@article{
 title = {Online Learning for Latent Dirichlet Allocation},
 type = {article},
 websites = {https://papers.nips.cc/paper/3902-online-learning-for-latent-dirichlet-allocation.pdf},
 id = {ab7bae81-a341-3d9b-8ebf-12729cff1b08},
 created = {2018-02-05T16:53:51.835Z},
 accessed = {2018-02-05},
 file_attached = {true},
 profile_id = {371589bb-c770-37ff-8193-93c6f25ffeb1},
 group_id = {f982cd63-7ceb-3aa2-ac7e-a953963d6716},
 last_modified = {2018-02-05T16:53:55.841Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {We develop an online variational Bayes (VB) algorithm for Latent Dirichlet Al-location (LDA). Online LDA is based on online stochastic optimization with a natural gradient step, which we show converges to a local optimum of the VB objective function. It can handily analyze massive document collections, includ-ing those arriving in a stream. We study the performance of online LDA in several ways, including by fitting a 100-topic topic model to 3.3M articles from Wikipedia in a single pass. We demonstrate that online LDA finds topic models as good or better than those found with batch VB, and in a fraction of the time.},
 bibtype = {article},
 author = {Hoffman, Matthew D and Blei, David M and Bach, Francis}
}

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