Fix citations

gdalle · Feb 22, 2024 · f28be9b · f28be9b
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diff --git a/paper/HMM.bib b/paper/HMM.bib
@@ -1,3 +1,16 @@
+@software{antonelloHMMGradientsJlEnables2021,
+  title = {{{HMMGradients}}.Jl: {{Enables}} Computing the Gradient of the Parameters of {{Hidden Markov Models}} ({{HMMs}})},
+  shorttitle = {Idiap/{{HMMGradients}}.Jl},
+  author = {Antonello, Niccolò},
+  date = {2021-06-07},
+  doi = {10.5281/zenodo.4454565},
+  url = {https://doi.org/10.5281/zenodo.4454565},
+  urldate = {2023-09-12},
+  organization = {{Zenodo}},
+  keywords = {hmm},
+  file = {/home/gdalle/snap/zotero-snap/common/Zotero/storage/PEFYSLF7/4906644.html}
+}
+
 @inproceedings{bengioInputOutputHMM1994,
   title = {An {{Input Output HMM Architecture}}},
   booktitle = {Advances in {{Neural Information Processing Systems}}},
@@ -84,6 +97,17 @@ @thesis{dalleMachineLearningCombinatorial2022
   file = {/home/gdalle/snap/zotero-snap/common/Zotero/storage/CEVJMUP4/Dalle - Machine learning and combinatorial optimization al.pdf}
 }
 
+@software{hmmlearnHmmlearnHiddenMarkov2023,
+  title = {Hmmlearn: {{Hidden Markov Models}} in {{Python}}, with Scikit-Learn like {{API}}},
+  author = {{hmmlearn}},
+  date = {2023},
+  url = {https://github.com/hmmlearn/hmmlearn},
+  urldate = {2023-09-12},
+  abstract = {Hidden Markov Models in Python, with scikit-learn like API},
+  organization = {{hmmlearn}},
+  keywords = {hmm}
+}
+
 @software{mouchetHMMBaseJlHidden2023,
   title = {{{HMMBase}}.Jl:  {{Hidden Markov Models}} for {{Julia}}},
   author = {Mouchet, Maxime},
@@ -171,6 +195,22 @@ @software{rowleyLogarithmicNumbersJlLogarithmic2023
   keywords = {hmm}
 }
 
+@article{schreiberPomegranateFastFlexible2018,
+  title = {Pomegranate: {{Fast}} and {{Flexible Probabilistic Modeling}} in {{Python}}},
+  shorttitle = {Pomegranate},
+  author = {Schreiber, Jacob},
+  date = {2018},
+  journaltitle = {Journal of Machine Learning Research},
+  volume = {18},
+  number = {164},
+  pages = {1--6},
+  issn = {1533-7928},
+  url = {http://jmlr.org/papers/v18/17-636.html},
+  urldate = {2019-05-16},
+  keywords = {hmm},
+  file = {/home/gdalle/snap/zotero-snap/common/Zotero/storage/QKMY8X8M/Schreiber_2018_pomegranate.pdf;/home/gdalle/snap/zotero-snap/common/Zotero/storage/9GWGT5RK/17-636.html}
+}
+
 @software{whiteJuliaDiffChainRulesJl2022,
   title = {{{JuliaDiff}}/{{ChainRules}}{{.jl}}: V1.44.7},
   shorttitle = {{{JuliaDiff}}/{{ChainRules}}.Jl},

diff --git a/paper/paper.md b/paper/paper.md
@@ -34,12 +34,12 @@ Given a sequence of observations and a parametric family of HMMs $\mathbb{P}_\th
 In generic graphical models, these problems are often intractable, but HMMs have a tree-like structure that yields exact solution procedures with polynomial complexity.
 The package `HiddenMarkovModels.jl` leverages the Julia language [@bezansonJuliaFreshApproach2017] to implement those algorithms in a _generic_, _fast_ and _reliable_ way.
 
-| Problem                         | Formula                                                                       | Algorithm        |
-| ------------------------------- | ----------------------------------------------------------------------------- | ---------------- |
-| Best state sequence             | $\mathrm{argmax}_{X_{1:T}}~\mathbb{P}_\theta(X_{1:T} \vert Y_{1:T}, U_{1:T})$ | Viterbi          |
-| Observation sequence likelihood | $\mathbb{P}_\theta(Y_{1:T} \vert U_{1:T})$                                    | Forward          |
-| State marginals                 | $\mathbb{P}_\theta(X_t \vert Y_{1:T}, U_{1:T})$                               | Forward-backward |
-| Maximum likelihood parameter    | $\mathrm{argmax}_\theta~\mathbb{P}_\theta(Y_{1:T} \vert U_{1:T})$             | Baum-Welch       |
+| Inference problem                                                                                              | Algorithm        |
+| -------------------------------------------------------------------------------------------------------------- | ---------------- |
+| Best state sequence              $\mathrm{argmax}_{X_{1:T}}~\mathbb{P}_\theta(X_{1:T} \vert Y_{1:T}, U_{1:T})$ | Viterbi          |
+| Observation sequence likelihood  $\mathbb{P}_\theta(Y_{1:T} \vert U_{1:T})$                                    | Forward          |
+| State marginals                  $\mathbb{P}_\theta(X_t \vert Y_{1:T}, U_{1:T})$                               | Forward-backward |
+| Maximum likelihood parameter     $\mathrm{argmax}_\theta~\mathbb{P}_\theta(Y_{1:T} \vert U_{1:T})$             | Baum-Welch       |
 
 # Statement of need
 
@@ -77,6 +77,7 @@ Model use is just as intuitive:
 state_seq, obs_seq = rand(hmm, 100)
 logdensityof(hmm, obs_seq)
 forward_backward(hmm, obs_seq)
+baum_welch(hmm, obs_seq)
 ```
 
 The extendable `AbstractHMM` interface allows incorporating features such as priors or structured transition matrices, as well as control variables, simply by defining three methods:
@@ -99,7 +100,7 @@ Special care was given to code quality, type stability, and compatibility checks
 
 However, it is also _limited in scope_.
 It centers around CPU efficiency, and remains untested on GPU.
-Furthermore, it does not perform probability computations in the logarithmic domain, but instead uses the scaling trick from @rabinerTutorialHiddenMarkov1989 with a variation borrowed from `HMMBase.jl`.
+Furthermore, it does not perform probability computations in the logarithmic domain, but instead uses the scaling trick [@rabinerTutorialHiddenMarkov1989] with a variation borrowed from `HMMBase.jl`.
 Thus, its numerical stability might be worse than that of Python counterparts on challenging instances.
 Luckily, thanks to unrestricted number types, users are free to bring in third-party packages like `LogarithmicNumbers.jl` [@rowleyLogarithmicNumbersJlLogarithmic2023] to recover additional precision.