Speaker
Description
Large-scale public datasets have been always a key to accelerate research and enable new discoveries in the machine learning research community. We propose to build a public dataset repository for the experimental neutrino physics community, which will be a new machine learning research hub that connects researchers from multiple domains including neutrino physics, machine learning, and more. Furthermore, the public data repository will make our research more accessible and transparent. However, there are also challenges. Along with each dataset, science challenges must be described with clarity with clearly defined performance metrics that drives R&D of new techniques. Moreover, those datasets must be versioned and maintained over time, which takes human effort on top of the technical needs such as a large-scale storage space. In this talk, we discuss these challenges and ask for the community inputs in order to make this repository serve the best for experimental neutrino physics.
| Type of contribution | Talk: 15 minutes. |
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