Meta-learning (computer science): Difference between revisions

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{{short description|Subfield of machine learning}}

{{About|meta -learning in machine learning|meta -learning in social psychology|Meta -learning|metalearning in neuroscience|Metalearning (neuroscience)}}

{{See also|Ensemble learning}}

{{machine learning|Paradigms}}

'''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 | language = en}}</ref><ref name="scholarpedia">{{cite journal | last1 = Schaul | first1 = Tom | last2 = Schmidhuber | first2 = Jürgen | year = 2010| title = Metalearning | journal = Scholarpedia | volume = 5 | issue = 6| page = 4650 | doi=10.4249/scholarpedia.4650| bibcode = 2010SchpJ...5.4650S | doi-access = free | language = en }}</ref>

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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}}</ref> This means that it will only learn well if the bias matches the learning problem. A learning algorithm may perform very well in one ___domain, but not on the next. This poses strong restrictions on the use of [[machine learning]] or [[data mining]] techniques, since the relationship between the learning problem (often some kind of [[database]]) and the effectiveness of different learning algorithms is not yet understood.

By using different kinds of metadata, like properties of the learning problem, algorithm properties (like performance measures), or patterns previously derived from the data, it is possible to learn, select, alter or combine different learning algorithms to effectively solve a given learning problem. Critiques of meta -learning approaches bear a strong resemblance to the critique of [[metaheuristic]], a possibly related problem. A good analogy to meta-learning, and the inspiration for [[Jürgen Schmidhuber]]'s early work (1987)<ref name="sch1987" /> and [[Yoshua Bengio]] et al.'s work (1991),<ref>{{cite conference|last1=Bengio|first1=Yoshua|last2=Bengio|first2=Samy|last3=Cloutier|first3=Jocelyn|conference=IJCNN'91|url=http://bengio.abracadoudou.com/publications/pdf/bengio_1991_ijcnn.pdf|date=1991|title=Learning to learn a synaptic rule|language=en}}</ref> considers that genetic evolution learns the learning procedure encoded in genes and executed in each individual's brain. In an open-ended hierarchical meta -learning system<ref name="sch1987" /> using [[genetic programming]], better evolutionary methods can be learned by meta evolution, which itself can be improved by meta meta evolution, etc.<ref name="sch1987" />

== Definition ==

A proposed definition<ref>{{Cite journal|last1=Lemke|first1=Christiane|last2=Budka|first2=Marcin|last3=Gabrys|first3=Bogdan|date=2013-07-20|title=Metalearning: a survey of trends and technologies|journal=Artificial Intelligence Review|language=en|volume=44|issue=1|pages=117–130|doi=10.1007/s10462-013-9406-y|issn=0269-2821|pmc=4459543|pmid=26069389}}</ref> for a meta -learning system combines three requirements:

* The system must include a learning subsystem.

* Experience is gained by exploiting meta knowledge extracted

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** from different domains.

* Learning bias must be chosen dynamically.

''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|language=en}}</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 |access-date=27 March 2020|doi-access=free|language=en }}</ref>

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==Examples==

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 | journal = Proceedings of ICANN'93, Amsterdam | pages = 446–451 | language = en}}</ref> In 2001, [[Sepp Hochreiter]] & A.S. Younger & P.R. Conwell built a successful supervised meta -learner based on [[Long short-term memory]] RNNs. It learned through backpropagation a learning algorithm for quadratic functions that is much faster than backpropagation.<ref name="hoch2001">{{cite journal | last1 = Hochreiter | first1 = Sepp | last2 = Younger | first2 = A. S. | last3 = Conwell | first3 = P. R. | year = 2001| title = Learning to Learn Using Gradient Descent | journal = Proceedings of ICANN'01| pages = 87–94| language = en}}</ref><ref name="scholarpedia" /> Researchers at [[Deepmind]] (Marcin Andrychowicz et al.) extended this approach to optimization in 2017.<ref name="marcin2017">{{cite journal | last1 = Andrychowicz | first1 = Marcin | last2 = Denil | first2 = Misha | last3 = Gomez | first3 = Sergio | last4 = Hoffmann | first4 = Matthew | last5 = Pfau | first5 = David | last6 = Schaul | first6 = Tom | last7 = Shillingford | first7 = Brendan | last8 = de Freitas | first8 = Nando | year = 2017| title = Learning to learn by gradient descent by gradient descent | journal = Proceedings of ICML'17, Sydney, Australia}}</ref>

* 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 | journal = Technical Report FKI-198-94, Tech. Univ. Munich | language = en | url = http://people.idsia.ch/~juergen/FKI-198-94ocr.pdf}}</ref><ref name="sch1997">{{cite journal | last1 = Schmidhuber | first1 = Jürgen | last2 = Zhao | first2 = J. | last3 = Wiering | first3 = M. | year = 1997| title = Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement | journal = Machine Learning | volume = 28 | pages = 105–130 | doi=10.1023/a:1007383707642| doi-access = free| language = en }}</ref>

* 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 | language=en}}</ref><ref name="scholarpedia" />

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* Video courses about Meta-Learning with step-by-step explanation of [https://www.youtube.com/watch?v=IkDw22a8BDE MAML], [https://www.youtube.com/watch?v=rHGPfl0pvLY Prototypical Networks], and [https://www.youtube.com/watch?v=j8qDaVfrO_c Relation Networks].

{{DEFAULTSORT:Meta -Learning (Computer Science)}}

[[Category:Machine learning]]