Summary of Dataset Difficulty and the Role Of Inductive Bias, by Devin Kwok et al.
Dataset Difficulty and the Role of Inductive Bias
by Devin Kwok, Nikhil Anand, Jonathan Frankle, Gintare Karolina Dziugaite, David Rolnick
First submitted to arxiv on: 3 Jan 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 study on “example difficulty scores” aims to uncover the consistency of rankings between different training runs, scoring methods, and model architectures. The authors systematically compare various formulations of these scores over multiple runs and architectures, revealing that they are noisy across individual runs, strongly correlated with a single notion of difficulty, and can identify sensitive examples that vary in their responsiveness to inductive biases. Building on statistical genetics, the researchers develop a simple method for fingerprinting model architectures using a few sensitive examples. This work provides guidelines for practitioners to maximize score consistency and establishes comprehensive baselines for evaluating scores in future studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models need to learn from data effectively, but which data points are most important? A new study investigates how different methods for scoring individual data points affect the results. By comparing various scoring methods over many runs of a model, researchers found that these scores tend to be noisy, closely related to difficulty, and able to identify special examples that depend on specific model biases. This work helps us understand how to get consistent rankings from different models and scoring methods. |
Keywords
* Artificial intelligence * Machine learning
