Add to ORKGWe introduce a two-layer undirected graphical model, called a “Replicated Soft-max”, that can be used to model and automatically extract low-dimensional latent semantic representations from a large unstructured collection of documents. We present efficient learning and inference algorithms for this model, and show how a Monte-Carlo based method, Annealed Importance Sampling, can be used to pro-duce an accurate estimate of the log-probability the model assigns to test data. This allows us to demonstrate that the proposed model is able to generalize much better compared to Latent Dirichlet Allocation in terms of both the log-probability of held-out documents and the retrieval accuracy.
Based on vector-based representation, topic models, like latent Dirichlet allocation (LDA), are cons...
With the development of computer technology and the internet, increasingly large amounts of textual ...
Topic modeling is an unsupervised learning task that discovers the hidden topics in a ...
We describe a new model for learning meaningful representations of text docu-ments from an unlabeled...
We propose a new type of undirected graphical model suitable for topic modeling and dimensionality r...
In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, ...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of d...
Statistical topic models such as latent Dirich-let allocation have become enormously popu-lar in the...
Thesis (Master's)--University of Washington, 2014In their 2001 work Latent Dirichlet Allocation, Ble...
Latent Dirichlet Allocation (LDA) is a popular machine-learning technique that identifies latent str...
Probabilistic topic models are machine learning tools for processing and understanding large text d...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of ...
Abstract. Most nonparametric topic models such as Hierarchical Dirichlet Pro-cesses, when viewed as ...
Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling h...
Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requ...
Based on vector-based representation, topic models, like latent Dirichlet allocation (LDA), are cons...
With the development of computer technology and the internet, increasingly large amounts of textual ...
Topic modeling is an unsupervised learning task that discovers the hidden topics in a ...
We describe a new model for learning meaningful representations of text docu-ments from an unlabeled...
We propose a new type of undirected graphical model suitable for topic modeling and dimensionality r...
In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, ...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of d...
Statistical topic models such as latent Dirich-let allocation have become enormously popu-lar in the...
Thesis (Master's)--University of Washington, 2014In their 2001 work Latent Dirichlet Allocation, Ble...
Latent Dirichlet Allocation (LDA) is a popular machine-learning technique that identifies latent str...
Probabilistic topic models are machine learning tools for processing and understanding large text d...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of ...
Abstract. Most nonparametric topic models such as Hierarchical Dirichlet Pro-cesses, when viewed as ...
Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling h...
Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requ...
Based on vector-based representation, topic models, like latent Dirichlet allocation (LDA), are cons...
With the development of computer technology and the internet, increasingly large amounts of textual ...
Topic modeling is an unsupervised learning task that discovers the hidden topics in a ...