible units using the same equation as in step 2.
  5. Perform the weight update: \Delta w_{ij} \propto \langle v_ih_j\rangle_\text{data} - \langle v_ih_j\rangle_\text{reconstruction}.
Once an RBM is trained, another RBM can be "stacked" atop of it to create a multilayer model. Each time another RBM is stacked, the input visible layer is initialized to a training vector and values for the units in the already-trained RBM layers are assigned using the current weights and biases. The final layer of the already-trained layers is used as input to the new RBM. The new RBM is then trained with the procedure above, and then this whole process can be repeated until some desired stopping criterion is met.[40]
Despite the approximation of CD to maximum likelihood being very crude (CD has been shown to not follow the gradient of any function), empirical results have shown it to be an effective method for use with training deep architectures.[36]

Convolutional neural networks[edit]

Arguably the most recent achievement in deep learning is from the use of convolutional neural networks (CNN). A CNN is composed of one or more convolutional layers with fully connected layers (matching those in typical artificial neural networks) on top. It also uses tied weights and pooling layers. This architecture allows CNNs to take advantage of the 2D structure of input data. In comparison with other deep architectures, convolutional neural networks are starting to show superior results in both image and speech applications. They can also be trained with standard backpropagation. CNNs are are easier to train than other regular, deep, feed-forward neural networks and have many fewer parameters to estimate, making them a highly attractive architecture to use.[41]


Automatic speech recognition[edit]

The results shown in the table below are for automatic speech recognition on the popular TIMIT data set. This is a common data set used for initial evaluations of deep learning architectures. The entire set contains 630 speakers from eight major dialects of American English, with each speaker reading 10 different sentences.[42] Its small size allows many different configurations to be tried effectively with it. The error rates presented are phone error rates (PER).
MethodPER (%)
Randomly Initialized RNN26.1
Bayesian Triphone GMM-HMM25.6
Monophone Randomly Initialized DNN23.4
Monophone DBN-DNN22.4
Triphone GMM-HMM with BMMI Training21.7
Monophone DBN-DNN on fbank20.7
Convolutional DNN20.0

Image classification[edit]

A common evaluation set for image classification is the MNIST database data set. MNIST is composed of handwritten digits and includes of 60000 training examples and 10000 test examples. Similar to TIMIT, its small size allows multiple configurations to be tested. A comprehensive list of results on this set can be found in.[43] The current best result on MNIST is an error rate of 0.23%, achieved by Ciresan et. al in 2012.[44]

Image retrieval[edit]

Deep learning in the human brain[edit]

Computational deep learning is closely related to a class of theories of brain development (specifically, neocortical development) proposed by cognitive neuroscientists in the early 1990s.[45] The most approachable summary of this work is Elman, et al.'s 1996 book "Rethinking Innateness" [46] (see also: Shrager and Johnson;[47] Quartz and Sejnowski [48]). As these theories were also instantiated in computational models, they are technical predecessors of purely computationally-motivated deep learning models. These models share the interesting property that various proposed learning dynamics in the brain (e.g., a wave of neurotrophic growth factor) conspire to support the self-organization of just the sort of inter-related neural networks utilized in the later, purely computational deep learning models, and which appear to be analogous to one way of understanding the neocortex of the brain as a hierarchy of filters where each layer captures some of the information in the operating environment, and then passes the remainder, as well as modified base signal, to other layers further up the hierarchy. The result of this process is a self-organizing stack of transducers, well-tuned to their operating environment. As described in The New York Times in 1995: "...the infant's brain seems to organize itself under the influence of waves of so-called trophic-factors ... different regions of the brain become connected sequentially, with one layer of tissue maturing before another and so on until the whole brain is mature." [49]
The importance of deep learning with respect to the evolution and development of human cognition did not escape the attention of these researchers. One aspect of human development that distinguishes us from our nearest primate neighbors may be changes in the timing of development.[50] Among primates, the human brain remains relatively plastic until late in the post-natal period, whereas the brains of our closest relatives are more completely formed by birth. Thus, humans have greater access to the complex experiences afforded by being out in the world during the most formative period of brain development. This may enable us to "tune in" to rapidly changing features of the environment that other animals, more constrained by evolutionary structuring of their brains, are unable to take account of. To the extent that these changes are reflected in similar timing changes in hypothesized wave of cortical development, they may also lead to changes in the extraction of information from the stimulus environment during the early self-organization of the brain. Of course, along with this flexibility comes an extended period of immaturity, during which we are dependent upon our caretakers and our community for both support and training. The theory of deep learning therefore sees the coevolution of culture and cognition as a fundamental condition of human evolution.[51]

Publicity around deep learning[edit]

Deep learning is often presented as a step towards realising strong AI [52] and thus many organizations have become interested in its use for particular applications. Most recently, in December 2013, Facebook announced that it hired Yann LeCun to head its new artificial intelligence (AI) lab that will have operations in California, London, and New York. The AI lab will be used for developing deep learning techniques that will help Facebook do tasks such as automatically tagging uploaded pictures with the names of the people in them.[53]
In March 2013, Geoffrey Hinton and two of his graduate students, Alex Krizhevsky and Ilya Sutskever, were hired by Google. Their work will be focused on both improving existing machine learning products at Google (such as the Android) and also help deal with the growing amount of data Google has. Google also purchased Hinton's company, DNNresearch, for an undisclosed sum of money.


A main criticism of deep learning concerns the lack of theory surrounding many of the methods. Most of the learning in deep architectures is just some form of gradient descent. While gradient descent has been understood for a while now, the theory surrounding other algorithms, such as contrastive divergence is less clear (i.e., Does it converge? If so, how fast? What is it approximating?). Deep learning methods are often looked at as a black box, with most confirmations done empirically, rather than theoretically.
Deep learning is a step in the right direction for machine learning and AI, however, much remains to be done to understand the inner workings of the architectures.

See also[edit]


  1. ^ Jump up to: a b c d Y. Bengio, A. Courville, and P. Vincent., "Representation Learning: A Review and New Perspectives," IEEE Trans. PAMI, special issue Learning Deep Architectures, 2013
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  9. Jump up ^ J. Schmidhuber., "My First Deep Learning System of 1991 + Deep Learning Timeline 1962–2013."
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  12. Jump up ^ M Riesenhuber, T Poggio. Hierarchical models of object recognition in cortex. Nature neuroscience, 1999(11) 1019–1025.
  13. Jump up ^ Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1(4):541–551, 1989.
  14. Jump up ^ S. Hochreiter. Untersuchungen zu dynamischen neuronalen Netzen. Diploma thesis, Institut f. Informatik, Technische Univ. Munich, 1991. Advisor: J. Schmidhuber
  19. Jump up ^ Sven Behnke (2003). Hierarchical Neural Networks for Image Interpretation.. Lecture Notes in Computer Science 2766. Springer. 
  20. Jump up ^ Smolensky, P. (1986). "Information processing in dynamical systems: Foundations of harmony theory.". In D. E. Rumelhart, J. L. McClelland, & the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. 1. pp. 194–281. 
  22. Jump up ^ Hinton, G. (2009). "Deep belief networks". Scholarpedia 4 (5): 5947. doi:10.4249/scholarpedia.5947.  edit
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  36. ^ Jump up to: a b c d G. E. Hinton., "A Practical Guide to Training Restricted Boltzmann Machines," Tech. Rep. UTML TR 2010-003, Dept. CS., Univ. of Toronto, 2010.
  37. Jump up ^ G.E. Hinton., "Deep belief networks," Scholarpedia, 4(5):5947.
  38. Jump up ^ H. Larochelle et al., "An empirical evaluation of deep architectures on problems with many factors of variation," in Proc. 24th Int. Conf. Machine Learning, pp. 473–480, 2007.
  39. Jump up ^ G. E. Hinton., "Training Product of Experts by Minimizing Contrastive Divergence," Neural Computation, 14, pp. 1771–1800, 2002.
  40. Jump up ^ Y. Bengio., "Learning Deep Architectures for AI," Foundations and Trends in Machine Learning, 2 (1), pp. 1–127, 2009.
  41. Jump up ^
  42. Jump up ^ TIMIT Acoustic-Phonetic Continuous Speech Corpus Linguistic Data Consortium, Philadelphia.
  43. Jump up ^
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  45. Jump up ^ P. E. Utgoff and D. J. Stracuzzi., "Many-layered learning," Neural Computation, 14, pp. 2497–2529, 2002.
  46. Jump up ^ J. Elman, et al., "Rethinking Innateness," 1996.
  47. Jump up ^ J. Shrager, MH Johnson., "Dynamic plasticity influences the emergence of function in a simple cortical array," Neural Networks, 9 (7), pp. 1119–1129, 1996
  48. Jump up ^ SR Quartz and TJ Sejnowski., "The neural basis of cognitive development: A constructivist manifesto," Behavioral and Brain Sciences, 20 (4), pp. 537–556, 1997.
  50. Jump up ^ {BUFILL} E. Bufill, J. Agusti, R. Blesa., "Human neoteny revisited: The case of synaptic plasticity," American Journal of Human Biology, 23 (6), pp. 729–739, 2011.
  51. Jump up ^ J. Shrager and M. H. Johnson., "Timing in the development of cortical function: A computational approach," In B. Julesz and I. Kovacs (Eds.), Maturational windows and adult cortical plasticity, 1995.
  52. Jump up ^ D. Hernandez., "The Man Behind the Google Brain: Andrew Ng and the Quest for the New AI," Wired, 10 May 2013.
  53. Jump up ^ C. Metz., "Facebook's 'Deep Learning' Guru Reveals the Future of AI," Wired, 12 December 2013.
  54. Jump up ^ G. Marcus., "Is "Deep Learning" a Revolution in Artificial Intelligence?" The New Yorker, 25 November 2012.

External links[edit]

  • Wealth of deep learning information from the University of Montreal [1]
  • Geoffrey Hinton's webpage [2]
  • Deep learning tutorial [3]
  • Yann LeCun's webpage [4]
  • The Center for Biological and Computational Learning (CBCL) [5]
  • Stanford tutorial on unsupervised feature learning and deep learning [6]
  • Google's DistBelief Framework [7]
  • Theano Python Library (useful for creating deep learning models) [8]
  • NIPS 2013 Conference (Many talks on deep learning related material) [9]

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