A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

FitNets: Hints for Thin Deep Nets (2014, background): “Along this line of reasoning, (Cho et al., 2012) ease the optimization problem of DBM by borrowing the activations of another model every second layer in a purely unsupervised scenario.”
Deep learning for visual understanding: A review (2016, background): “The improvements can either take place at the pre-training stage [68,69] or at the training stage [70,71].”
Semi-Amortized Variational Autoencoders (2018, methodology): “…closely related the line of work which uses a separate model to initialize variational parameters and subsequently updates them through an iterative procedure (Salakhutdinov & Larochelle, 2010; Cho et al., 2013; Salimans et al., 2015; Hjelm et al., 2016; Krishnan et al., 2018; Pu et al., 2017).”
Deep learning for visual understanding (2017, unknown): “The improvements can either take place at the pre-training stage [94, 95] or at the training stage [96, 97].”
Deep Learning for Computer Vision: A Brief Review (2018, methodology): “These include accelerating inference by using separate models to initialize the values of the hidden units in all layers [47, 49], or other improvements at the pretraining stage [50, 51] or at the training stage [52, 53].”