Summary of Impact Of White Noise in Artificial Neural Networks Trained For Classification: Performance and Noise Mitigation Strategies, by Nadezhda Semenova and Daniel Brunner
Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies
by Nadezhda Semenova, Daniel Brunner
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET)
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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 A recent surge in hardware implementation of neural networks using physical coupling and analog neurons has led to significant speed and energy efficiency advantages. However, this non-digital approach may be more susceptible to internal noise compared to digital emulations. This paper investigates the effects of additive and multiplicative Gaussian white noise on neuronal-level accuracy when applied for specific tasks, including a softmax function in the readout layer. To mitigate the detrimental impact of noise, several techniques are adapted for classification tasks, which represent a large fraction of neural network computing. The results show that these adjusted concepts are highly effective in reducing the effects of noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neural networks have become super fast and efficient thanks to new hardware technology. But this physical approach can also make them more prone to errors caused by internal noise. In this research, scientists explore how different types of random noise affect the accuracy of these physical neural networks when used for specific tasks like classification. To combat this noise problem, the researchers develop special techniques specifically designed for classification tasks. The findings show that these new methods can significantly reduce the impact of noise on the network’s performance. |
Keywords
» Artificial intelligence » Classification » Neural network » Softmax
