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--- ml:bayesian_methods [2021/10/29 12:12] – [Overviews] jmflanig
+++ ml:bayesian_methods [2023/06/15 07:36] (current) – external edit 127.0.0.1
@@ Line 8: / Line 8: @@
   * [[https://aclanthology.org/D07-1031.pdf|Johnson 2007 - Why doesn’t EM find good HMM POS-taggers?]]
-===== Bayesian Deep Learning =====
+===== Bayesian Neural Networks =====
 ==== Overviews ====
   * Blog post: [[https://jorisbaan.nl/2021/03/02/introduction-to-bayesian-deep-learning.html|Intro to Bayesian Deep Learning]]
   * [[https://arxiv.org/pdf/2001.10995.pdf|Wilson 2020 - The Case for Bayesian Deep Learning]]
+  * [[https://arxiv.org/pdf/2007.06823.pdf|Jospin et al 2020 - Hands-on Bayesian Neural Networks – a Tutorial for Deep Learning Users]]
+  * [[https://arxiv.org/pdf/2006.12024.pdf|Goan & Fookes 2020 - Bayesian Neural Networks: An Introduction and Survey]]
+  * [[https://arxiv.org/pdf/2006.01490.pdf|Charnock et al 2020 - Bayesian Neural Networks]] (Draft book chapter)
 ==== Papers ====
+  * [[https://dl.acm.org/doi/pdf/10.1145/3296957.3173212|Cai et al 2018 - VIBNN: Hardware Acceleration of Bayesian Neural Networks]]
+  * [[https://arxiv.org/pdf/1901.02731.pdf|Shridhar et al 2019 - A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference]]
   * [[https://papers.nips.cc/paper/2020/file/0b1ec366924b26fc98fa7b71a9c249cf-Paper.pdf|He et al 2020 - Bayesian Deep Ensembles via the Neural Tangent Kernel]]
+  * [[https://arxiv.org/pdf/2002.04033.pdf|Karaletsos & Bui 2020 - Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights]]
+===== Variational Bayes =====
+==== Overviews ====
+  * [[https://arxiv.org/pdf/2103.01327.pdf|Tran et al 2021 - A Practical Tutorial on Variational Bayes]]
+==== Papers ====
+  * [[https://arxiv.org/pdf/1705.03439.pdf|Wang & Blei - Frequentist Consistency of Variational Bayes]]
 ===== Bayesian Nonparametrics =====
@@ Line 52: / Line 68: @@
 ===== Related Pages =====
-   * [[Probabilistic Graphical Models]]  Bayesian methods often use techniques from graphical models, such as MCMC and variational inference, as well as representing likelihood and prior as a graphical model
+   * [[Graphical Models]]  Bayesian methods often use techniques from graphical models, such as MCMC and variational inference, as well as representing likelihood and prior as a graphical model