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: Representation Learning: A Review and New Perspectives 
Chapter Title:  5. 
1. Abstract of Cited Paper (BIB001):  The deep Boltzmann machine (DBM) has been an important development in the quest for powerful "deep" probabilistic models. To date, simultaneous or joint training of all layers of the DBM has been largely unsuccessful with existing training methods. We introduce a simple regularization scheme that encourages the weight vectors associated with each hidden unit to have similar norms. We demonstrate that this regularization can be easily combined with standard stochastic maximum likelihood to yield an effective training strategy for the simultaneous training of all layers of the deep Boltzmann machine.

Literature Review:
Finally, update the model parameters with a step of approximate stochastic gradient ascent. While the above procedure appears to be a simple extension of the highly effective SML scheme for training RBMs, as we demonstrate in Desjardins et al. BIB002 , this procedure seems vulnerable to falling in poor local minima that leave many hidden units effectively dead (not significantly different from its random initialization with small norm). The failure of the SML joint training strategy was noted by Salakhutdinov and Hinton BIB001 . As an alternative, they proposed a greedy layerwise training strategy. This procedure consists of pretraining the layers of the DBM, in much the same way as the DBN, i.e., by stacking RBMs and training each layer to independently model the output of the previous layer. A final joint "fine tuning" is done following the above SML-based procedure.