Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI)
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

Machine learning systems deployed in the wild are often trained on a source distribution but
deployed on a different target distribution. Unlabeled data can be a powerful point of …

Deep learning is driving recent advances behind many everyday technologies, including
speech and image recognition, natural language processing and autonomous driving. It is …

Animal behaviour is remarkably sensitive to disruption by chemical pollution, with
widespread implications for ecological and evolutionary processes in contaminated wildlife …

Camera traps enable the automatic collection of large quantities of image data. Ecologists
use camera traps to monitor animal populations all over the world. In order to estimate the …

Images of the natural world collected by a variety of cameras from drones to individual
phones are increasingly abundant sources of biological information. There is an explosion …

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor
an ecosystems animal population. However, manual inspection of the images produced is …

Generative AI models, including large language models and multimodal models that include
text and other media, are on the cusp of transforming many aspects of modern life, including …