world:lda
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world:lda [2023/01/10 15:54] rdouc |
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| ====== Sujet et ressources ====== | ====== Sujet et ressources ====== | ||
| - | Ce projet de recherche tournera autour des LDA (Latent Dirichlet Allocation) | + | * **Binôme:** Paul Héllégouarch et Yassine Maalej |
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| + | Ce projet de recherche consistera à étudier les papiers: | ||
| + | * [[https://jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf|Stochastic Variational Inférence]] (LDA, version appliquée via Stochastic VI) | ||
| + | * [[https://users.stat.ufl.edu/~doss/Research/lda-ntopics.pdf|Inference for the Number of Topics in the Latent | ||
| + | Dirichlet Allocation Model via Bayesian Mixture | ||
| + | Modelling]] by Zhe Chen and Ani Doss, 2019. (LDA version théorique via Bayesian Mixture modelling et Monte Carlo by Markov Chains techniques) | ||
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| + | ===== Lectures complémentaires ===== | ||
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| + | * Le papier original introduisant la LDA est celui là: [[https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf?ref=https://githubhelp.com| Latent Dirichlet Allocation, 2003]] by David Blei, Andrew Ng, Michael Jordan. On pourra lire aussi cette survey [[https://www.eecis.udel.edu/~shatkay/Course/papers/UIntrotoTopicModelsBlei2011-5.pdf| Introduction to probabilistic topics models]] | ||
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| + | * Pour info: | ||
| + | * Liste de papiers faisant le lien entre musique et LDA: [[https://scholar.google.fr/scholar?hl=fr&as_sdt=0%2C5&q=music+latent+dirichlet+allocation&oq=music+|liste de papiers]] | ||
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| - | * On commencera par lire certaines propriétés théoriques dans: [[https://par.nsf.gov/servlets/purl/10160128|Inference for the Number of Topics in the Latent Dirichlet Allocation Model via | ||
| - | Bayesian Mixture Modeling, by Zhe Chen and Ani Doss, 2019]] | ||
world/lda.1673362446.txt.gz · Last modified: 2023/01/10 15:54 by rdouc
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