Exploring the Performance of Perforated Backpropagation through Further Experiments
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916770452865024 |
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| author | Brenner, Rorry Davis, Evan Chaudhari, Rushi Morse, Rowan Chen, Jingyao Liu, Xirui You, Zhaoyi Itti, Laurent |
| author_facet | Brenner, Rorry Davis, Evan Chaudhari, Rushi Morse, Rowan Chen, Jingyao Liu, Xirui You, Zhaoyi Itti, Laurent |
| contents | Perforated Backpropagation is a neural network optimization technique based on modern understanding of the computational importance of dendrites within biological neurons. This paper explores further experiments from the original publication, generated from a hackathon held at the Carnegie Mellon Swartz Center in February 2025. Students and local Pittsburgh ML practitioners were brought together to experiment with the Perforated Backpropagation algorithm on the datasets and models which they were using for their projects. Results showed that the system could enhance their projects, with up to 90% model compression without negative impact on accuracy, or up to 16% increased accuracy of their original models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00356 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Performance of Perforated Backpropagation through Further Experiments Brenner, Rorry Davis, Evan Chaudhari, Rushi Morse, Rowan Chen, Jingyao Liu, Xirui You, Zhaoyi Itti, Laurent Machine Learning Artificial Intelligence Perforated Backpropagation is a neural network optimization technique based on modern understanding of the computational importance of dendrites within biological neurons. This paper explores further experiments from the original publication, generated from a hackathon held at the Carnegie Mellon Swartz Center in February 2025. Students and local Pittsburgh ML practitioners were brought together to experiment with the Perforated Backpropagation algorithm on the datasets and models which they were using for their projects. Results showed that the system could enhance their projects, with up to 90% model compression without negative impact on accuracy, or up to 16% increased accuracy of their original models. |
| title | Exploring the Performance of Perforated Backpropagation through Further Experiments |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.00356 |