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:  Autoencoders 
1. Abstract of Cited Paper (BIB001):  The multilayer perceptron, when working in auto-association mode, is sometimes considered as an interesting candidate to perform data compression or dimensionality reduction of the feature space in information processing applications. The present paper shows that, for auto-association, the nonlinearities of the hidden units are useless and that the optimal parameter values can be derived directly by purely linear techniques relying on singular value decomposition and low rank matrix approximation, similar in spirit to the well-known Karhunen-Loève transform. This approach appears thus as an efficient alternative to the general error back-propagation algorithm commonly used for training multilayer perceptrons. Moreover, it also gives a clear interpretation of the rôle of the different parameters. 
2. Abstract of Cited Paper (BIB002):  We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying non-linearity that turns a code vector into a quasi-binary sparse code vector. Given an input, the optimal code minimizes the distance between the output of the decoder and the input patch while being as similar as possible to the encoder output. Learning proceeds in a two-phase EM-like fashion: (1) compute the minimum-energy code vector, (2) adjust the parameters of the encoder and decoder so as to decrease the energy. The model produces "stroke detectors" when trained on handwritten numerals, and Gabor-like filters when trained on natural image patches. Inference and learning are very fast, requiring no preprocessing, and no expensive sampling. Using the proposed unsupervised method to initialize the first layer of a convolutional network, we achieved an error rate slightly lower than the best reported result on the MNIST dataset. Finally, an extension of the method is described to learn topographical filter maps. 
3. Abstract of Cited Paper (BIB003):  We explore an original strategy for building deep networks, based on stacking layers of denoising autoencoders which are trained locally to denoise corrupted versions of their inputs. The resulting algorithm is a straightforward variation on the stacking of ordinary autoencoders. It is however shown on a benchmark of classification problems to yield significantly lower classification error, thus bridging the performance gap with deep belief networks (DBN), and in several cases surpassing it. Higher level representations learnt in this purely unsupervised fashion also help boost the performance of subsequent SVM classifiers. Qualitative experiments show that, contrary to ordinary autoencoders, denoising autoencoders are able to learn Gabor-like edge detectors from natural image patches and larger stroke detectors from digit images. This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations. 
4. Abstract of Cited Paper (BIB004):  We present in this paper a novel approach for training deterministic auto-encoders. We show that by adding a well chosen penalty term to the classical reconstruction cost function, we can achieve results that equal or surpass those attained by other regularized auto-encoders as well as denoising auto-encoders on a range of datasets. This penalty term corresponds to the Frobenius norm of the Jacobian matrix of the encoder activations with respect to the input. We show that this penalty term results in a localized space contraction which in turn yields robust features on the activation layer. Furthermore, we show how this penalty term is related to both regularized auto-encoders and denoising auto-encoders and how it can be seen as a link between deterministic and non-deterministic auto-encoders. We find empirically that this penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold. Finally, we show that by using the learned features to initialize a MLP, we achieve state of the art classification error on a range of datasets, surpassing other methods of pretraining.

Literature Review:
An autoencoder (AE) is a feedforward neural network to encode the input into a more compact representation and reconstruct the input with the learned representation. In its simplest form, an autoencoder is no more than a multilayer perceptron with a bottleneck layer (a layer with a small number of hidden units) in the middle. The idea of autoencoders has been around for decades BIB002 ,  , BIB001 ,  and abundant variants of autoencoders have been proposed to enhance representation learning including sparse AE BIB003 , contrastive AE BIB005 , and denoising AE BIB004 . For more details, please refer to a nice recent book on deep learning  . Here we introduce a kind of multilayer denoising AE, known as stacked denoising autoencoders (SDAE), both as an example of AE variants and as background for its applications on BDL-based recommender systems in Section 4. SDAE BIB004 is a feedforward neural network for learning representations (encoding) of the input data by learning to predict the clean input itself in the output, as shown in Figure 1 . The hidden layer in the middle, i.e., X 2 in the figure, can be constrained to be a bottleneck to learn compact representations. The difference between traditional AE and SDAE is that the input layer X 0 is a corrupted version of the clean input data. Essentially an SDAE solves the following optimization problem: where λ is a regularization parameter and · F denotes the Frobenius norm. Here SDAE can be regarded as a multilayer perceptron for regression tasks described in the previous section. The input X 0 of the MLP is the corrupted version of the data and the target Y is the clean version of the data X c . For example, X c can be the raw data matrix, and we can randomly set 30% of the entries in X c to 0 and get X 0 . In a nutshell, SDAE learns a neural network that takes the noisy data as input and recovers the clean data in the last layer. This is what 'denoising' in the name means. Normally, the output of the middle layer, i.e., X 2 in Figure 1 , would be used to compactly represent the data.