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arXiv AI By Jake Williams, Abel Tadesse, Tyler Sam, Huey Sun, George D. Montanez

Limits of Transfer Learning

Read the original on arXiv AI →

The paper investigates the theoretical limits of transfer learning, demonstrating that careful selection of transferable information and its dependence on target problems is crucial. It establishes that the degree of probabilistic change in a transfer-learning algorithm imposes an upper bound on achievable improvement. These findings extend the algorithmic search framework to a broad class of learning tasks involving transfer.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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