Enhancing Machine Learning: striking a balance between imitation and trial-and-error
Researchers are working on a more effective way to train machines for uncertain, real-world situations. A new algorithm will decide when a “student” machine should follow its teacher, and when it should learn on its own.
Researchers are working on a more effective way to train machines for uncertain, real-world situations. A new algorithm will decide when a “student” machine should follow its teacher, and when it should learn on its own.
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