Neural Cellular Automata and Deep Equilibrium Models
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913638983401472 |
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| author | Jia, Zhibai |
| author_facet | Jia, Zhibai |
| contents | This essay discusses the connections and differences between two emerging paradigms in deep learning, namely Neural Cellular Automata and Deep Equilibrium Models, and train a simple Deep Equilibrium Convolutional model to demonstrate the inherent similarity of NCA and DEQ based methods. Finally, this essay speculates about ways to combine theoretical and practical aspects of both approaches for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03573 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Cellular Automata and Deep Equilibrium Models Jia, Zhibai Neural and Evolutionary Computing Formal Languages and Automata Theory This essay discusses the connections and differences between two emerging paradigms in deep learning, namely Neural Cellular Automata and Deep Equilibrium Models, and train a simple Deep Equilibrium Convolutional model to demonstrate the inherent similarity of NCA and DEQ based methods. Finally, this essay speculates about ways to combine theoretical and practical aspects of both approaches for future research. |
| title | Neural Cellular Automata and Deep Equilibrium Models |
| topic | Neural and Evolutionary Computing Formal Languages and Automata Theory |
| url | https://arxiv.org/abs/2501.03573 |