Papers by Nikita Srivatsan
Modeling Online Discourse with Coupled Distributed Topics (D18-1)
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| Challenge: | a topic model that incorporates structural relationships connecting documents in socially generated corpora is of limited application in the sciences. |
| Approach: | They propose a topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. |
| Outcome: | The proposed model captures discursive interactions along observed reply links and integrates latent distributed representations in a deep architecture. |
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. |
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 . |