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- W143403533 abstract "The process of training neural networks online for robot control suffers from catastrophic forgetting. Long sequences of repetitive sensory inputs corresponding to slowly changing environmental stimuli tend to dominate the learning process of neural network controllers. In robot control tasks, however, short critical periods of sensory data are often more vital to the learning process. Traditionally, to enhance the importance of these short critical periods of sensory information, sensory data from the required task is collected and balanced before being used as a training set for an offline neural network. Using vector quantization techniques, we have developed a neural network governor that dynamically samples the input data online so as to excise long sequences of repetitive data that would otherwise prevent shorter critical sequences from being properly learned." @default.
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- W143403533 date "2004-05-01" @default.
- W143403533 modified "2023-09-23" @default.
- W143403533 title "Dynamic sampling with governed neural networks" @default.
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