Summary of Complexity Matters: Effective Dimensionality As a Measure For Adversarial Robustness, by David Khachaturov et al.
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness
by David Khachaturov, Robert Mullins
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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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 novel approach to quantifying the robustness of machine learning models by developing a single measure that can be used for model selection, adversarial training, and predicting trends. The authors demonstrate that existing metrics, such as trainable parameters, boundary thickness, and gradient flatness, are insufficient or inadequate proxies for robustness. They aim to fill this gap by introducing a new metric that accurately captures the robustness properties of models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to find one way to measure how well a machine learning model can resist being fooled by bad data. Right now, there’s no single number that can tell us if a model is good at handling tricky inputs. The authors look at some existing ideas and show that they’re not perfect. They want to come up with something better. |
Keywords
* Artificial intelligence * Machine learning
