Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning - Liu Yang from family case study sample Watch Video
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⏲ Duration: 22 min 42 sec ✓ Published: 21-Nov-2011
Description: We explore a transfer learning setting, in which a finite sequence of target concepts are sampled independently with an unknown distribution from a known family. We study the total number of labeled examples required to learn all targets to an arbitrary specified expected accuracy, focusing on the asymptotics in the number of tasks and the desired accuracy. Our primary interest is formally understanding the fundamental benefits of transfer learning, compared to learning each target independently
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