Papers by Jonathan Barron
A Deep Factorization of Style and Structure in Fonts (D19-1)
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| Challenge: | Using a variational inference procedure, we factor each training glyph into a combination of a character-specific content embedding and a latent font-specific style variable. |
| Approach: | They propose a deep factorization model that disentangles content from style by factorizing each training glyph into a latent content embedding and a learned embeddable character. |
| Outcome: | The proposed model outperforms a strong nearest neighbors baseline and state-of-the-art discriminative model on reconstructing missing glyphs from an unknown font given only a small number of observations. |
Limited Linguistic Diversity in Embodied AI Datasets (2026.acl-long)
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Selma Liliane Wanna, Agnes Luhtaru, Jonathan Salfity, Ryan Barron, Juston Moore, Cynthia Matuszek, Mitch Pryor
| Challenge: | Language is a key modality in Vision-Language-Action (VLA) models, yet its linguistic characteristics are poorly documented. |
| Approach: | They propose to audit VLA corpora to characterize what kinds of instructions they contain . they quantify instruction language along complementary dimensions including lexical variety, duplication and overlap, semantic similarity, and syntactic complexity. |
| Outcome: | The proposed dataset audit of several widely used VLA datasets shows that many datasets rely on highly repetitive, template-like commands with limited structural variation yielding a narrow distribution of instruction forms. |
Scalable Font Reconstruction with Dual Latent Manifolds (2021.emnlp-main)
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| Challenge: | a recent study has shown that fonts with a large number of missing glyphs are difficult to model due to the relative sparsity of most fonts. |
| Approach: | They propose a deep generative model that performs typography analysis and font reconstruction by learning disentangled manifolds of both font style and character shape. |
| Outcome: | The proposed model scales up the number of character types we can model compared to previous methods . it can generalize to characters that were not observed during training time, and it compares favorably to other models . |