Summary of Neural Networks with (low-precision) Polynomial Approximations: New Insights and Techniques For Accuracy Improvement, by Chi Zhang et al.
Neural Networks with (Low-Precision) Polynomial Approximations: New Insights and Techniques for Accuracy Improvement
by Chi Zhang, Jingjing Fan, Man Ho Au, Siu Ming Yiu
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 presents an investigation into polynomial approximation of neural networks (PANNs), which are often used in privacy-preserving machine learning. By replacing non-polynomial functions with their polynomial approximations, PANNs can enable model inference while maintaining data privacy. The authors show that state-of-the-art PANNs achieve similar inference accuracy to the underlying backbone models using highly precise approximation. However, they also find that PANNs are susceptible to certain perturbations and that weight regularization significantly reduces their accuracy. To address these limitations, the authors propose solutions to increase inference accuracy for PANNs, demonstrating a 10% to 50% improvement in accuracy at the same precision as state-of-the-art methods using the ResNet-20 model on CIFAR-10 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PANNs are a special type of neural network that helps keep data private. They work by replacing some math functions with simpler ones, making it harder to figure out what the original data was. The researchers looked at how well PANNs do this and found some surprising things. For example, they discovered that sometimes these approximations can make the model worse, not better! But they also found ways to fix this problem, making the PANNs much more accurate than before. This is important because it helps keep people’s personal information safe. |
Keywords
* Artificial intelligence * Inference * Machine learning * Neural network * Precision * Regularization * Resnet
