Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
نویسندگان
چکیده
منابع مشابه
Hybrid Variational/Gibbs Collapsed Inference in Topic Models
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is also inefficient for large count values and requires averaging over many samples to reduce variance. On the other hand, variational Bayesian inference is efficient and ...
متن کاملModel-Parallel Inference for Big Topic Models
In real world industrial applications of topic modeling, the ability to capture gigantic conceptual space by learning an ultra-high dimensional topical representation, i.e., the so-called “big model”, is becoming the next desideratum after enthusiasms on ”big data”, especially for fine-grained downstream tasks such as online advertising, where good performances are usually achieved by regressio...
متن کاملOnline Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models
Topic models for text analysis are most commonly trained using either Gibbs sampling or variational Bayes. Recently, hybrid variational-Gibbs algorithms have been found to combine the best of both worlds. Variational algorithms are fast to converge and more efficient for inference on new documents. Gibbs sampling enables sparse updates since each token is only associated with one topic instead ...
متن کاملFully Sparse Topic Models
In this paper, we propose Fully Sparse Topic Model (FSTM) for modeling large collections of documents. Three key properties of the model are: (1) the inference algorithm converges in linear time, (2) learning of topics is simply a multiplication of two sparse matrices, (3) it provides a principled way to directly trade off sparsity of solutions against inference quality and running time. These ...
متن کاملProvable Algorithms for Inference in Topic Models
Recently, there has been considerable progress on designing algorithms with provable guarantees — typically using linear algebraic methods — for parameter learning in latent variable models. But designing provable algorithms for inference has proven to be more challenging. Here we take a first step towards provable inference in topic models. We leverage a property of topic models that enables u...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2018
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2017.1366913