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Abstract
Despite many years of research into latent Dirichlet allocation (LDA), applying LDA to collections of non-categorical items is still challenging for practitioners. Yet many problems with much richer data share a similar structure and could benefit from the vast literature on LDA. We propose logistic LDA, a novel discriminative variant of latent Dirichlet allocation which is easy to apply to arbitrary inputs. In particular, our model can easily be applied to groups of images, arbitrary text embeddings, or integrate deep neural networks. Although it is a discriminative model, we show that logistic LDA can learn from unlabeled data in an unsupervised manner by exploiting the group structure present in the data. In contrast to other recent topic models designed to handle arbitrary inputs, our model does not sacrifice the interpretability and principled motivation of LDA.

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MLA
Korshunova, Iryna, et al. “Discriminative Topic Modeling with Logistic LDA.” ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), edited by H. Wallah et al., vol. 32, 2019.
APA
Korshunova, I., Xiong, H., Fedoryszak, M., & Theis, L. (2019). Discriminative topic modeling with logistic LDA. In H. Wallah, H. Larochelle, A. Beygelzimer, F. d’Alche-Buc, E. Fox, & R. Garnett (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) (Vol. 32). Vancouver, Canada.
Chicago author-date
Korshunova, Iryna, Hanchen Xiong, Mateusz Fedoryszak, and Lucas Theis. 2019. “Discriminative Topic Modeling with Logistic LDA.” In ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), edited by H. Wallah, H. Larochelle, A. Beygelzimer, F. d’Alche-Buc, E. Fox, and R. Garnett. Vol. 32.
Chicago author-date (all authors)
Korshunova, Iryna, Hanchen Xiong, Mateusz Fedoryszak, and Lucas Theis. 2019. “Discriminative Topic Modeling with Logistic LDA.” In ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), ed by. H. Wallah, H. Larochelle, A. Beygelzimer, F. d’Alche-Buc, E. Fox, and R. Garnett. Vol. 32.
Vancouver
1.
Korshunova I, Xiong H, Fedoryszak M, Theis L. Discriminative topic modeling with logistic LDA. In: Wallah H, Larochelle H, Beygelzimer A, d’Alche-Buc F, Fox E, Garnett R, editors. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019). 2019.
IEEE
[1]
I. Korshunova, H. Xiong, M. Fedoryszak, and L. Theis, “Discriminative topic modeling with logistic LDA,” in ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), Vancouver, Canada, 2019, vol. 32.
@inproceedings{8638059,
  abstract     = {Despite many years of research into latent Dirichlet allocation (LDA), applying LDA to collections of non-categorical items is still challenging for practitioners. Yet many problems with much richer data share a similar structure and could benefit from the vast literature on LDA. We propose logistic LDA, a novel discriminative variant of latent Dirichlet allocation which is easy to apply to arbitrary inputs. In particular, our model can easily be applied to groups of images, arbitrary text embeddings, or integrate deep neural networks. Although it is a discriminative model, we show that logistic LDA can learn from unlabeled data in an unsupervised manner by exploiting the group structure present in the data. In contrast to other recent topic models designed to handle arbitrary inputs, our model does not sacrifice the interpretability and principled motivation of LDA.},
  author       = {Korshunova, Iryna and Xiong, Hanchen and Fedoryszak, Mateusz and Theis, Lucas},
  booktitle    = {ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019)},
  editor       = {Wallah, H. and Larochelle, H. and Beygelzimer, A. and d'Alche-Buc, F. and Fox, E. and Garnett, R.},
  issn         = {1049-5258},
  language     = {eng},
  location     = {Vancouver, Canada},
  title        = {Discriminative topic modeling with logistic LDA},
  url          = {https://github.com/lucastheis/logistic_lda},
  volume       = {32},
  year         = {2019},
}

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