Summary of Extracting Explanations, Justification, and Uncertainty From Black-box Deep Neural Networks, by Paul Ardis et al.
Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks
by Paul Ardis, Arjuna Flenner
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Deep Neural Networks (DNNs) lack inherent task confidence, making understanding their reasoning and supporting evidence crucial in mission-critical applications. This paper proposes a novel Bayesian approach to extract explanations, justifications, and uncertainty estimates from DNNs, which is efficient in terms of memory and computation, applicable to any black box DNN without retraining, including anomaly detection and out-of-distribution detection tasks. The proposed method is validated on the CIFAR-10 dataset, showing significant improvements in interpretability and reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how Deep Neural Networks (DNNs) think about a task and why they’re confident or not. Right now, DNNs don’t give us good reasons for their decisions, which is important when we need them to make life-or-death choices. The researchers came up with a new way to get DNNs to explain themselves using Bayesian methods. This approach works fast, uses little memory, and can be used on any existing DNN without needing to retrain it. They tested this method on a popular dataset called CIFAR-10 and showed that it makes the DNNs much more understandable and reliable. |
Keywords
* Artificial intelligence * Anomaly detection
