Finding scientific topics

By T. Griffiths, Mark Steyvers. 2004

6,704 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing, Information Extraction

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In Molecular Biology

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Citations in the corpus

Demographic Dialectal Variation in Social Media: A Case Study of African-American English (2016, methodology): “The sole exception is the word brotha, which appears to have been adopted into general usage as its own lexical item.”
Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017, background): “(4), thus Gibbs Sampling [7] is usually applied to estimate the posterior probability p(z |w,α , β) and solve Θ, Φ iteratively.”
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014, methodology): “We use collapsed Gibbs sampling to perform inference, as this has been observed to converge quickly, particularly in mixture models [17].”
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections (2006, methodology): “As in [11], we specialize to scalar hyperparameters (e.”
Petuum: A New Platform for Distributed Machine Learning on Big Data (2013, methodology): “We begin by formalizing ML algorithms as iterativeconvergent programs, which encompass a large space of modern ML, such as stochastic gradient descent (SGD)[18] and coordinate descent (CD) [10] for convex optimization problems, proximal methods [19] for more complex constrained optimization, as well”
Graph-Sparse LDA: A Topic Model with Structured Sparsity (2014, background): “The modeling objective varies depending on the application.”
Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds (2017, methodology): “We implement our TM solver using collapsed Gibbs sampling [30].”
Unsupervised learning for text-to-speech synthesis (2013, neutral): “, 2003) or Gibbs sampling (Griffiths and Steyvers, 2004).”
Unsupervised Learning and Segmentation of Complex Activities from Video (2018, methodology): “Specifically, we use slice sampling for ρ and collapsed Gibbs sampling [8] for z.”
Proximal Methods for Hierarchical Sparse Coding (2010, neutral): “, see Hastie et al., 2009). Before turning to optimization methods for the hierarchical sparse coding problem, we consider a particular instance. The sparse group Lasso was recently considered by Sprechmann et al. (2010) and Friedman et al.”
On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking (2008, methodology): “Among other approaches, statistical modeling using versions of PLSI (e.g. [5]) and LDA (e.g. [16, 3, 6, 13, 12, 15]) have been deployed to solve this task.”
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews (2018, methodology): “Different approximate inference methods have been developed for parameter estimation in topic models, such as variation inference [5] and collapsed Gibbs sampling [15].”
Scaling Politically Meaningful Dimensions Using Texts and Votes (2014, methodology): “Our estimator is a built from the LDA collapsed Gibbs sampler described by Griffiths and Steyvers (Griffiths and Steyvers 2004) and the IRT Gibbs sampler described by Jackman (Jackman 2001).”
WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation (2015, methodology): “We now describe our methodology to analyze the memory access efficiency, and analyze of existing fast sampling algorithms of LDA [30, 21, 32, 31] to motivate the development of WarpLDA.”
The Security of Latent Dirichlet Allocation (2015, methodology): “We simply take the poisoned corpus by Alg1 and give it to LDA with collapsed Gibbs sampling (Griffiths & Steyvers 2004).”