7 citations · 9 across the 4 of their papers we have counts for
5 papers
Targeted Review for AI-Assisted Biodiversity Surveys: Active Continuous-Score Occupancy Modeling
Timm Haucke, Lauren Harrell, Justin Kay +2
We increasingly use machine learning to label scientific datasets. The models we develop and deploy are improving all the time, but they are not and will likely never be perfect. M…
Perch 2.0 transfers 'whale' to underwater tasks
Andrea Burns, Lauren Harrell, Bart van Merriënboer +3
Perch 2.0 is a supervised bioacoustics foundation model pretrained on 14,597 species, including birds, mammals, amphibians, and insects, and has state-of-the-art performance on mul…
Perch 2.0: The Bittern Lesson for Bioacoustics
Bart van Merriënboer, Vincent Dumoulin, Jenny Hamer +3
Perch is a performant pre-trained model for bioacoustics. It was trained in supervised fashion, providing both off-the-shelf classification scores for thousands of vocalizing speci…
The Search for Squawk: Agile Modeling in Bioacoustics
Vincent Dumoulin, Otilia Stretcu, Jenny Hamer +16
Passive acoustic monitoring (PAM) has shown great promise in helping ecologists understand the health of animal populations and ecosystems. However, extracting insights from millio…
Heterogeneous graph neural networks for species distribution modeling
Lauren Harrell, Christine Kaeser-Chen, Burcu Karagol Ayan +7
Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We i…