Code of Conduct



We are a community that fosters knowledge transfer of accelerated and real-time artificial intelligence applications to fundamental science. By joining the community, you agree to abide by our codes of conduct and policies of collaboration, which are detailed below.

Credits: We adapted our terms of collaboration from the Deep Skies Lab

Conduct

We strive for and require the respectful treatment of all of our colleagues. Therefore, we do not tolerate any kind of bullying or harassment in our community. This is a community dedicated to inclusive behavior, which acknowledges the need, as well as advocates for, the equitable treatment of all members of our community. This includes all axes of human identity related to age, race, ethnicity, nationality, gender, sexuality, physical ability/accessibility. It also includes equal respect and equitable support for early-career participants.

Please contact community contacts if issues arise: Javier Duarte, Jen Ngadiuaba, Nhan Tran

If you are interested in being a part of the community contact us, and fill out this form .

Project Activity

  • Those who initiate and manage projects have final say regarding who they would like to participate and in which capacity.
  • Projects may be private or public.
  • To search for a project, feel free to introduce yourself in the #general channel and ask if anyone is interested in collaboration.

Authorship

We are a community committed to fostering open lines of communication between scientists, researchers, deep learning enthusiasts and experts. Because of this, we ask all members to be generous with authorship on projects that come out of the community.

  • If someone has provided code or text for a publication, offer them an opt-in opportunity to co-author the work.
  • If someone‚Äôs ideas and feedback have been instrumental to your results, we require that you include them in either co-authorship or acknowledgements.

Acknowledgments

We request that you acknowledge the Fast Machine Learning collaboration if you benefited from participation in our community. Please use the following text for this acknowledgment: We acknowledge the Fast Machine Learning collective as an open community of multi-domain experts and collaborators. This community was important for the development of this project.