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http://hdl.handle.net/10553/56249
Title: | Modularity as a means for complexity management in neural networks learning | Authors: | Castillo Bolado, David Alejandro Guerra, Cayetano Hernández Tejera, Mario |
UNESCO Clasification: | 120304 Inteligencia artificial | Issue Date: | 2019 | Journal: | CEUR Workshop Proceedings | Conference: | 2019 AAAI Spring Symposium on Combining Machine Learning with Knowledge Engineering, AAAI-MAKE 2019 | Abstract: | Training a Neural Network (NN) with lots of parameters orintricate architectures creates undesired phenomena that com-plicate the optimization process. To address this issue we pro-pose a first modular approach to NN design, wherein the NNis decomposed into a control module and several functionalmodules, implementing primitive operations. We illustratethe modular concept by comparing performances between amonolithic and a modular NN on a list sorting problem andshow the benefits in terms of training speed, training stabil-ity and maintainability. We also discuss some questions thatarise in modular NNs. | URI: | http://hdl.handle.net/10553/56249 | ISSN: | 1613-0073 | Source: | CEUR Workshop Proceedings [ISSN 1613-0073], v. 2350, (Abril 2019) |
Appears in Collections: | Actas de congresos |
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