Harnessing the Power of Noise: A Survey of Techniques and Applications
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914968126881792 |
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| author | Abdolazimi, Reyhaneh Jin, Shengmin Varshney, Pramod K. Zafarani, Reza |
| author_facet | Abdolazimi, Reyhaneh Jin, Shengmin Varshney, Pramod K. Zafarani, Reza |
| contents | Noise, traditionally considered a nuisance in computational systems, is reconsidered for its unexpected and counter-intuitive benefits across a wide spectrum of domains, including nonlinear information processing, signal processing, image processing, machine learning, network science, and natural language processing. Through a comprehensive review of both historical and contemporary research, this survey presents a dual perspective on noise, acknowledging its potential to both disrupt and enhance performance. Particularly, we highlight how noise-enhanced training strategies can lead to models that better generalize from noisy data, positioning noise not just as a challenge to overcome but as a strategic tool for improvement. This work calls for a shift in how we perceive noise, proposing that it can be a spark for innovation and advancement in the information era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_06348 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harnessing the Power of Noise: A Survey of Techniques and Applications Abdolazimi, Reyhaneh Jin, Shengmin Varshney, Pramod K. Zafarani, Reza Machine Learning Noise, traditionally considered a nuisance in computational systems, is reconsidered for its unexpected and counter-intuitive benefits across a wide spectrum of domains, including nonlinear information processing, signal processing, image processing, machine learning, network science, and natural language processing. Through a comprehensive review of both historical and contemporary research, this survey presents a dual perspective on noise, acknowledging its potential to both disrupt and enhance performance. Particularly, we highlight how noise-enhanced training strategies can lead to models that better generalize from noisy data, positioning noise not just as a challenge to overcome but as a strategic tool for improvement. This work calls for a shift in how we perceive noise, proposing that it can be a spark for innovation and advancement in the information era. |
| title | Harnessing the Power of Noise: A Survey of Techniques and Applications |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.06348 |