Latent dirichlet allocation
TLDR
This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.Abstract:
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.read more
Citations
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Journal ArticleDOI
From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure.
Russell A. Poldrack,Tal Yarkoni +1 more
TL;DR: A number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings are reviewed, and how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis.
Proceedings ArticleDOI
Reducing the sampling complexity of topic models
TL;DR: An algorithm which scales linearly with the number of actually instantiated topics kd in the document, for large document collections and in structured hierarchical models kd ll k, yields an order of magnitude speedup.
Book
Neural Machine Translation
TL;DR: A comprehensive treatment of the topic, ranging from introduction to neural networks, computation graphs, description of the currently dominant attentional sequence-to-sequence model, recent refinements, alternative architectures and challenges.
Proceedings ArticleDOI
Semi-supervised learning of compact document representations with deep networks
TL;DR: An algorithm to learn text document representations based on semi-supervised autoencoders that are stacked to form a deep network that can be trained efficiently on partially labeled corpora, producing very compact representations of documents, while retaining as much class information and joint word statistics as possible.
Journal ArticleDOI
Sourcerer: mining and searching internet-scale software repositories
TL;DR: By combining software textual content with structural information captured by the CodeRank approach, this work is able to significantly improve software retrieval performance, increasing the area under the curve (AUC) retrieval metric to 0.92, roughly 10–30% better than previous approaches based on text alone.
References
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Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
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Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
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Harold Jeffreys,R. Bruce Lindsay +1 more
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