Restricted Boltzmann machines modeling human choice

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Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data (2016, background): “Examples of these applications are: modeling human choice (Osogami and Otsuka, 2014), collaborative filtering (Salakhutdinov et al, 2007), information retrieval (Gehler et al, 2006), transfer learning (Ammar et al, 2013), or multiclass classification (Larochelle and Bengio, 2008).”
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science (2017, positive): “…supervised training of deep arti ﬁ cial neural network architectures 42 , are also very successful as stand alone models in a variety of tasks, such as density estimation to model human choice 31 , collaborative ﬁ ltering 58 , information retrieval 59 , multi-class classi ﬁ cation 60 , and so on.”
A topological insight into restricted Boltzmann machines (2016, methodology): “Examples are density estimation to model human choice (Osogami and Otsuka, 2014), collaborative ﬁl-tering (Salakhutdinov et al, 2007), information retrieval (Gehler et al, 2006), or multi-class classiﬁcation (Larochelle and Bengio, 2008).”