Abstract Machine Learning and AI have the potential to transform data-driven scientific
discovery, enabling accurate predictions for several scientific phenomena. As many …

Quantification of behavior is critical in diverse applications from neuroscience, veterinary
medicine to animal conservation. A common key step for behavioral analysis is first …

Understanding the behavior of non-human primates is crucial for improving animal welfare,
modeling social behavior, and gaining insights into distinctively human and phylogenetically …

Natural behavior is hierarchical. Yet, there is a paucity of benchmarks addressing this
aspect. Recognizing the scarcity of large-scale hierarchical behavioral benchmarks, we …

Self-supervised learning (SSL) is a machine learning approach where the data itself
provides supervision, eliminating the need for external labels. The model is forced to learn …

Advances in computer vision as well as increasingly widespread video-based behavioral
monitoring have great potential for transforming how we study animal cognition and …

The study of social interactions and collective behaviors through multi-agent video analysis
is crucial in biology. While self-supervised keypoint discovery has emerged as a promising …

Behavioural experiments often happen in specialised arenas, but this may confound the
analysis. To address this issue, we provide tools to study mice in the home-cage …

Neuroscience as a field is collecting more data than at any other time in history. The scale of
this data allows us to ask fundamental questions about the mechanisms of brain function …

Recent advances in computer vision have enabled the development of automated animal
behavior observation tools. Despite their generally encouraging performance, multi-animal …