Learning to Increment A Contextual Model

Date

2018-12-07

Author

Kalkan, Sinan
Doğan, Fethiye Irmak
Bozcan, İlker

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In this paper, we summarized our efforts on incremental construction of latent variables in context (topic) models. With our models, an agent can incrementally learn a representation of critical contextual information. We demonstrated that a learning-based formulation outperforms rule-based models, and generalizes well across many settings and to real data

URI

http://www.kovan.ceng.metu.edu.tr/~sinan/publications/NIPS2018_CL.pdf
https://hdl.handle.net/11511/76375

Conference Name

32nd Conference on Neural Information Processing Systems (NIPS 2018)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

S. Kalkan, F. I. Doğan, and İ. Bozcan, “Learning to Increment A Contextual Model,” Montréal, Canada, 2018, p. 1, Accessed: 00, 2021. [Online]. Available: http://www.kovan.ceng.metu.edu.tr/~sinan/publications/NIPS2018_CL.pdf.