Update publications from getcomputo-pub.fsx [skip ci]

Author: github-actions[bot]

site/mock-papers.yml

@@ -1,69 +1,4 @@
-- abstract'@: >-
-    We present a new technique called “t-SNE” that visualizes
-        high-dimensional data by giving each datapoint a location in a two
-        or three-dimensional map. The technique is a variation of Stochastic
-        Neighbor Embedding {[}@hinton:stochastic{]} that is much easier to
-        optimize, and produces significantly better visualizations by
-        reducing the tendency to crowd points together in the center of the
-        map. t-SNE is better than existing techniques at creating a single
-        map that reveals structure at many different scales. This is
-        particularly important for high-dimensional data that lie on several
-        different, but related, low-dimensional manifolds, such as images of
-        objects from multiple classes seen from multiple viewpoints. For
-        visualizing the structure of very large data sets, we show how t-SNE
-        can use random walks on neighborhood graphs to allow the implicit
-        structure of all the data to influence the way in which a subset of
-        the data is displayed. We illustrate the performance of t-SNE on a
-        wide variety of data sets and compare it with many other
-        non-parametric visualization techniques, including Sammon mapping,
-        Isomap, and Locally Linear Embedding. The visualization produced by
-        t-SNE are significantly better than those produced by other
-        techniques on almost all of the data sets.
-  authors@: Laurens van der Maaten and Geoffrey Hinton
-  bibtex@: >+
-    @article{van_der_maaten2008,
-    	author = {van der Maaten, Laurens and Hinton, Geoffrey},
-    	publisher = {French Statistical Society},
-    	title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
-    	journal = {Computo},
-    	date = {2008-08-11},
-    	doi = {10.57750/xxxxxx},
-    	issn = {2824-7795},
-    	langid = {en},
-    	abstract = {We present a new technique called “t-SNE” that visualizes
-        high-dimensional data by giving each datapoint a location in a two
-        or three-dimensional map. The technique is a variation of Stochastic
-        Neighbor Embedding {[}@hinton:stochastic{]} that is much easier to
-        optimize, and produces significantly better visualizations by
-        reducing the tendency to crowd points together in the center of the
-        map. t-SNE is better than existing techniques at creating a single
-        map that reveals structure at many different scales. This is
-        particularly important for high-dimensional data that lie on several
-        different, but related, low-dimensional manifolds, such as images of
-        objects from multiple classes seen from multiple viewpoints. For
-        visualizing the structure of very large data sets, we show how t-SNE
-        can use random walks on neighborhood graphs to allow the implicit
-        structure of all the data to influence the way in which a subset of
-        the data is displayed. We illustrate the performance of t-SNE on a
-        wide variety of data sets and compare it with many other
-        non-parametric visualization techniques, including Sammon mapping,
-        Isomap, and Locally Linear Embedding. The visualization produced by
-        t-SNE are significantly better than those produced by other
-        techniques on almost all of the data sets.}
-    }
-
-  date@: 2008-08-11
-  description@: >
-    This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.
-  doi@: 10.57750/xxxxxx
-  draft@: false
-  journal@: Computo
-  pdf@: ''
-  repo@: published-paper-tsne
-  title@: Visualizing Data using t-SNE (mock contributon)
-  url@: ''
-  year@: 2008
-  abstract': >-
+- abstract': >-
     We present a new technique called “t-SNE” that visualizes
         high-dimensional data by giving each datapoint a location in a two
         or three-dimensional map. The technique is a variation of Stochastic
@@ -128,72 +63,7 @@
   title: Visualizing Data using t-SNE (mock contributon)
   url: ''
   year: 2008
-- abstract'@: >-
-    We present a new technique called “t-SNE” that visualizes
-        high-dimensional data by giving each datapoint a location in a two
-        or three-dimensional map. The technique is a variation of Stochastic
-        Neighbor Embeddi{[}@hinton:stochastic{]} that is much easier to
-        optimize, and produces significantly better visualizations by
-        reducing the tendency to crowd points together in the center of the
-        map. t-SNE is better than existing techniques at creating a single
-        map that reveals structure at many different scales. This is
-        particularly important for high-dimensional data that lie on several
-        different, but related, low-dimensional manifolds, such as images of
-        objects from multiple classes seen from multiple viewpoints. For
-        visualizing the structure of very large data sets, we show how t-SNE
-        can use random walks on neighborhood graphs to allow the implicit
-        structure of all the data to influence the way in which a subset of
-        the data is displayed. We illustrate the performance of t-SNE on a
-        wide variety of data sets and compare it with many other
-        non-parametric visualization techniques, including Sammon mapping,
-        Isomap, and Locally Linear Embedding. The visualization produced by
-        t-SNE are significantly better than those produced by other
-        techniques on almost all of the data sets.
-  authors@: Laurens van der Maaten and Geoffrey Hinton
-  bibtex@: >+
-    @article{van_der_maaten2008,
-    	author = {van der Maaten, Laurens and Hinton, Geoffrey},
-    	publisher = {French Statistical Society},
-    	title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
-    	journal = {Computo},
-    	date = {2008-08-11},
-    	doi = {10.57750/xxxxxx},
-    	issn = {2824-7795},
-    	langid = {en},
-    	abstract = {We present a new technique called “t-SNE” that visualizes
-        high-dimensional data by giving each datapoint a location in a two
-        or three-dimensional map. The technique is a variation of Stochastic
-        Neighbor Embeddi{[}@hinton:stochastic{]} that is much easier to
-        optimize, and produces significantly better visualizations by
-        reducing the tendency to crowd points together in the center of the
-        map. t-SNE is better than existing techniques at creating a single
-        map that reveals structure at many different scales. This is
-        particularly important for high-dimensional data that lie on several
-        different, but related, low-dimensional manifolds, such as images of
-        objects from multiple classes seen from multiple viewpoints. For
-        visualizing the structure of very large data sets, we show how t-SNE
-        can use random walks on neighborhood graphs to allow the implicit
-        structure of all the data to influence the way in which a subset of
-        the data is displayed. We illustrate the performance of t-SNE on a
-        wide variety of data sets and compare it with many other
-        non-parametric visualization techniques, including Sammon mapping,
-        Isomap, and Locally Linear Embedding. The visualization produced by
-        t-SNE are significantly better than those produced by other
-        techniques on almost all of the data sets.}
-    }
-
-  date@: 2008-08-11
-  description@: >
-    This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.
-  doi@: 10.57750/xxxxxx
-  draft@: false
-  journal@: Computo
-  pdf@: ''
-  repo@: published-paper-tsne-R
-  title@: Visualizing Data using t-SNE (mock contributon)
-  url@: ''
-  year@: 2008
-  abstract': >-
+- abstract': >-
     We present a new technique called “t-SNE” that visualizes
         high-dimensional data by giving each datapoint a location in a two
         or three-dimensional map. The technique is a variation of Stochastic