23 citations · 36 across the 14 of their papers we have counts for
4 papers · 1 filter
Applying the maximum entropy principle to neural networks enhances multi-species distribution models
Maxime Ryckewaert, Diego Marcos, Christophe Botella +3
The rapid expansion of citizen science initiatives has led to a significant growth of biodiversity databases, and particularly presence-only (PO) observations. PO data are invaluab…
MALPOLON: A Framework for Deep Species Distribution Modeling
Theo Larcher, Lukas Picek, Benjamin Deneu +3
This paper describes a deep-SDM framework, MALPOLON. Written in Python and built upon the PyTorch library, this framework aims to facilitate training and inferences of deep species…
AI-based Mapping of the Conservation Status of Orchid Assemblages at Global Scale
Joaquim Estopinan, Maximilien Servajean, Pierre Bonnet +2
Although increasing threats on biodiversity are now widely recognised, there are no accurate global maps showing whether and where species assemblages are at risk. We hereby assess…
A two-head loss function for deep Average-K classification
Camille Garcin, Maximilien Servajean, Alexis Joly +1
Average-K classification is an alternative to top-K classification in which the number of labels returned varies with the ambiguity of the input image but must average to K over al…