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ml:graphical_models

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ml:graphical_models [2023/06/15 07:36] – external edit 127.0.0.1ml:graphical_models [2025/05/03 01:43] (current) – [Interesting NLP Deep Learning + PGM Papers] jmflanig
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 ===== Overviews ===== ===== Overviews =====
   * Graphical Models   * Graphical Models
-    * [[https://linqs.soe.ucsc.edu/sites/default/files/papers/koller-book07.pdf|Koller et al 2007 - Graphical Models in a Nutshell]] (book chapter)+    * [[https://direct.mit.edu/books/edited-volume/3811/chapter-standard/125067/Graphical-Models-in-a-Nutshell|Koller et al 2007 - Graphical Models in a Nutshell]] (book chapter)
   * Deep Latent Variable Models   * Deep Latent Variable Models
     * Paper: [[https://arxiv.org/pdf/1812.06834.pdf|Kim et al 2018 - A Tutorial on Deep Latent Variable Models of Natural Language]]     * Paper: [[https://arxiv.org/pdf/1812.06834.pdf|Kim et al 2018 - A Tutorial on Deep Latent Variable Models of Natural Language]]
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   * [[https://arxiv.org/pdf/2106.02736.pdf|Goyal et al 2021 - Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis–Hastings]]   * [[https://arxiv.org/pdf/2106.02736.pdf|Goyal et al 2021 - Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis–Hastings]]
   * [[https://arxiv.org/pdf/2105.15021.pdf|Yang et al 2021 - Neural Bi-Lexicalized PCFG Induction]] Uses a Bayesian network to describe their model   * [[https://arxiv.org/pdf/2105.15021.pdf|Yang et al 2021 - Neural Bi-Lexicalized PCFG Induction]] Uses a Bayesian network to describe their model
 +  * [[https://arxiv.org/pdf/2406.06950|Hou et al 2024 - A Probabilistic Framework for LLM Hallucination Detection via Belief Tree Propagation]]
  
 ===== Recent NLP Papers that Use PGMs ==== ===== Recent NLP Papers that Use PGMs ====
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     * [[https://aclanthology.org/P19-1186.pdf|Lee et al 2019 - Semi-supervised Stochastic Multi-Domain Learning using Variational Inference]]     * [[https://aclanthology.org/P19-1186.pdf|Lee et al 2019 - Semi-supervised Stochastic Multi-Domain Learning using Variational Inference]]
     * [[https://aclanthology.org/2020.acl-main.367.pdf|Emerson 2020 - Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics]]     * [[https://aclanthology.org/2020.acl-main.367.pdf|Emerson 2020 - Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics]]
 +  * **Other Papers**
 +    * [[https://arxiv.org/pdf/2306.05836.pdf|Jin et al 2023 - Can Large Language Models Infer Causation from Correlation?]]
  
 ===== Courses, Tutorials, and Overview Papers ===== ===== Courses, Tutorials, and Overview Papers =====
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   * Sometimes PGMS are covered in the UCSC course [[https://courses.soe.ucsc.edu/courses/cse290c|CSE 290C]] (when [[https://courses.soe.ucsc.edu/courses/cmps290c/Fall15/01|Lisa Geetoor teaches it]])   * Sometimes PGMS are covered in the UCSC course [[https://courses.soe.ucsc.edu/courses/cse290c|CSE 290C]] (when [[https://courses.soe.ucsc.edu/courses/cmps290c/Fall15/01|Lisa Geetoor teaches it]])
   * **Course at CMU**: Probabilistic Graphical Models [[https://www.cs.cmu.edu/~epxing/Class/10708-20/index.html|Spring 2020]] [[https://www.cs.cmu.edu/~epxing/Class/10708-20/lectures.html|Lectures with videos]] [[https://www.cs.cmu.edu/~epxing/Class/10708/|2014 (with videos and scribe notes)]]   * **Course at CMU**: Probabilistic Graphical Models [[https://www.cs.cmu.edu/~epxing/Class/10708-20/index.html|Spring 2020]] [[https://www.cs.cmu.edu/~epxing/Class/10708-20/lectures.html|Lectures with videos]] [[https://www.cs.cmu.edu/~epxing/Class/10708/|2014 (with videos and scribe notes)]]
 +  * Stanford course: [[https://ermongroup.github.io/cs228/|CS 228 - Probabilistic Graphical Models]]
   * **Matt Gormley's course at CMU**: [[https://www.cs.cmu.edu/~mgormley/courses/10418/|10418]] (with videos)   * **Matt Gormley's course at CMU**: [[https://www.cs.cmu.edu/~mgormley/courses/10418/|10418]] (with videos)
   * **Best overview tutorial:** [[https://kuleshov.github.io/cs228-notes/|CS228 Lecture Notes]]   * **Best overview tutorial:** [[https://kuleshov.github.io/cs228-notes/|CS228 Lecture Notes]]
ml/graphical_models.1686814574.txt.gz · Last modified: 2023/06/15 07:36 by 127.0.0.1
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