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Modelling the niches of wild and domesticated Ungulate species using deep learning

https://doi.org/10.1101/744441
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Recurrent neural network based language model
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Population structure and conservation of a high-altitude specialist, the Andean cat Leopardus jacobita
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ACTIVATION FUNCTIONS IN NEURAL NETWORKS
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Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
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A Unified Approach to Interpreting Model Predictions
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Understanding Deep Learning Generalization by Maximum Entropy
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New Theory Cracks Open the Black Box of Deep Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Benthic invertebrates in the Wadden Sea form a stable community characterized by facilitating relationships
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Multispecies deep learning using citizen science data produces more informative plant community models
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Comparison of different models in predicting habitat suitability of rare species in Uzbekistan: 8 rare Tulipa species case-study
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Rank-based deep learning from citizen-science data to model plant communities
inferred← citeshttps://doi.org/10.1101/826347openalex-snapshot
Modeling Abiotic Niches of Crops and Wild Ancestors Using Deep Learning: A Generalized Approach
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Integrating Remote Sensing and Machine Learning for Regional-Scale Habitat Mapping: Advances and future challenges for desert locust monitoring
inferred← citeshttps://doi.org/10.3390/rs12091475openalex-snapshot
Habitat Suitability Estimation Using a Two-Stage Ensemble Approach
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A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks
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