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Prospective coding in deep recurrent networks can boost performance and efficiency, achieving state-of-the-art results with fewer parameters than conventional methods.
Variational learning can tame the inherent chaos of nanoscale devices, paving the way for practical, larger-scale probabilistic computers.
