Publications by years in reversed chronological order
Published
2022
Trade-off between deep learning for species
identification and inference about predator-prey
co-occurrence: Reproducible R workflow integrating
models in computer vision and ecological
statistics
Olivier Gimenez,
Maelis Kervellec,
Jean-Baptiste Fanjul,
Anna Chaine,
Lucile Marescot,
Yoann Bollet,
and Christophe Duchamp
Deep learning is used in computer vision problems with
important applications in several scientific
fields. In ecology for example, there is a growing
interest in deep learning for automatizing
repetitive analyses on large amounts of images, such
as animal species identification. However, there
are challenging issues toward the wide adoption of
deep learning by the community of ecologists. First,
there is a programming barrier as most algorithms
are written in Python while most ecologists are
versed in R. Second, recent applications of deep
learning in ecology have focused on computational
aspects and simple tasks without addressing the
underlying ecological questions or carrying out the
statistical data analysis to answer these questions.
Here, we showcase a reproducible R workflow
integrating both deep learning and statistical
models using predator-prey relationships as a case
study. We illustrate deep learning for the
identification of animal species on images collected
with camera traps, and quantify spatial
co-occurrence using multispecies occupancy models.
Despite average model classification performances,
ecological inference was similar whether we analysed
the ground truth dataset or the classified
dataset. This result calls for further work on the
trade-offs between time and resources allocated to
train models with deep learning and our ability to
properly address key ecological questions with
biodiversity monitoring. We hope that our
reproducible workflow will be useful to ecologists
and applied statisticians.
computer vision, deep-learning, species distribution modeling, ecological statistics
@article{gimenez_lynx,bibtex_show={true},author={Gimenez, Olivier and Kervellec, Maelis and Fanjul, Jean-Baptiste and Chaine, Anna and Marescot, Lucile and Bollet, Yoann and Duchamp, Christophe},title={{Trade-off between deep learning for species
identification and inference about predator-prey
co-occurrence: Reproducible R workflow integrating
models in computer vision and ecological
statistics}},journal={Computo},year={2022},doi={10.57750/yfm2-5f45},html={https://computo.sfds.asso.fr/published-202204-deeplearning-occupancy-lynx/},review={https://github.com/computorg/published-202204-deeplearning-occupancy-lynx/issues?q=is%3Aopen+is%3Aissue+label%3Areview},code={https://github.com/computorg/published-202204-deeplearning-occupancy-lynx/},type={{Research article}},language={R},domain={Statistical Ecology},keywords={computer vision, deep-learning, species distribution modeling, ecological statistics},issn={2824-7795}}
Upcoming
In the pipeline
Under review
At the moment, 3 manuscripts are under review.
Example: a mock contribution
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.
Visualizing Data using t-SNE: practical Computo example
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.
template, documentation, quarto, R, python
@article{mock_tsne,bibtex_show={true},author={van der Maaten, Laurens and Hinton, Geoffrey},title={{Visualizing Data using t-SNE: practical Computo example}},journal={Computo},year={2021},volume={0},doi={},html={https://computorg.github.io/published-paper-tsne},code={https://github.com/computorg/published-paper-tsne},data={},review={https://github.com/computorg/published-paper-tsne/issues},type={Template},language={R, Python},domain={Template},keywords={template, documentation, quarto, R, python}}