Laor Initialization: A New Weight Initialization Method for the Backpropagation of Deep Learning
This paper presents Laor Initialization, an innovative weight initialization technique for deep neural networks that utilizes forward-pass error feedback in conjunction with k-means clustering to optimize the initial weights. In contrast to traditional methods, Laor adopts a data-driven approach tha...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI AG
2025-07-01
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| Serie: | Big Data and Cognitive Computing |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/2504-2289/9/7/181 |
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