Papers by Paidamoyo Chapfuwa
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (P18-1)
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Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa, Wenlin Wang, Guoyin Wang, Ricardo Henao, Lawrence Carin
| Challenge: | Existing approaches to fast similarity search require two-stage training and the binary constraints are handled ad-hoc. |
| Approach: | They propose an end-to-end neural architecture for semantic hashing where binary hash codes are treated as Bernoulli latent variables. |
| Outcome: | The proposed approach outperforms state-of-the-art models on unsupervised and supervised scenarios on three public datasets. |