Fast Latent Variable Models for Inference and Visualization on Mobile Devices [article]

Joseph W Robinson, Aaron Q Li
2015 arXiv   pre-print
In this project we outline Vedalia, a high performance distributed network for performing inference on latent variable models in the context of Amazon review visualization. We introduce a new model, RLDA, which extends Latent Dirichlet Allocation (LDA) [Blei et al., 2003] for the review space by incorporating auxiliary data available in online reviews to improve modeling while simultaneously remaining compatible with pre-existing fast sampling techniques such as [Yao et al., 2009; Li et al.,
more » ... 4a] to achieve high performance. The network is designed such that computation is efficiently offloaded to the client devices using the Chital system [Robinson & Li, 2015], improving response times and reducing server costs. The resulting system is able to rapidly compute a large number of specialized latent variable models while requiring minimal server resources.
arXiv:1510.07035v1 fatcat:laoi2rjdebgtbaeom4ukpj3tbe