Summary of Deepcover: Advancing Rnn Test Coverage and Online Error Prediction Using State Machine Extraction, by Pouria Golshanrad and Fathiyeh Faghih
DeepCover: Advancing RNN Test Coverage and Online Error Prediction using State Machine Extraction
by Pouria Golshanrad, Fathiyeh Faghih
First submitted to arxiv on: 10 Feb 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 RNNs have become essential tools in processing sequential data, but their lack of explainability hinders understanding of their internal workings. This paper proposes a method to extract a state machine from an RNN-based model, providing insights into its internal function. The algorithm was evaluated using four newly proposed metrics: Purity, Richness, Goodness, and Scale. This methodology, along with the assessment metrics, increases explainability in RNN models by representing their internal decision-making process through the extracted SM. Additionally, the extracted SM can be used to advance testing and monitoring of primary RNN-based models. The paper also introduces six model coverage criteria based on the extracted SM as metrics for evaluating test suites designed to analyze the primary model. Furthermore, a tree-based model is proposed to predict the error probability of the primary model for each input based on the extracted SM, achieving an AUC exceeding 80% using the MNIST and Mini Speech Commands datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RNNs are powerful tools that help us understand speech and language. But have you ever wondered how they make decisions? This paper helps solve this mystery by creating a simple diagram of an RNN’s inner workings. It also develops new ways to test and improve the accuracy of these models. The results show that this approach can accurately predict when an RNN might make a mistake, which is important for building reliable language systems. |
Keywords
* Artificial intelligence * Auc * Probability * Rnn
