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Found 2 papers, 1 papers with code
A deep convolutional neural network that is invariant to time rescaling
This property, inspired by findings from contemporary neuroscience and consistent with findings from cognitive psychology, may enable networks that learn with fewer training examples, fewer weights and that generalize more robustly to out of sample data.
Scale-invariant temporal history (SITH): optimal slicing of the past in an uncertain world
In both the human brain and any general artificial intelligence (AI), a representation of the past is necessary to predict the future.
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