Summary of Accelerated Smoothing: a Scalable Approach to Randomized Smoothing, by Devansh Bhardwaj et al.
Accelerated Smoothing: A Scalable Approach to Randomized Smoothing
by Devansh Bhardwaj, Kshitiz Kaushik, Sarthak Gupta
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper presents a novel approach to certified defense against adversarial attacks using randomized smoothing. The authors improve upon existing methods by replacing Monte Carlo sampling with a trained surrogate neural network, enabling more efficient and scalable robust radius certification. Their proposed method shows remarkable precision in approximating the smoothed classifier and achieves nearly 600 times faster computation time compared to traditional randomized smoothing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at predicting things without getting tricked by bad data. They want to make sure that when a computer makes a prediction, it’s a good one! To do this, they use something called “randomized smoothing”. This helps keep the predictions safe from fake or manipulated information. The problem is that doing this can be very slow and take up lots of computer power. So, the authors came up with a new way to do this that uses special neural networks (like super smart computers) to help speed things up. They tested their method and it works really well! It’s much faster than the old way, which is great because we need fast and accurate predictions in many areas like healthcare, finance, and more. |
Keywords
* Artificial intelligence * Neural network * Precision
