Summary of Oris: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling For Robust Streaming Analytics System, by Rahul Pandey et al.
ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System
by Rahul Pandey, Ziwei Zhu, Hemant Purohit
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach to online active learning is proposed, which aims to improve streaming analytics systems by minimizing human errors in labeling. The method, called ORIS, uses a Deep Q-Network-based strategy to sample incoming documents that enhance the machine learning (ML) model performance. This approach involves iteratively selecting a small set of informative documents for labeling, and it outperforms traditional baselines on emotion recognition tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help machines learn from people is developed. It’s called ORIS, and it helps reduce mistakes when humans label information for machine learning models. The goal is to make the process more efficient and accurate. This approach uses a special kind of computer program that decides which documents are most important to label, so people only have to work on the most helpful ones. |
Keywords
» Artificial intelligence » Active learning » Machine learning
