You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Modeling language and cognition with deep unsupervised learning: a tutorial overview 
Chapter Title:  LEARNING A HIERARCHICAL GENERATIVE MODEL: DEEP BELIEF NETWORKS 
1. Abstract of Cited Paper (BIB001):  Complexity theory of circuits strongly suggests that deep architectures can be much more efficient (sometimes exponentially) than shallow architectures, in terms of computational elements required to represent some functions. Deep multi-layer neural networks have many levels of non-linearities allowing them to compactly represent highly non-linear and highly-varying functions. However, until recently it was not clear how to train such deep networks, since gradient-based optimization starting from random initialization appears to often get stuck in poor solutions. Hinton et al. recently introduced a greedy layer-wise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with many layers of hidden causal variables. In the context of the above optimization problem, we study this algorithm empirically and explore variants to better understand its success and extend it to cases where the inputs are continuous or where the structure of the input distribution is not revealing enough about the variable to be predicted in a supervised task. Our experiments also confirm the hypothesis that the greedy layer-wise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are high-level abstractions of the input, bringing better generalization. 
2. Abstract of Cited Paper (BIB002):  Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure. Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of Graph Transformer Networks. A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with global training techniques to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day. 
3. Abstract of Cited Paper (BIB003):  The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free parameters. We consider two well-known unsupervised learning models, deep belief networks (DBNs) and sparse coding, that have recently been applied to a flurry of machine learning applications (Hinton & Salakhutdinov, 2006; Raina et al., 2007). Unfortunately, current learning algorithms for both models are too slow for large-scale applications, forcing researchers to focus on smaller-scale models, or to use fewer training examples. In this paper, we suggest massively parallel methods to help resolve these problems. We argue that modern graphics processors far surpass the computational capabilities of multicore CPUs, and have the potential to revolutionize the applicability of deep unsupervised learning methods. We develop general principles for massively parallelizing unsupervised learning tasks using graphics processors. We show that these principles can be applied to successfully scaling up learning algorithms for both DBNs and sparse coding. Our implementation of DBN learning is up to 70 times faster than a dual-core CPU implementation for large models. For example, we are able to reduce the time required to learn a four-layer DBN with 100 million free parameters from several weeks to around a single day. For sparse coding, we develop a simple, inherently parallel algorithm, that leads to a 5 to 15-fold speedup over previous methods. 
4. Abstract of Cited Paper (BIB004):  Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestly- sized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm. 
5. Abstract of Cited Paper (BIB005):  It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full-blown model of multi-syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading. 
6. Abstract of Cited Paper (BIB006):  Deep belief networks hold great promise for the simulation of human cognition because they show how structured and abstract representations may emerge from probabilistic unsupervised learning. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. However, learning in deep networks typically requires big datasets and it can involve millions of connection weights, which implies that simulations on standard computers are unfeasible. Developing realistic, medium-to-large-scale learning models of cognition would therefore seem to require expertise in programing parallel-computing hardware, and this might explain why the use of this promising approach is still largely confined to the machine learning community. Here we show how simulations of deep unsupervised learning can be easily performed on a desktop PC by exploiting the processors of low cost graphic cards (graphic processor units) without any specific programing effort, thanks to the use of high-level programming routines (available in MATLAB or Python). We also show that even an entry-level graphic card can outperform a small high-performance computing cluster in terms of learning time and with no loss of learning quality. We therefore conclude that graphic card implementations pave the way for a widespread use of deep learning among cognitive scientists for modeling cognition and behavior. 
7. Abstract of Cited Paper (BIB007):  It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights.

Literature Review:
RBMs can be used as building blocks of more complex architectures, where the hidden variables of the generative model can be organized into layers of a hierarchy ( Figure 3A) . The resulting architecture is referred to as a "deep network." In particular, the Deep Belief Network (DBN; BIB001 ) is a stack of RBMs that can be trained layer by layer in a greedy, unsupervised way. The main intuition behind deep learning is that, by training a generative model at level l using as input the hidden causes discovered at level l-1, the network will progressively build more structured and abstract representations of the input data. Importantly, architectures with multiple processing levels permit an efficient encoding of information by exploiting re-use of features among different layers: simple features extracted at lower levels can be successively combined to create more complex features, which will eventually unravel the main causal factors underlying the data distribution. Indeed, it has been shown that functions that can be compactly represented by a depth k architecture might require an exponential number of computational elements to be represented by a depth k-1 architecture  . Moreover, adding a new layer to the architecture increases a lower bound on the log-likelihood of the generative model , thus improving the overall capacity of the network. After learning of all layers, the deep architecture can be used as a generative model by reproducing the data when sampling from the model, that is by feeding the activations of the deepest layer all the way back to the input layer. Note that the hierarchical structure of the internal representations is an emergent property of the learning algorithm. In contrast, hierarchy in classic connectionist models is typically built in by stipulating the representations to be used at more than one layer (e.g.,  BIB006 ; indeed, training of deep multi-layer perceptrons using error backpropagation is very difficult because the error gradient tends to vanish when propagated backwards through more than one hidden layer (see BIB008 , for further discussion). An important advantage of deep unsupervised learning is that the internal representations discovered by the network are not tied to a particular discriminative task, because the objective of learning is only to model the hidden causes of the data. However, once the system has developed expressive abstract representations, possible supervised tasks can be carried out by introducing additional modules, which directly operate on such high-level representations of the data and can therefore yield excellent performance in classification or function learning ( Figure 3B ). For example, on a popular handwritten digit recognition problem (MNIST dataset; BIB003 , high discriminative accuracy can be obtained even by a linear classifier applied on the top-level internal representations of a DBN that was only trained to reconstruct the digit images BIB007 ; examples of digits reconstructed by the network are reported in Figure 3C ). Within this perspective, the use of an additional fine-tuning phase of the whole deep network using error backpropagation (as done in BIB001 ) might be unwarranted, not only because of the biological implausibility of the learning algorithm, but also because the network would become specifically tuned to a particular task. Indeed, the idea that high-level representations obtained from (unsupervised) model learning should be usable across several tasks ( Figure 3B ) is referred to as "transfer learning" and it is a hot topic for the machine learning community   . It is worth mentioning that machine learning researchers have recently investigated deep networks built through greedy layer-wise training of stacked autoencoders, where each autoencoder is a multilayer perceptron trained to auto-associate the input BIB002  . This approach has been successful in terms of machine learning benchmarks, but it is less appealing than DBNs for cognitive modeling purposes because learning is based on error backpropagation and it is not grounded in a sound probabilistic framework. Moreover, deep autoencoders are not used as generative models to produce predictions based on top-down signals. A final consideration concerns the computational complexity of deep learning: thanks to its efficiency, the algorithm proposed by solves the problem of learning in densely connected networks that have many hidden layers. If implemented on multicore hardware, deep learning is practical even with billions of connections, thereby allowing the development of very-large-scale simulations BIB004 BIB005  . Medium-to-large-scale simulations can even be performed on a desktop PC equipped with a low-cost graphic card BIB007 see below) .