Summary of Open Problem: Active Representation Learning, by Nikola Milosevic et al.
Open Problem: Active Representation Learning
by Nikola Milosevic, Gesine Müller, Jan Huisken, Nico Scherf
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Active Representation Learning (ARL), a new class of problems combining exploration and representation learning within partially observable environments. Building on ideas from Active Simultaneous Localization and Mapping (active SLAM), the authors apply these concepts to scientific discovery problems, specifically adaptive microscopy. The goal is to develop a framework that derives exploration skills from actionable representations, enhancing data collection and model building efficiency in natural sciences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way of learning representations by combining exploration and representation learning together. It’s like using a map to find the best places to take pictures with a microscope! The authors use ideas from another field called active SLAM and apply them to scientific discovery, making it easier and more efficient to collect data and build models. |
Keywords
» Artificial intelligence » Representation learning
