• BP-sLDA (Chen et al., 2015): it applies back propagation over a deep architecture together with stochastic gradient/mirror descent for  The third kind is a supervised n-gram model named sNTM, which is applicable to predict both categorical and real-valued labels for new documents (Cao et al., 2015).
the results indicate that although all models can learn meaningful topics, SLtM performs better than baseline models in label-specific topic discovery.