7 citations · 21 across the 8 of their papers we have counts for
9 papers
MetaPerch: Learning from metadata for bioacoustics foundation models
Mustafa Chasmai, Vincent Dumoulin, Jenny Hamer
Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data. Recent work has shown that supervision…
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…
Capturing Individual Human Preferences with Reward Features
André Barreto, Vincent Dumoulin, Yiran Mao +6
Reinforcement learning from human feedback usually models preferences using a reward function that does not distinguish between people. We argue that this is unlikely to be a good…
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition
Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa +12
We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and initiate discussions on formal and robust…
Data Selection for Transfer Unlearning
Nazanin Mohammadi Sepahvand, Vincent Dumoulin, Eleni Triantafillou +1
As deep learning models are becoming larger and data-hungrier, there are growing ethical, legal and technical concerns over use of data: in practice, agreements on data use may cha…
Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Ben Williams, Bart van Merriënboer, Vincent Dumoulin +12
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's effic…
BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics
Jenny Hamer, Eleni Triantafillou, Bart van Merriënboer +4
The ability for a machine learning model to cope with differences in training and deployment conditions--e.g. in the presence of distribution shift or the generalization to new cla…