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: Towards Bayesian Deep Learning: A Survey 
Chapter Title:  INTRODUCTION 
1. Abstract of Cited Paper (BIB001):  Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers. 
2. Abstract of Cited Paper (BIB002):  Variational methods have been previously explored as a tractable approximation to Bayesian inference for neural networks. However the approaches proposed so far have only been applicable to a few simple network architectures. This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length loss function) that can be applied to most neural networks. Along the way it revisits several common regularisers from a variational perspective. It also provides a simple pruning heuristic that can both drastically reduce the number of network weights and lead to improved generalisation. Experimental results are provided for a hierarchical multidimensional recurrent neural network applied to the TIMIT speech corpus. 
3. Abstract of Cited Paper (BIB003):  Kullback Leibler (KL) control problems allow for efficient computation of optimal control by solving a principal eigenvector problem. However, direct applicability of such framework to continuous state-action systems is limited. In this paper, we propose to embed a KL control problem in a probabilistic graphical model where observed variables correspond to the continuous (possibly high-dimensional) state of the system and latent variables correspond to a discrete (low-dimensional) representation of the state amenable for KL control computation. We present two examples of this approach. The first one uses standard hidden Markov models (HMMs) and computes exact optimal control, but is only applicable to low-dimensional systems. The second one uses factorial HMMs, it is scalable to higher dimensional problems, but control computation is approximate. We illustrate both examples in several robot motor control tasks. 
4. Abstract of Cited Paper (BIB004):  Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets. 
5. Abstract of Cited Paper (BIB005):  Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recently advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art. 
6. Abstract of Cited Paper (BIB006):  We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constrained to be locally linear. Our model is derived directly from an optimal control formulation in latent space, supports long-term prediction of image sequences and exhibits strong performance on a variety of complex control problems. 
7. Abstract of Cited Paper (BIB007):  We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields comparable performance to dropout on MNIST classification. We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforcement learning. 
8. Abstract of Cited Paper (BIB008):  Large multilayer neural networks trained with backpropagation have recently achieved state-of-the-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibrated probabilistic predictions, and a tendency to overfit the training data. In principle, the Bayesian approach to learning neural networks does not have these problems. However, existing Bayesian techniques lack scalability to large dataset and network sizes. In this work we present a novel scalable method for learning Bayesian neural networks, called probabilistic backpropagation (PBP). Similar to classical backpropagation, PBP works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients. A series of experiments on ten real-world datasets show that PBP is significantly faster than other techniques, while offering competitive predictive abilities. Our experiments also show that PBP provides accurate estimates of the posterior variance on the network weights. 
9. Abstract of Cited Paper (BIB009):  A new framework for topic modeling is developed, based on deep graphical models, where interactions between topics are inferred through deep latent binary hierarchies. The proposed multi-layer model employs a deep sigmoid belief network or restricted Boltzmann machine, the bottom binary layer of which selects topics for use in a Poisson factor analysis model. Under this setting, topics live on the bottom layer of the model, while the deep specification serves as a flexible prior for revealing topic structure. Scalable inference algorithms are derived by applying Bayesian conditional density filtering algorithm, in addition to extending recently proposed work on stochastic gradient thermostats. Experimental results on several corpora show that the proposed approach readily handles very large collections of text documents, infers structured topic representations, and obtains superior test perplexities when compared with related models. 
10. Abstract of Cited Paper (BIB010):  Tag recommendation has become one of the most important ways of organizing and indexing online resources like articles, movies, and music. Since tagging information is usually very sparse, effective learning of the content representation for these resources is crucial to accurate tag recommendation. Recently, models proposed for tag recommendation, such as collaborative topic regression and its variants, have demonstrated promising accuracy. However, a limitation of these models is that, by using topic models like latent Dirichlet allocation as the key component, the learned representation may not be compact and effective enough. Moreover, since relational data exist as an auxiliary data source in many applications, it is desirable to incorporate such data into tag recommendation models. In this paper, we start with a deep learning model called stacked denoising autoencoder (SDAE) in an attempt to learn more effective content representation. We propose a probabilistic formulation for SDAE and then extend it to a relational SDAE (RSDAE) model. RSDAE jointly performs deep representation learning and relational learning in a principled way under a probabilistic framework. Experiments conducted on three real datasets show that both learning more effective representation and learning from relational data are beneficial steps to take to advance the state of the art.

Literature Review:
D EEP learning has achieved significant success in many perception tasks including seeing (visual object recognition), reading (text understanding), and hearing (speech recognition). These are undoubtedly fundamental tasks for a functioning comprehensive artificial intelligence (AI) system. However, in order to build a real AI system, simply being able to see, read, and hear is far from enough. It should, above all, possess the ability of thinking. Take medical diagnosis as an example. Besides seeing visible symptoms (or medical images from CT) and hearing descriptions from patients, a doctor has to look for relations among all the symptoms and preferably infer the etiology of them. Only after that can the doctor provide medical advice for the patients. In this example, although the abilities of seeing and hearing allow the doctor to acquire information from the patients, it is the thinking part that defines a doctor. Specifically, the ability of thinking here could involve causal inference, logic deduction, and dealing with uncertainty, which is apparently beyond the capability of conventional deep learning methods. Fortunately, another type of models, probabilistic graphical models (PGM), excels at causal inference and dealing with uncertainty. The problem is that PGM is not as good as deep learning models at perception tasks. To address the problem, it is, therefore, a natural choice to tightly integrate deep learning and PGM within a principled probabilistic framework, which we call Bayesian deep learning (BDL) in this paper. With the tight and principled integration in Bayesian deep learning, the perception task and inference task are regarded as a whole and can benefit from each other. In the example above, being able to see the medical image could help with the doctor's diagnosis and inference. On the other hand, diagnosis and inference can in return help with understanding the medical image. Suppose the doctor may not be sure about what a dark spot in a medical image is, but if she is able to infer the etiology of the symptoms and disease, it can help him better decide whether the dark spot is a tumor or not. As another example, to achieve high accuracy in recommender systems BIB002 , BIB006 , we need to fully understand the content of items (e.g., documents and movies), analyze the profile and preference of users, and evaluate the similarity among users. Deep learning is good at the first subtask while PGM excels at the other two. Besides the fact that better understanding of item content would help with the analysis of user profiles, the estimated similarity among users could provide valuable information for understanding item content in return. In order to fully utilize this bidirectional effect to boost recommendation accuracy, we might wish to unify deep learning and PGM in one single principled probabilistic framework, as done in BIB006 . Besides recommender systems, the need for Bayesian deep learning may also arise when we are dealing with control of non-linear dynamical systems with raw images as input. Consider controlling a complex dynamical system according to the live video stream received from a camera. This problem can be transformed into iteratively performing two tasks, perception from raw images and control based on dynamic models. The perception task can be taken care of using multiple layers of simple nonlinear transformation (deep learning) while the control task usually needs more sophisticated models like hidden Markov models and Kalman filters  , BIB004 . The feedback loop is then completed by the fact that actions chosen by the control model can affect the received video stream in return. To enable an effective iterative process between the perception task and the control task, we need two-way information exchange between them. The perception component would be the basis on which the control component estimates its states and the control component with a dynamic model built in would be able to predict the future trajectory (images). In such cases, Bayesian deep learning is a suitable choice BIB007 . Apart from the major advantage that BDL provides a principled way of unifying deep learning and PGM, another benefit comes from the implicit regularization built in BDL. By imposing a prior on hidden units, parameters defining a neural network, or the model parameters specifying the causal inference, BDL can to some degree avoid overfitting, especially when we do not have sufficient data. Usually, a BDL model consists of two components, a perception component that is a Bayesian formulation of a certain type of neural networks and a task-specific component that describes the relationship among different hidden or observed variables using PGM. Regularization is crucial for them both. Neural networks usually have large numbers of free parameters that need to be regularized properly. Regularization techniques like weight decay and dropout BIB005 are shown to be effective in improving performance of neural networks and they both have Bayesian interpretations  . In terms of the task-specific component, expert knowledge or prior information, as a kind of regularization, can be incorporated into the model through the prior we imposed to guide the model when data are scarce. Yet another advantage of using BDL for complex tasks (tasks that need both perception and inference) is that it provides a principled Bayesian approach of handling parameter uncertainty. When BDL is applied to complex tasks, there are three kinds of parameter uncertainty that need to be taken into account: 1) Uncertainty on the neural network parameters. 2) Uncertainty on the task-specific parameters. 3) Uncertainty of exchanging information between the perception component and the task-specific component. By representing the unknown parameters using distributions instead of point estimates, BDL offers a promising framework to handle these three kinds of uncertainty in a unified way. It is worth noting that the third uncertainty could only be handled under a unified framework like BDL. If we train the perception component and the task-specific component separately, it is equivalent to assuming no uncertainty when exchanging information between the two components. Of course, there are challenges when applying BDL to real-world tasks. (1) First, it is nontrivial to design an efficient Bayesian formulation of neural networks with reasonable time complexity. This line of work is pioneered by  ,  , BIB001 , but it has not been widely adopted due to its lack of scalability. Fortunately, some recent advances in this direction  , BIB008 , BIB003 , BIB009 ,  seem to shed light on the practical adoption of Bayesian neural network  . (2) The second challenge is to ensure efficient and effective information exchange between the perception component 1. Here we refer to Bayesian treatment of neural networks as Bayesian neural network. The other term, Bayesian deep learning, is retained to refer to complex Bayesian models with both a perception component and a task-specific component. and the task-specific component. Ideally both the first-order and second-order information (e.g., the mean and the variance) should be able to flow back and forth between the two components. A natural way is to represent the perception component as a PGM and seamlessly connect it to the task-specific PGM, as done in BIB010 , BIB011 , BIB006 . In this survey, we aim to give a comprehensive overview of BDL models for recommender systems, topic models (and representation learning), and control. The rest of the survey is organized as follows: In Section 2, we provide a review of some basic deep learning models. Section 3 covers the main concepts and techniques for PGM. These two sections serve as the background for BDL, and the next section, Section 4, would survey the BDL models applied to areas like recommender systems and control. Section 5 discusses some future research issues and concludes the paper.