Orateur
Description
The adoption of deep learning and machine learning techniques in the scientific community has become increasingly widespread. Sharing knowledge and code on approaches and architectures is already become practice, but this could be improved by creating common resources that require limited effort but may have a high impact on the field, by both enabling new science and ensuring prior work can be reproduced. This can span a range of possibilities, starting from well-maintained collections of recent work and related code, to the collaboration on specific modelling or infrastructural building blocks; from the identification of common themes, such as multi-task learning, simulated-to-real domain adaptation, modelling distributions from data, uncertainty and drift detection, all the way to finding common ground towards multi-messenger astronomy.
