ferté et al published

Author: jchiquet

_bibliography/published.bib

@@ -1,11 +1,50 @@
+@article{ferte2025,
+  bibtex_show = {true},
+  author = {Ferté, Thomas and Ba, Kalidou and Dutartre, Dan and Legrand,
+    Pierrick and Jouhet, Vianney and Thiébaut, Rodolphe and Hinaut,
+    Xavier and P Hejblum, Boris},
+  publisher = {French Statistical Society},
+  title = {Reservoir {Computing} in {R:} A {Tutorial} for {Using}
+    Reservoirnet to {Predict} {Complex} {Time-Series}},
+  journal = {Computo},
+  date = {2025-06-27},
+  doi = {10.57750/arxn-6z34},
+  issn = {2824-7795},
+  langid = {en},
+  abstract = {Reservoir Computing (RC) is a machine learning method
+    based on neural networks that efficiently process information
+    generated by dynamical systems. It has been successful in solving
+    various tasks including time series forecasting, language processing
+    or voice processing. RC is implemented in `Python` and `Julia` but
+    not in `R`. This article introduces `reservoirnet`, an `R` package
+    providing access to the `Python` API `ReservoirPy`, allowing `R`
+    users to harness the power of reservoir computing. This article
+    provides an introduction to the fundamentals of RC and showcases its
+    real-world applicability through three distinct sections. First, we
+    cover the foundational concepts of RC, setting the stage for
+    understanding its capabilities. Next, we delve into the practical
+    usage of `reservoirnet` through two illustrative examples. These
+    examples demonstrate how it can be applied to real-world problems,
+    specifically, regression of COVID-19 hospitalizations and
+    classification of Japanese vowels. Finally, we present a
+    comprehensive analysis of a real-world application of
+    `reservoirnet`, where it was used to forecast COVID-19
+    hospitalizations at Bordeaux University Hospital using public data
+    and electronic health records.}
+  year    = 2025,
+  type        = {{Research article}},
+  domain      = {Machine Learning},
+  language    = {R, Python},
+  repository  = {published-202505-ferte-reservoirnet},
+}
+
 @article{giorgi2024,
   bibtex_show = {true},
   author = {Giorgi, Daphn\'e and Kaakai, Sarah and Lemaire, Vincent},
   publisher = {French Statistical Society},
   title = {Efficient simulation of individual-based population models},
   journal = {Computo},
   year        = 2025,
-  url = {https://computo.sfds.asso.fr/published-202412-giorgi-efficient/},
   doi = {10.57750/sfxn-1t05},
   issn = {2824-7795},
   type        = {{Research article}},
@@ -25,7 +64,6 @@ @article{ambroise2024
   title = {Spectral Bridges: Scalable Spectral Clustering Based on Vector Quantization},
   journal = {Computo},
   year        = 2025,
-  url = {https://computo.sfds.asso.fr/published-202412-ambroise-spectral/},
   doi = {10.57750/1gr8-bk61},
   issn = {2824-7795},
   type        = {{Research article}},
@@ -46,7 +84,6 @@ @article{legrand2024
     Sizes for Projections Under Climate Change},
   journal = {Computo},
   year        = 2024,
-  url = {https://computo.sfds.asso.fr/published-202407-legrand-wildfires/},
   doi = {10.57750/4y84-4t68},
   issn = {2824-7795},
   type        = {{Research article}},
@@ -94,7 +131,6 @@ @article{pishchagina2024
     {Detection} in {Multiple} {Independent} {Time} {Series}},
   journal = {Computo},
   year        = 2024,
-  url = {https://computo.sfds.asso.fr/published-202406-pishchagina-change-point/},
   doi = {10.57750/9vvx-eq57},
   issn = {2824-7795},
   type        = {{Research article}},
@@ -131,7 +167,6 @@ @article{susmann_adaptive
   title       = {{AdaptiveConformal: An R Package for Adaptive Conformal Inference}},
   journal     = {Computo},
   year        = 2024,
-  url         = {https://computo.sfds.asso.fr/published-202407-susmann-adaptive-conformal},
   doi         = {10.57750/edan-5f53},
   type        = {{Research article}},
   domain      = {Statistics},

_layouts/bib.html

@@ -44,9 +44,9 @@
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       {% else %}
       {% if entry.repository %}
-      <a href="https://www.computo-journal.org/{{ entry.repository }}" class="btn btn-sm z-depth-0" role="button"
+      <a href="https://computo-journal.org/{{ entry.repository }}" class="btn btn-sm z-depth-0" role="button"
         target="_blank">HTML</a>
-      <a href="https://www.computo-journal.org/{{ entry.repository }}/{{ entry.repository }}.pdf"
+      <a href="https://computo-journal.org/{{ entry.repository }}/{{ entry.repository }}.pdf"
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@@ -56,7 +56,7 @@
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         class="btn btn-sm z-depth-0" role="button" target="_blank">Review</a>
-      <a href="https://www.computo-journal.org/{{ entry.repository }}/#citation" class="btn btn-sm z-depth-0" role="button"
+      <a href="https://computo-journal.org/{{ entry.repository }}/#citation" class="btn btn-sm z-depth-0" role="button"
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_news/announcement_publication_202506.md

@@ -0,0 +1,8 @@
+---
+layout: post
+date: 2025-06-27 07:59:00-0400
+inline: true
+---
+
+A new article, first of 2025, was published, by
+   Thomas Ferté an co-authors: [Reservoir Computing in R: a Tutorial for Using reservoirnet to Predict Complex Time-Series](http://computo-journal.org/published-202505-ferte-reservoirnet/)