June 11, 2024, 4:48 a.m. | Alireza Rafiei, Ronald Moore, Sina Jahromi, Farshid Hajati, Rishikesan Kamaleswaran

cs.LG updates on arXiv.org arxiv.org

arXiv:2308.02877v2 Announce Type: replace
Abstract: As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model's capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization. These unique characteristics position meta-learning as a suitable choice for developing influential solutions in various healthcare contexts, where the available data is often insufficient, and the data collection methodologies …

abstract arxiv capabilities challenges cs.ai cs.lg domain experience healthcare improving knowledge learn machine machine learning meta meta-learning paradigm prior replace samples survey type unique

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