Add to ORKGThis paper describes a high performance sampling archi-tecture for inference of latent topic models on a cluster of workstations. Our system is faster than previous work by over an order of magnitude and it is capable of dealing with hundreds of millions of documents and thousands of topics. The algorithm relies on a novel communication structure, namely the use of a distributed (key, value) storage for syn-chronizing the sampler state between computers. Our archi-tecture entirely obviates the need for separate computation and synchronization phases. Instead, disk, CPU, and net-work are used simultaneously to achieve high performance. We show that this architecture is entirely general and that it can be extended easily to more sophisticated...
Topic models provide a powerful tool for analyzing large text collections by representing high dimen...
Scalable and effective analysis of large text corpora remains a chal-lenging problem as our ability ...
Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieva...
In real world industrial applications of topic modeling, the ability to capture gigantic conceptual ...
Topic modeling algorithms (like Latent Dirichlet Allocation) tend to be very slow when run over larg...
<p>When building large-scale machine learning (ML) programs, such as big topic models or deep neural...
We describe distributed algorithms for two widely-used topic models, namely the Latent Dirichlet All...
In real world industrial applications of topic modeling, the ability to capture gigantic conceptual ...
Editor: We describe distributed algorithms for two widely-used topic models, namely the Latent Diric...
Learning meaningful topic models with massive document collections which contain millions of documen...
Inference in topic models typically involves a sampling step to associate latent variables with obse...
ABSTRACT Inference in topic models typically involves a sampling step to associate latent variables ...
Abstract. Topic modeling techniques have been widely used to uncover dominant themes hidden inside a...
Topic models provide a powerful tool for analyzing large text collections by representing high dimen...
We present LDA*, a system that has been deployed in one of the largest Internet companies to fulfil ...
Topic models provide a powerful tool for analyzing large text collections by representing high dimen...
Scalable and effective analysis of large text corpora remains a chal-lenging problem as our ability ...
Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieva...
In real world industrial applications of topic modeling, the ability to capture gigantic conceptual ...
Topic modeling algorithms (like Latent Dirichlet Allocation) tend to be very slow when run over larg...
<p>When building large-scale machine learning (ML) programs, such as big topic models or deep neural...
We describe distributed algorithms for two widely-used topic models, namely the Latent Dirichlet All...
In real world industrial applications of topic modeling, the ability to capture gigantic conceptual ...
Editor: We describe distributed algorithms for two widely-used topic models, namely the Latent Diric...
Learning meaningful topic models with massive document collections which contain millions of documen...
Inference in topic models typically involves a sampling step to associate latent variables with obse...
ABSTRACT Inference in topic models typically involves a sampling step to associate latent variables ...
Abstract. Topic modeling techniques have been widely used to uncover dominant themes hidden inside a...
Topic models provide a powerful tool for analyzing large text collections by representing high dimen...
We present LDA*, a system that has been deployed in one of the largest Internet companies to fulfil ...
Topic models provide a powerful tool for analyzing large text collections by representing high dimen...
Scalable and effective analysis of large text corpora remains a chal-lenging problem as our ability ...
Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieva...