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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2211.16782 |
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| _version_ | 1866918114592030720 |
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| author | Cabrera, Guillermo Hong, Sungwook E. Nakazono, Lilianne Parkinson, David Ting, Yuan-Sen |
| author_facet | Cabrera, Guillermo Hong, Sungwook E. Nakazono, Lilianne Parkinson, David Ting, Yuan-Sen |
| contents | Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may move forward. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_16782 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Panel Discussion: Practical Problem Solving for Machine Learning Cabrera, Guillermo Hong, Sungwook E. Nakazono, Lilianne Parkinson, David Ting, Yuan-Sen Instrumentation and Methods for Astrophysics Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may move forward. |
| title | Panel Discussion: Practical Problem Solving for Machine Learning |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2211.16782 |