Call for Abstracts
BayLearn 2024 will be an in-person event hosted at Cupertino, CA.
The BayLearn 2024 abstract submission site is now open for submissions:
https://baylearn.org/submissions
The abstract submission deadline has been extended to Aug 5th, 2024 11:59pm PDT.
Please submit abstracts as a 2-page PDF in NeurIPS format. An extra page for acknowledgements and references is allowed.
About BayLearn
The BayLearn Symposium is an annual gathering of machine learning researchers and scientists from the San Francisco Bay Area. While BayLearn promotes community building and technical discussions between local researchers from academic and industrial institutions, it also welcomes visitors. This one-day event combines invited talks, contributed talks, and posters, to foster exchange of ideas.
Meet with fellow Bay Area machine learning researchers and scientists during the symposium that will be held on Thursday, October 10th, 2024
Feel free to circulate this invitation to your colleagues and relevant contacts.
Key Dates
- Abstract submission deadline--Extended...Now: Aug 5th, 2024 11:59pm PDT
- Acceptance notifications: Sep 9th, 2024
- BayLearn 2024 Symposium: Thursday, October 10th, 2024
We are planning for BayLearn 2024 to be a purely in-person (NOT hybrid) event at Cupertino. Details to be announced.
Submissions
We encourage submission of abstracts. Acceptable material includes work which has already been submitted or published, preliminary results, and controversial findings. We do not intend to publish paper proceedings; only abstracts will be shared through an online repository. Our primary goal is to foster discussion! For examples of previously accepted talks, please watch the paper presentations from previous BayLearn Symposiums: https://baylearn.org/previous
For more information about submissions, please look here:
https://baylearn.org/submissions
Submit your abstracts via CMT:
https://cmt3.research.microsoft.com/BAYLEARN2024
Please use the NeurIPS submission format: https://neurips.cc/Conferences/2023/PaperInformation/StyleFiles