Summary of Sensitivity Analysis on Loss Landscape, by Salman Faroz
Sensitivity Analysis On Loss Landscape
by Salman Faroz
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 leverages gradients for sensitivity analysis by utilizing automatic differentiation to compute first, second, and third derivatives of a loss landscape. The authors employ Spearman’s rank correlation coefficient to detect monotonic relationships between variables, but also propose an alternative approach that visualizes non-linear patterns through the incorporation of activation functions in the loss function. The study shows that retraining the model multiple times can provide valuable insights into the impact of independent variables on the dependent variable, with first and third derivatives offering additional information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special math tools to understand how different inputs affect an output. They use a method called automatic differentiation to find the rate at which the output changes when different inputs change. The authors are trying to figure out what makes the most difference in the outcome, and they think that using activation functions in the calculation can help them do this. By training the model multiple times, they can get more insights into how the inputs affect the output. |
Keywords
* Artificial intelligence * Loss function
