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{{About|meta learning in machine learning|meta learning in social psychology|Meta learning|metalearning in neuroscience|Metalearning (neuroscience)}}
{{More citations needed|date=August 2010}}
'''Meta learning'''<ref name="sch1987">{{cite journal | last1 = Schmidhuber | first1 = Jürgen | year = 1987| title = Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook | url= http://people.idsia.ch/~juergen/diploma1987ocr.pdf | journal = Diploma Thesis, Tech. Univ. Munich}}</ref><ref name="scholarpedia">{{cite journal | last1 = Schaul | first1 = Tom | last2 = Schmidhuber | first2 = Jürgen | year = 2010| title = Metalearning
is a subfield of [[machine learning]] where automatic learning algorithms are applied to [[meta-data|metadata]] about machine learning experiments. As of 2017 the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing [[learning algorithms]] or to learn (induce) the learning algorithm itself, hence the alternative term '''learning to learn'''.<ref name="sch1987" />
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''Bias'' refers to the assumptions that influence the choice of explanatory hypotheses<ref>{{Cite book|title=Metalearning - Springer|doi=10.1007/978-3-540-73263-1|series = Cognitive Technologies|year = 2009|isbn = 978-3-540-73262-4|last1 = Brazdil|first1 = Pavel|last2=Carrier|first2=Christophe Giraud|last3=Soares|first3=Carlos|last4=Vilalta|first4=Ricardo}}</ref> and not the notion of bias represented in the [[bias-variance dilemma]]. Meta learning is concerned with two aspects of learning bias.
* Declarative bias specifies the representation of the space of hypotheses, and affects the size of the search space (e.g., represent hypotheses using linear functions only).
* Procedural bias imposes constraints on the ordering of the inductive hypotheses (e.g., preferring smaller hypotheses). <ref>{{cite journal |last1=Gordon |first1=Diana |last2=Desjardins |first2=Marie |title=Evaluation and Selection of Biases in Machine Learning |journal=Machine Learning |date=1995 |volume=20 |pages=5–22 |doi=10.1023/A:1022630017346 |url=https://link.springer.com/content/pdf/10.1023/A:1022630017346.pdf |
==Common approaches==
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Some approaches which have been viewed as instances of meta learning:
* [[Recurrent neural networks]] (RNNs) are universal computers. In 1993, [[Jürgen Schmidhuber]] showed how "self-referential" RNNs can in principle learn by [[backpropagation]] to run their own weight change algorithm, which may be quite different from backpropagation.<ref name="sch1993">{{cite journal | last1 = Schmidhuber | first1 = Jürgen | year = 1993| title = A self-referential weight matrix
* In the 1990s, Meta [[Reinforcement Learning]] or Meta RL was achieved in Schmidhuber's research group through self-modifying policies written in a universal programming language that contains special instructions for changing the policy itself. There is a single lifelong trial. The goal of the RL agent is to maximize reward. It learns to accelerate reward intake by continually improving its own learning algorithm which is part of the "self-referential" policy.<ref name="sch1994">{{cite journal | last1 = Schmidhuber | first1 = Jürgen | year = 1994| title = On learning how to learn learning strategies
* An extreme type of Meta [[Reinforcement Learning]] is embodied by the [[Gödel machine]], a theoretical construct which can inspect and modify any part of its own software which also contains a general [[Automated theorem proving|theorem prover]]. It can achieve [[recursive self-improvement]] in a provably optimal way.<ref name="goedelmachine">{{cite journal | last1 = Schmidhuber | first1 = Jürgen | year = 2006| title = Gödel machines: Fully Self-Referential Optimal Universal Self-Improvers | url=https://archive.org/details/arxiv-cs0309048| journal = In B. Goertzel & C. Pennachin, Eds.: Artificial General Intelligence | pages = 199–226}}</ref><ref name="scholarpedia" />
* ''Model-Agnostic Meta-Learning'' (MAML) was introduced in 2017 by Chelsea Finn et al.<ref name="maml" /> Given a sequence of tasks, the parameters of a given model are trained such that few iterations of gradient descent with few training data from a new task will lead to good generalization performance on that task. MAML "trains the model to be easy to fine-tune."<ref name="maml" /> MAML was successfully applied to few-shot image classification benchmarks and to policy gradient-based reinforcement learning.<ref name="maml">{{cite arxiv | last1 = Finn | first1 = Chelsea | last2 = Abbeel | first2 = Pieter | last3 = Levine | first3 = Sergey |year = 2017| title = Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | eprint=1703.03400|class=cs.LG }}</ref>
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* ''[[Inductive transfer]]'' studies how the learning process can be improved over time. Metadata consists of knowledge about previous learning episodes and is used to efficiently develop an effective hypothesis for a new task. A related approach is called [[learning to learn]], in which the goal is to use acquired knowledge from one ___domain to help learning in other domains.
* Other approaches using metadata to improve automatic learning are [[learning classifier system]]s, [[case-based reasoning]] and [[constraint satisfaction]].
* Some initial, theoretical work has been initiated to use ''[[Applied Behavioral Analysis]]'' as a foundation for agent-mediated meta-learning about the performances of human learners, and adjust the instructional course of an artificial agent.<ref name="Begoli, PRS-ABA, ABA Ontology">{{cite book|last1=Begoli|first1=Edmon|title=Procedural-Reasoning Architecture for Applied Behavior Analysis-based Instructions|date=May 2014|publisher=University of Tennessee, Knoxville|___location=Knoxville, Tennessee, USA|pages=44–79|url=http://trace.tennessee.edu/utk_graddiss/2749|
* [[AutoML]] such as Google Brain's "AI building AI" project, which according to Google briefly exceeded existing [[ImageNet]] benchmarks in 2017.<ref>{{cite news|title=Robots Are Now 'Creating New Robots,' Tech Reporter Says|url=https://www.npr.org/2018/03/15/593863645/robots-are-now-creating-new-robots-tech-reporter-says|
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