Summary of Adaptive Scheduling For Adaptive Sampling in Pos Taggers Construction, by Manuel Vilares Ferro et al.
Adaptive scheduling for adaptive sampling in POS taggers construction
by Manuel Vilares Ferro, Victor M. Darriba Bilbao, Jesús Vilares Ferro
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 an innovative adaptive scheduling approach for machine learning-based part-of-speech taggers. The goal is to accelerate training on large datasets without sacrificing performance. Unlike previous methods using fixed or random spacing between instances, this algorithm analyzes the learning curve’s shape and adjusts the spacing in real-time based on a functional model. The results show that this method is formally correct and can be used to improve the robustness of sampling by focusing on regions with temporary performance inflation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for machines to learn about language. They want to make it faster without losing accuracy. Usually, computers take data in fixed or random chunks, but this method looks at how well they’re learning and adjusts the chunk size accordingly. It works by using a mathematical model that helps decide when to speed up or slow down learning. The results show that this approach is reliable and can help prevent machines from getting stuck in a pattern too early. |
Keywords
* Artificial intelligence * Machine learning
