Open Problem: Active Representation Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929578334748672 |
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| author | Milosevic, Nikola Müller, Gesine Huisken, Jan Scherf, Nico |
| author_facet | Milosevic, Nikola Müller, Gesine Huisken, Jan Scherf, Nico |
| contents | In this work, we introduce the concept of Active Representation Learning, a novel class of problems that intertwines exploration and representation learning within partially observable environments. We extend ideas from Active Simultaneous Localization and Mapping (active SLAM), and translate them to scientific discovery problems, exemplified by adaptive microscopy. We explore the need for a framework that derives exploration skills from representations that are in some sense actionable, aiming to enhance the efficiency and effectiveness of data collection and model building in the natural sciences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_03845 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Open Problem: Active Representation Learning Milosevic, Nikola Müller, Gesine Huisken, Jan Scherf, Nico Machine Learning Robotics Systems and Control In this work, we introduce the concept of Active Representation Learning, a novel class of problems that intertwines exploration and representation learning within partially observable environments. We extend ideas from Active Simultaneous Localization and Mapping (active SLAM), and translate them to scientific discovery problems, exemplified by adaptive microscopy. We explore the need for a framework that derives exploration skills from representations that are in some sense actionable, aiming to enhance the efficiency and effectiveness of data collection and model building in the natural sciences. |
| title | Open Problem: Active Representation Learning |
| topic | Machine Learning Robotics Systems and Control |
| url | https://arxiv.org/abs/2406.03845 |