Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913497926860800 |
|---|---|
| author | Zhang, Jun-Jie Cheng, Nan Li, Fu-Peng Wang, Xiu-Cheng Chen, Jian-Nan Pang, Long-Gang Meng, Deyu |
| author_facet | Zhang, Jun-Jie Cheng, Nan Li, Fu-Peng Wang, Xiu-Cheng Chen, Jian-Nan Pang, Long-Gang Meng, Deyu |
| contents | Understanding the mechanisms behind neural network optimization is crucial for improving network design and performance. While various optimization techniques have been developed, a comprehensive understanding of the underlying principles that govern these techniques remains elusive. Specifically, the role of symmetry breaking, a fundamental concept in physics, has not been fully explored in neural network optimization. This gap in knowledge limits our ability to design networks that are both efficient and effective. Here, we propose the symmetry breaking hypothesis to elucidate the significance of symmetry breaking in enhancing neural network optimization. We demonstrate that a simple input expansion can significantly improve network performance across various tasks, and we show that this improvement can be attributed to the underlying symmetry breaking mechanism. We further develop a metric to quantify the degree of symmetry breaking in neural networks, providing a practical approach to evaluate and guide network design. Our findings confirm that symmetry breaking is a fundamental principle that underpins various optimization techniques, including dropout, batch normalization, and equivariance. By quantifying the degree of symmetry breaking, our work offers a practical technique for performance enhancement and a metric to guide network design without the need for complete datasets and extensive training processes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06402 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion Zhang, Jun-Jie Cheng, Nan Li, Fu-Peng Wang, Xiu-Cheng Chen, Jian-Nan Pang, Long-Gang Meng, Deyu Machine Learning Artificial Intelligence Mathematical Physics Understanding the mechanisms behind neural network optimization is crucial for improving network design and performance. While various optimization techniques have been developed, a comprehensive understanding of the underlying principles that govern these techniques remains elusive. Specifically, the role of symmetry breaking, a fundamental concept in physics, has not been fully explored in neural network optimization. This gap in knowledge limits our ability to design networks that are both efficient and effective. Here, we propose the symmetry breaking hypothesis to elucidate the significance of symmetry breaking in enhancing neural network optimization. We demonstrate that a simple input expansion can significantly improve network performance across various tasks, and we show that this improvement can be attributed to the underlying symmetry breaking mechanism. We further develop a metric to quantify the degree of symmetry breaking in neural networks, providing a practical approach to evaluate and guide network design. Our findings confirm that symmetry breaking is a fundamental principle that underpins various optimization techniques, including dropout, batch normalization, and equivariance. By quantifying the degree of symmetry breaking, our work offers a practical technique for performance enhancement and a metric to guide network design without the need for complete datasets and extensive training processes. |
| title | Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion |
| topic | Machine Learning Artificial Intelligence Mathematical Physics |
| url | https://arxiv.org/abs/2409.06402 |