The Mixture of Experts is an ensemble learning technique developed within the field of deep learning. It introduces the idea of training experts (which are other neural networks) on specific subtasks, decomposing a more complex predictive modelling problem.<\/p>\n\n\n\n
Typically, in an ensemble scenario, all models are trained on the same dataset, and their results are combined through majority voting or averaging. In contrast, with the Mixture of Experts approach, each \u201cexpert model\u201d within the ensemble is only trained on a subset of data where it can achieve optimal performance, thus limiting the model\u2019s focus. To decide which expert must process a specific subset of data, an addition model is used, called \u201cgate network\u201d or \u201crouter\u201d.<\/p>\n","protected":false},"excerpt":{"rendered":"
The Mixture of Experts is an ensemble learning technique developed within the field of deep learning. It introduces the idea of training experts (which are…<\/p>\n","protected":false},"author":1,"featured_media":4814,"menu_order":0,"template":"","meta":{"footnotes":""},"class_list":["post-5229","glossary","type-glossary","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\n