Activation Functions for "A Feedforward Unitary Equivariant Neural Network"
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917844711636992 |
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| author | Ma, Pui-Wai |
| author_facet | Ma, Pui-Wai |
| contents | In our previous work [Ma and Chan (2023)], we presented a feedforward unitary equivariant neural network. We proposed three distinct activation functions tailored for this network: a softsign function with a small residue, an identity function, and a Leaky ReLU function. While these functions demonstrated the desired equivariance properties, they limited the neural network's architecture. This short paper generalises these activation functions to a single functional form. This functional form represents a broad class of functions, maintains unitary equivariance, and offers greater flexibility for the design of equivariant neural networks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_14462 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Activation Functions for "A Feedforward Unitary Equivariant Neural Network" Ma, Pui-Wai Machine Learning Neural and Evolutionary Computing In our previous work [Ma and Chan (2023)], we presented a feedforward unitary equivariant neural network. We proposed three distinct activation functions tailored for this network: a softsign function with a small residue, an identity function, and a Leaky ReLU function. While these functions demonstrated the desired equivariance properties, they limited the neural network's architecture. This short paper generalises these activation functions to a single functional form. This functional form represents a broad class of functions, maintains unitary equivariance, and offers greater flexibility for the design of equivariant neural networks. |
| title | Activation Functions for "A Feedforward Unitary Equivariant Neural Network" |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2411.14462 |