Summary of On Sensitivity Of Learning with Limited Labelled Data to the Effects Of Randomness: Impact Of Interactions and Systematic Choices, by Branislav Pecher et al.
On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic Choices
by Branislav Pecher, Ivan Srba, Maria Bielikova
First submitted to arxiv on: 20 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 The proposed method investigates the effects of randomness factors in learning with limited labelled data. The approach mitigates the effects of other factors and observes performance variation across multiple runs to measure true effects. The study applies this method to text classification tasks and meta-learning, showing that disregarding interactions between randomness factors can lead to inconsistent findings. Additionally, the research highlights the importance of considering systematic choices such as number of classes and samples per class in understanding the effects of randomness factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers learn with limited information. Sometimes, things happen randomly while they’re learning that can affect their performance. The researchers developed a way to study these random events and understand how they impact what the computer learns. They tested this method on different tasks, like classifying text, and found that ignoring these random events can give misleading results. |
Keywords
* Artificial intelligence * Meta learning * Text classification
