Annealing Paths for the Evaluation of Topic Models
暂无分享,去创建一个
[1] Jason Baldridge,et al. A recursive estimate for the predictive likelihood in a topic model , 2013, AISTATS.
[2] Philip Resnik,et al. Modeling topic control to detect influence in conversations using nonparametric topic models , 2014, Machine Learning.
[3] Francis R. Bach,et al. Online Learning for Latent Dirichlet Allocation , 2010, NIPS.
[4] Andrew McCallum,et al. Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression , 2008, UAI.
[5] Timothy Baldwin,et al. Automatic Evaluation of Topic Coherence , 2010, NAACL.
[6] Justin Grimmer,et al. A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases , 2010, Political Analysis.
[7] Ruslan Salakhutdinov,et al. Annealing between distributions by averaging moments , 2013, NIPS.
[8] Ruslan Salakhutdinov,et al. Evaluation methods for topic models , 2009, ICML '09.
[9] James R. Foulds,et al. Latent Variable Modeling for Networks and Text: Algorithms, Models and Evaluation Techniques , 2014 .
[10] Dragomir R. Radev,et al. The ACL anthology network corpus , 2009, Language Resources and Evaluation.
[11] Chong Wang,et al. Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.
[12] Radford M. Neal. Annealed importance sampling , 1998, Stat. Comput..
[13] Jeffrey Heer,et al. Differentiating language usage through topic models , 2013 .
[14] Andrew McCallum,et al. Optimizing Semantic Coherence in Topic Models , 2011, EMNLP.
[15] Thomas L. Griffiths,et al. The Author-Topic Model for Authors and Documents , 2004, UAI.
[16] James R. Foulds,et al. Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation , 2013, KDD.
[17] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[18] Wray L. Buntine. Estimating Likelihoods for Topic Models , 2009, ACML.
[19] Andrew McCallum,et al. Rethinking LDA: Why Priors Matter , 2009, NIPS.
[20] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..