text: 466,191,1257783060214530048,38844142,kartik goyal,"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 <LINK> 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 <LINK> 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 <LINK> 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 <LINK> This is joint work with fantastic collaborators @BergKirkpatrick, @ChrisVVarren, @redpony and Max G'Sell. Code coming soon! 6/6",https://arxiv.org/abs/2005.01646,"We propose a deep and interpretable probabilistic generative model to analyze glyph shapes in printed Early Modern documents. We focus on clustering extracted glyph images into underlying templates in the presence of multiple confounding sources of variance. Our approach introduces a neural editor model that first generates well-understood printing phenomena like spatial perturbations from template parameters via interpertable latent variables, and then modifies the result by generating a non-interpretable latent vector responsible for inking variations, jitter, noise from the archiving process, and other unforeseen phenomena associated with Early Modern printing. Critically, by introducing an inference network whose input is restricted to the visual residual between the observation and the interpretably-modified template, we are able to control and isolate what the vector-valued latent variable captures. We show that our approach outperforms rigid interpretable clustering baselines (Ocular) and overly-flexible deep generative models (VAE) alike on the task of completely unsupervised discovery of typefaces in mixed-font documents. ","A Probabilistic Generative Model for Typographical Analysis of Early