Meta-learning
Computer science
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Meta-learning
is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. Wikipedia
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Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments.
Nov 30, 2018 · The adaptation process, essentially a mini learning session, happens during test but with a limited exposure to the new task configurations.
Apr 27, 2021 · Summary · Meta-learning refers to machine learning algorithms that learn from the output of other machine learning algorithms. · Meta-learning ...
Meta-learning is a branch of metacognition concerned with learning about one's own learning and learning processes. The term comes from the meta prefix's ...
Jul 25, 2023 · Meta-learning refers to the ability of “learning to learn”. A more comprehensive definition would describe meta-learning as any system that includes a learning ...
Nov 29, 2023 · Meta-learning is learning to learn algorithms, which aim to create AI systems that can adapt to new tasks and improve their performance over time.
May 30, 2020 · Meta-learning, also known as learning how to learn, has recently emerged as a potential learning paradigm that can learn information from one ...
Sep 12, 2020 · Meta-learning provides an alternative paradigm where a machine learning model gains experience over multiple learning episodes.
Sep 1, 2022 · Meta-learning, described as “learning to learn”, is a subset of machine learning in the field of computer science. It is used to improve the ...