A Two-Layer Model of Natural Stimuli Estimated with Score Matching

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How researchers see this paper

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Density Modeling of Images using a Generalized Normalization Transformation (2015, methodology): “The topographic ICA model (TICA; Hyvärinen et al., 2001) and the model presented in Köster & Hyvärinen (2010) are generalizations of ISA that are related to our model, but have more constrained nonlinearities.”
Learning Deep Energy Models (2011, methodology): “A potential alternative to samplingbased learning is to use score matching to train our models; for example, Kingma & LeCun (2010) showed how score matching can be applied to arbitary energy functions while Köster & Hyvärinen (2010) showed how to train both layers of a two layer ICA-based model.”
Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex (2012, methodology): “Other research has shown that complex-cell properties can emerge without using temporal correlations of the input (Hyv¨arinen & Hoyer, 2000, 2001; Osindero, Welling, & Hinton, 2006; Karklin & Lewicki, 2009; K¨oster & Hyv¨arinen, 2010).”
Sparse Filtering (2011, methodology): “While recent work by [21, 22] has also been able to learn meaningful second layer features, our method is simpler to implement, fast to run and does not require time-consuming tuning of hyperparameters.”