Summary of Monitizer: Automating Design and Evaluation Of Neural Network Monitors, by Muqsit Azeem and Marta Grobelna and Sudeep Kanav and Jan Kretinsky and Stefanie Mohr and Sabine Rieder
Monitizer: Automating Design and Evaluation of Neural Network Monitors
by Muqsit Azeem, Marta Grobelna, Sudeep Kanav, Jan Kretinsky, Stefanie Mohr, Sabine Rieder
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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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 paper proposes a tool for monitoring neural networks (NNs) and detecting out-of-distribution (OOD) inputs, which is crucial for safety-critical applications. The tool allows users to apply various NN monitors from the literature, optimize hyperparameters, evaluate and compare different approaches, and develop new monitoring techniques. This enables the safe application of NNs in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a tool that helps developers and users of neural network (NN) monitors. It lets you try out different types of monitors on an input NN, adjust settings, test them, and compare results. The goal is to make sure NNs work well even when they’re not shown examples like the ones they were trained with. |
Keywords
» Artificial intelligence » Neural network
