LightLDA: Big Topic Models on Modest Compute Clusters
Proceedings of the 24th International Conference on World Wide Web, pp. 1351-1361, 2015.
We have implemented a distributed Latent Dirichlet Allocation sampler, LightLDA, that enables very large data sizes and models to be processed on a small computer cluster
When building large-scale machine learning (ML) programs, such as massive topic models or deep neural networks with up to trillions of parameters and training examples, one usually assumes that such massive tasks can only be attempted with industrial-sized clusters with thousands of nodes, which are out of reach for most practitioners and...More
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