Meta-learning (computer science): Difference between revisions

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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 | url= | journal = Scholarpedia | volume = 5 | issue = 6| page = 4650 | doi=10.4249/scholarpedia.4650| bibcode = 2010SchpJ...5.4650S | doi-access = free }}</ref>

is a subfield of [[machine learning]] where automatic learning algorithms are applied on [[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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* [[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 | url= | journal = Proceedings of ICANN'93, Amsterdam | pages = 446–451}}</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 | url= | journal = Proceedings of ICANN'01| pages = 87–94}}</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 | url= | 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 | url= | journal = Technical Report FKI-198-94, Tech. Univ. Munich}}</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 | url= | journal = Machine Learning | volume = 28 | pages = 105–130 | doi=10.1023/a:1007383707642| doi-access = free }}</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= | 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>