MOE, standing for 'Mixture of Experts', is a machine learning architecture where multiple specialized neural networks (the 'experts') are trained to handle different aspects of a complex problem. A 'gating network' then learns to route each input to the most relevant experts, combining their outputs to produce a final result. This approach allows for increased model capacity and specialization, leading to improved performance on complex tasks, particularly in areas like natural language processing and computer vision. MOE is commonly used to scale models efficiently by activating only a subset of the network for each input, reducing computational cost.
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