Local proxies for memory-efficient training
Local learning algorithms can reshape resource requirements of artificial intelligence. The memory bottleneck of standard end-to-end backpropagation lies in the need to store all network activations until the loss gradient has been propagated back through. Local learning avoids this, but typically at a cost in accuracy...
Wu-Zi S. Han, Brian A. Yuan, G. Bellec
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