text:   Modern Printing",6,"['In our #acl2020nlp paper, we propose a generative model to analyze glyph shapes in early-modern printing. The interpretable variables account for spatial noise while the neurally parametrized uninterpretable latent variable explains other variation. <LINK> 1/6 <LINK>', 'This model learns the underlying glyph templates and allows to cluster on the basis of subtle differences in glyph shapes in presence of multiple confounding sources of variance like inking. 2/6 https://t.co/DYqNiQvRWr', 'In order to ensure that the expressive neurally parametrized variable does not learn to explain the spatial adjustment noise (translation, shear, rotation, scale) captured by the interpretable variables, it is crucial to restrict the inference network for q(z). 3/6 https://t.co/43ShyS8BqS', 'Our model discovers different fonts used in early modern documents in a completely unsupervised manner! These are the discovered glyph shapes for Fs and Rs in Leviathan (1651?) 4/6 https://t.co/KTbKblska3', 'The interpretable variables also provide explicit control. We used the estimated offsets, rotation/shear angles and scaling factors to project the noisy images to a fixed size frame resulting in aligned/comparable input images. 5/6 https://t.co/PAMjeykCVj', ""This is joint work with fantastic collaborators @BergKirkpatrick, @ChrisVVarren, @redpony and Max G'Sell. Code coming soon! 6/6""]",20,05,1297