Generalized Training for Neural Network Learnability: a Spectral Methods Approach
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911191287201792 |
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| author | Perry, Altai Vuong, Luat |
| author_facet | Perry, Altai Vuong, Luat |
| contents | Hybrid optical neural networks (HONNs) offload some electronic computation to optical preprocessors to achieve low-power and fast training and inference phases in machine learning tasks. Our contribution to the development of HONNs is a spectral-methods paradigm for building synthetic training data for machine-learned models. Here, our synthetic training image data does not resemble the image test data. As a result, the neural network focuses on learning specific features parameterized by the synthetic training data. Within this paradigm, a dataset's singular value decomposition entropy indicates {\it learnability}, i.e., how rapidly a model converges. Subsequently, we train a neural network model to rapidly learn specific features for further downstream analyses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_12172 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Generalized Training for Neural Network Learnability: a Spectral Methods Approach Perry, Altai Vuong, Luat Optics Hybrid optical neural networks (HONNs) offload some electronic computation to optical preprocessors to achieve low-power and fast training and inference phases in machine learning tasks. Our contribution to the development of HONNs is a spectral-methods paradigm for building synthetic training data for machine-learned models. Here, our synthetic training image data does not resemble the image test data. As a result, the neural network focuses on learning specific features parameterized by the synthetic training data. Within this paradigm, a dataset's singular value decomposition entropy indicates {\it learnability}, i.e., how rapidly a model converges. Subsequently, we train a neural network model to rapidly learn specific features for further downstream analyses. |
| title | Generalized Training for Neural Network Learnability: a Spectral Methods Approach |
| topic | Optics |
| url | https://arxiv.org/abs/2304.12172 |