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<title>Meta-learning (computer science)</title>

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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Meta-learning (computer science)</span></span>

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</style><div role="note" class="hatnote navigation-not-searchable">This article is about meta-learning in machine learning. For meta-learning in social psychology, see <a href="Meta-learning" title="Meta-learning">Meta-learning</a>. For metalearning in neuroscience, see <a href="Metalearning_(neuroscience)" title="Metalearning (neuroscience)">Metalearning (neuroscience)</a>.</div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Ensemble_learning" title="Ensemble learning">Ensemble learning</a></div>

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</style><table class="sidebar sidebar-collapse nomobile nowraplinks"><tbody><tr><td class="sidebar-pretitle">Part of a series on</td></tr><tr><th class="sidebar-title-with-pretitle"><a href="Machine_learning" title="Machine learning">Machine learning</a><br>and <a href="Data_mining" title="Data mining">data mining</a></th></tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Paradigms</div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Supervised_learning" title="Supervised learning">Supervised learning</a></li>

<li><a href="Unsupervised_learning" title="Unsupervised learning">Unsupervised learning</a></li>

<li><a href="Semi-supervised_learning" class="mw-redirect" title="Semi-supervised learning">Semi-supervised learning</a></li>

<li><a href="Self-supervised_learning" title="Self-supervised learning">Self-supervised learning</a></li>

<li><a href="Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a></li>

<li><a href="Online_machine_learning" title="Online machine learning">Online learning</a></li>

<li><a href="Batch_learning" class="mw-redirect" title="Batch learning">Batch learning</a></li>

<li><a href="Curriculum_learning" title="Curriculum learning">Curriculum learning</a></li>

<li><a href="Rule-based_machine_learning" title="Rule-based machine learning">Rule-based learning</a></li>

<li><a href="Neuro-symbolic_AI" title="Neuro-symbolic AI">Neuro-symbolic AI</a></li>

<li><a href="Neuromorphic_engineering" class="mw-redirect" title="Neuromorphic engineering">Neuromorphic engineering</a></li>

<li><a href="Quantum_machine_learning" title="Quantum machine learning">Quantum machine learning</a></li></ul></div></div></td>

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<ul><li><a href="Statistical_classification" title="Statistical classification">Classification</a></li>

<li><a href="Generative_model" title="Generative model">Generative modeling</a></li>

<li><a href="Regression_analysis" title="Regression analysis">Regression</a></li>

<li><a href="Cluster_analysis" title="Cluster analysis">Clustering</a></li>

<li><a href="Dimensionality_reduction" title="Dimensionality reduction">Dimensionality reduction</a></li>

<li><a href="Density_estimation" title="Density estimation">Density estimation</a></li>

<li><a href="Anomaly_detection" title="Anomaly detection">Anomaly detection</a></li>

<li><a href="Data_cleaning" class="mw-redirect" title="Data cleaning">Data cleaning</a></li>

<li><a href="Automated_machine_learning" title="Automated machine learning">AutoML</a></li>

<li><a href="Association_rule_learning" title="Association rule learning">Association rules</a></li>

<li><a href="Semantic_analysis_(machine_learning)" title="Semantic analysis (machine learning)">Semantic analysis</a></li>

<li><a href="Structured_prediction" title="Structured prediction">Structured prediction</a></li>

<li><a href="Feature_engineering" title="Feature engineering">Feature engineering</a></li>

<li><a href="Feature_learning" title="Feature learning">Feature learning</a></li>

<li><a href="Learning_to_rank" title="Learning to rank">Learning to rank</a></li>

<li><a href="Grammar_induction" title="Grammar induction">Grammar induction</a></li>

<li><a href="Ontology_learning" title="Ontology learning">Ontology learning</a></li>

<li><a href="Multimodal_learning" title="Multimodal learning">Multimodal learning</a></li></ul></div></div></td>

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<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><div style="display: inline-block; line-height: 1.2em; padding: .1em 0;"><a href="Supervised_learning" title="Supervised learning">Supervised learning</a><br><span class="nobold"><span style="font-size: 85%;">(<b><a href="Statistical_classification" title="Statistical classification">classification</a></b> • <b><a href="Regression_analysis" title="Regression analysis">regression</a></b>)</span></span> </div></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Apprenticeship_learning" title="Apprenticeship learning">Apprenticeship learning</a></li>

<li><a href="Decision_tree_learning" title="Decision tree learning">Decision trees</a></li>

<li><a href="Ensemble_learning" title="Ensemble learning">Ensembles</a>

<ul><li><a href="Bootstrap_aggregating" title="Bootstrap aggregating">Bagging</a></li>

<li><a href="Boosting_(machine_learning)" title="Boosting (machine learning)">Boosting</a></li>

<li><a href="Random_forest" title="Random forest">Random forest</a></li></ul></li>

<li><a href="Linear_regression" title="Linear regression">Linear regression</a></li>

<li><a href="Naive_Bayes_classifier" title="Naive Bayes classifier">Naive Bayes</a></li>

<li><a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Artificial neural networks</a></li>

<li><a href="Logistic_regression" title="Logistic regression">Logistic regression</a></li>

<li><a href="Perceptron" title="Perceptron">Perceptron</a></li>

<li><a href="Relevance_vector_machine" title="Relevance vector machine">Relevance vector machine (RVM)</a></li>

<li><a href="Support_vector_machine" title="Support vector machine">Support vector machine (SVM)</a></li></ul></div></div></td>

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<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Cluster_analysis" title="Cluster analysis">Clustering</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="BIRCH" title="BIRCH">BIRCH</a></li>

<li><a href="Hierarchical_clustering" title="Hierarchical clustering">Hierarchical</a></li>

<li><a href="K-means_clustering" title="K-means clustering"><i>k</i>-means</a></li>

<li><a href="Fuzzy_clustering" title="Fuzzy clustering">Fuzzy</a></li>

<li><a href="Expectation%E2%80%93maximization_algorithm" title="Expectation–maximization algorithm">Expectation–maximization (EM)</a></li>

<li><br><a href="DBSCAN" title="DBSCAN">DBSCAN</a></li>

<li><a href="OPTICS_algorithm" title="OPTICS algorithm">OPTICS</a></li>

<li><a href="Mean_shift" title="Mean shift">Mean shift</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Dimensionality_reduction" title="Dimensionality reduction">Dimensionality reduction</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Factor_analysis" title="Factor analysis">Factor analysis</a></li>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Structured_prediction" title="Structured prediction">Structured prediction</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Graphical_model" title="Graphical model">Graphical models</a>

<ul><li><a href="Bayesian_network" title="Bayesian network">Bayes net</a></li>

<li><a href="Conditional_random_field" title="Conditional random field">Conditional random field</a></li>

<li><a href="Hidden_Markov_model" title="Hidden Markov model">Hidden Markov</a></li></ul></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Anomaly_detection" title="Anomaly detection">Anomaly detection</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Random_sample_consensus" title="Random sample consensus">RANSAC</a></li>

<li><a href="Local_outlier_factor" title="Local outlier factor">Local outlier factor</a></li>

<li><a href="Isolation_forest" title="Isolation forest">Isolation forest</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Neural_network_(machine_learning)" title="Neural network (machine learning)">Neural networks</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Autoencoder" title="Autoencoder">Autoencoder</a></li>

<li><a href="Deep_learning" title="Deep learning">Deep learning</a></li>

<li><a href="Feedforward_neural_network" title="Feedforward neural network">Feedforward neural network</a></li>

<li><a href="Recurrent_neural_network" title="Recurrent neural network">Recurrent neural network</a>

<li><a href="Reservoir_computing" title="Reservoir computing">reservoir computing</a></li></ul></li>

<li><a href="Boltzmann_machine" title="Boltzmann machine">Boltzmann machine</a>

<ul><li><a href="Restricted_Boltzmann_machine" title="Restricted Boltzmann machine">Restricted</a></li></ul></li>

<li><a href="Diffusion_model" title="Diffusion model">Diffusion model</a></li>

<li><a href="Convolutional_neural_network" title="Convolutional neural network">Convolutional neural network</a>

<li><a href="LeNet" title="LeNet">LeNet</a></li>

<li><a href="AlexNet" title="AlexNet">AlexNet</a></li>

<li><a href="DeepDream" title="DeepDream">DeepDream</a></li></ul></li>

<li><a href="Neural_field" title="Neural field">Neural field</a>

<ul><li><a href="Neural_radiance_field" title="Neural radiance field">Neural radiance field</a></li>

<li><a href="Physics-informed_neural_networks" title="Physics-informed neural networks">Physics-informed neural networks</a></li></ul></li>

<li><a href="Transformer_(deep_learning_architecture)" title="Transformer (deep learning architecture)">Transformer</a>

<ul><li><a href="Vision_transformer" title="Vision transformer">Vision</a></li></ul></li>

<li><a href="Mamba_(deep_learning_architecture)" title="Mamba (deep learning architecture)">Mamba</a></li>

<li><a href="Spiking_neural_network" title="Spiking neural network">Spiking neural network</a></li>

<li><a href="Memtransistor" title="Memtransistor">Memtransistor</a></li>

<li><a href="Electrochemical_RAM" title="Electrochemical RAM">Electrochemical RAM</a> (ECRAM)</li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)"><a href="Reinforcement_learning" title="Reinforcement learning">Reinforcement learning</a></div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Q-learning" title="Q-learning">Q-learning</a></li>

<li><a href="Policy_gradient_method" title="Policy gradient method">Policy gradient</a></li>

<li><a href="State%E2%80%93action%E2%80%93reward%E2%80%93state%E2%80%93action" title="State–action–reward–state–action">SARSA</a></li>

<li><a href="Temporal_difference_learning" title="Temporal difference learning">Temporal difference (TD)</a></li>

<li><a href="Multi-agent_reinforcement_learning" title="Multi-agent reinforcement learning">Multi-agent</a>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Active_learning_(machine_learning)" title="Active learning (machine learning)">Active learning</a></li>

<li><a href="Crowdsourcing" title="Crowdsourcing">Crowdsourcing</a></li>

<li><a href="Human-in-the-loop" title="Human-in-the-loop">Human-in-the-loop</a></li>

<li><a href="Mechanistic_interpretability" title="Mechanistic interpretability">Mechanistic interpretability</a></li>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Coefficient_of_determination" title="Coefficient of determination">Coefficient of determination</a></li>

<li><a href="Confusion_matrix" title="Confusion matrix">Confusion matrix</a></li>

<li><a href="Learning_curve_(machine_learning)" title="Learning curve (machine learning)">Learning curve</a></li>

<li><a href="Receiver_operating_characteristic" title="Receiver operating characteristic">ROC curve</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Mathematical foundations</div><div class="sidebar-list-content mw-collapsible-content hlist">

<ul><li><a href="Kernel_machines" class="mw-redirect" title="Kernel machines">Kernel machines</a></li>

<li><a href="Bias%E2%80%93variance_tradeoff" title="Bias–variance tradeoff">Bias–variance tradeoff</a></li>

<li><a href="Computational_learning_theory" title="Computational learning theory">Computational learning theory</a></li>

<li><a href="Empirical_risk_minimization" title="Empirical risk minimization">Empirical risk minimization</a></li>

<li><a href="Occam_learning" title="Occam learning">Occam learning</a></li>

<li><a href="Probably_approximately_correct_learning" title="Probably approximately correct learning">PAC learning</a></li>

<li><a href="Statistical_learning_theory" title="Statistical learning theory">Statistical learning</a></li>

<li><a href="Vapnik%E2%80%93Chervonenkis_theory" title="Vapnik–Chervonenkis theory">VC theory</a></li>

<li><a href="Topological_deep_learning" title="Topological deep learning">Topological deep learning</a></li></ul></div></div></td>

</tr><tr><td class="sidebar-content">

<div class="sidebar-list mw-collapsible mw-collapsed machine-learning-list-title"><div class="sidebar-list-title" style="border-top:1px solid #aaa; text-align:center;;color: var(--color-base)">Journals and conferences</div><div class="sidebar-list-content mw-collapsible-content hlist">

<li><a href="Conference_on_Neural_Information_Processing_Systems" title="Conference on Neural Information Processing Systems">NeurIPS</a></li>

<li><a href="International_Joint_Conference_on_Artificial_Intelligence" title="International Joint Conference on Artificial Intelligence">IJCAI</a></li>

</tr><tr><td class="sidebar-content">

<ul><li><a href="Glossary_of_artificial_intelligence" title="Glossary of artificial intelligence">Glossary of artificial intelligence</a></li>

<li><a href="List_of_datasets_for_machine-learning_research" title="List of datasets for machine-learning research">List of datasets for machine-learning research</a>

<ul><li><a href="List_of_datasets_in_computer_vision_and_image_processing" title="List of datasets in computer vision and image processing">List of datasets in computer vision and image processing</a></li></ul></li>

<li><a href="Outline_of_machine_learning" title="Outline of machine learning">Outline of machine learning</a></li></ul></div></div></td>

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<p><b>Meta-learning</b><sup id="cite_ref-sch1987_1-0" class="reference"><a href="#cite_note-sch1987-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-scholarpedia_2-0" class="reference"><a href="#cite_note-scholarpedia-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup>

is a subfield of <a href="Machine_learning" title="Machine learning">machine learning</a> where automatic learning algorithms are applied to <a href="Meta-data" class="mw-redirect" title="Meta-data">metadata</a> 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 <a href="Learning_algorithms" class="mw-redirect" title="Learning algorithms">learning algorithms</a> or to learn (induce) the learning algorithm itself, hence the alternative term <b>learning to learn</b>.<sup id="cite_ref-sch1987_1-1" class="reference"><a href="#cite_note-sch1987-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup>

</p><p>Flexibility is important because each learning algorithm is based on a set of assumptions about the data, its <a href="Inductive_bias" title="Inductive bias">inductive bias</a>.<sup id="cite_ref-utgoff1986_3-0" class="reference"><a href="#cite_note-utgoff1986-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> 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 <a href="Machine_learning" title="Machine learning">machine learning</a> or <a href="Data_mining" title="Data mining">data mining</a> techniques, since the relationship between the learning problem (often some kind of <a href="Database" title="Database">database</a>) and the effectiveness of different learning algorithms is not yet understood.

</p><p>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 <a href="Metaheuristic" title="Metaheuristic">metaheuristic</a>, a possibly related problem. A good analogy to meta-learning, and the inspiration for <a href="J%C3%BCrgen_Schmidhuber" title="Jürgen Schmidhuber">Jürgen Schmidhuber</a>'s early work (1987)<sup id="cite_ref-sch1987_1-2" class="reference"><a href="#cite_note-sch1987-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> and <a href="Yoshua_Bengio" title="Yoshua Bengio">Yoshua Bengio</a> et al.'s work (1991),<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> 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<sup id="cite_ref-sch1987_1-3" class="reference"><a href="#cite_note-sch1987-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> using <a href="Genetic_programming" title="Genetic programming">genetic programming</a>, better evolutionary methods can be learned by meta evolution, which itself can be improved by meta meta evolution, etc.<sup id="cite_ref-sch1987_1-4" class="reference"><a href="#cite_note-sch1987-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading2"><h2 id="Definition">Definition</h2></div>

<p>A proposed definition<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> for a meta-learning system combines three requirements:

</p>

<ul><li>The system must include a learning subsystem.</li>

<li>Experience is gained by exploiting meta knowledge extracted

<ul><li>in a previous learning episode on a single dataset, or</li>

<li>from different domains.</li></ul></li>

<li>Learning bias must be chosen dynamically.</li></ul>

<p><i>Bias</i> refers to the assumptions that influence the choice of explanatory hypotheses<sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> and not the notion of bias represented in the <a href="Bias-variance_dilemma" class="mw-redirect" title="Bias-variance dilemma">bias-variance dilemma</a>. Meta-learning is concerned with two aspects of learning bias.

</p>

<ul><li>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).</li>

<li>Procedural bias imposes constraints on the ordering of the inductive hypotheses (e.g., preferring smaller hypotheses).<sup id="cite_ref-7" class="reference"><a href="#cite_note-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup></li></ul>

<div class="mw-heading mw-heading2"><h2 id="Common_approaches">Common approaches</h2></div>

<p>There are three common approaches:<sup id="cite_ref-paper1_8-0" class="reference"><a href="#cite_note-paper1-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup>

</p>

<ol><li>using (cyclic) networks with external or internal memory (model-based)</li>

<li>learning effective distance metrics (metrics-based)</li>

<li>explicitly optimizing model parameters for fast learning (optimization-based).</li></ol>

<div class="mw-heading mw-heading3"><h3 id="Model-Based">Model-Based</h3></div>

<p>Model-based meta-learning models updates its parameters rapidly with a few training steps, which can be achieved by its internal architecture or controlled by another meta-learner model.<sup id="cite_ref-paper1_8-1" class="reference"><a href="#cite_note-paper1-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading4"><h4 id="Memory-Augmented_Neural_Networks">Memory-Augmented Neural Networks</h4></div>

<p>A Memory-Augmented <a href="Neural_Network" class="mw-redirect" title="Neural Network">Neural Network</a>, or MANN for short, is claimed to be able to encode new information quickly and thus to adapt to new tasks after only a few examples.<sup id="cite_ref-paper2_9-0" class="reference"><a href="#cite_note-paper2-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading4"><h4 id="Meta_Networks">Meta Networks</h4></div>

<p>Meta Networks (MetaNet) learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization.<sup id="cite_ref-paper3_10-0" class="reference"><a href="#cite_note-paper3-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading3"><h3 id="Metric-Based">Metric-Based</h3></div>

<p>The core idea in metric-based meta-learning is similar to <a href="K-nearest_neighbor_algorithm" class="mw-redirect" title="K-nearest neighbor algorithm">nearest neighbors</a> algorithms, which weight is generated by a kernel function. It aims to learn a metric or distance function over objects. The notion of a good metric is problem-dependent. It should represent the relationship between inputs in the task space and facilitate problem solving.<sup id="cite_ref-paper1_8-2" class="reference"><a href="#cite_note-paper1-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading4"><h4 id="Convolutional_Siamese_Neural_Network">Convolutional Siamese Neural Network</h4></div>

<p><a href="Siamese_neural_network" title="Siamese neural network">Siamese neural network</a> is composed of two twin networks whose output is jointly trained. There is a function above to learn the relationship between input data sample pairs. The two networks are the same, sharing the same weight and network parameters.<sup id="cite_ref-paper4_11-0" class="reference"><a href="#cite_note-paper4-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading4"><h4 id="Matching_Networks">Matching Networks</h4></div>

<p>Matching Networks learn a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types.<sup id="cite_ref-paper5_12-0" class="reference"><a href="#cite_note-paper5-12"><span class="cite-bracket">[</span>12<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading4"><h4 id="Relation_Network">Relation Network</h4></div>

<p>The Relation Network (RN), is trained end-to-end from scratch. During meta-learning, it learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting.<sup id="cite_ref-paper6_13-0" class="reference"><a href="#cite_note-paper6-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading4"><h4 id="Prototypical_Networks">Prototypical Networks</h4></div>

<p>Prototypical Networks learn a <a href="Metric_space" title="Metric space">metric space</a> in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve satisfied results.<sup id="cite_ref-paper7_14-0" class="reference"><a href="#cite_note-paper7-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading3"><h3 id="Optimization-Based">Optimization-Based</h3></div>

<p>What optimization-based meta-learning algorithms intend for is to adjust the <a href="Optimization_algorithm" class="mw-redirect" title="Optimization algorithm">optimization algorithm</a> so that the model can be good at learning with a few examples.<sup id="cite_ref-paper1_8-3" class="reference"><a href="#cite_note-paper1-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading4"><h4 id="LSTM_Meta-Learner">LSTM Meta-Learner</h4></div>

<p><a href="LSTM" class="mw-redirect" title="LSTM">LSTM</a>-based meta-learner is to learn the exact <a href="Optimization_algorithm" class="mw-redirect" title="Optimization algorithm">optimization algorithm</a> used to train another learner <a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">neural network</a> <a href="Classification_rule" title="Classification rule">classifier</a> in the few-shot regime. The parametrization allows it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made, while also learning a general initialization of the learner (classifier) network that allows for quick convergence of training.<sup id="cite_ref-paper8_15-0" class="reference"><a href="#cite_note-paper8-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading4"><h4 id="Temporal_Discreteness">Temporal Discreteness</h4></div>

<p>Model-Agnostic Meta-Learning (MAML) is a fairly general <a href="Optimization_algorithm" class="mw-redirect" title="Optimization algorithm">optimization algorithm</a>, compatible with any model that learns through gradient descent.<sup id="cite_ref-maml_16-0" class="reference"><a href="#cite_note-maml-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading4"><h4 id="Reptile">Reptile</h4></div>

<p>Reptile is a remarkably simple meta-learning optimization algorithm, given that both of its components rely on <a href="Meta-optimization" title="Meta-optimization">meta-optimization</a> through gradient descent and both are model-agnostic.<sup id="cite_ref-paper10_17-0" class="reference"><a href="#cite_note-paper10-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup>

</p>

<div class="mw-heading mw-heading2"><h2 id="Examples">Examples</h2></div>

<p>Some approaches which have been viewed as instances of meta-learning:

</p>

<ul><li><a href="Recurrent_neural_networks" class="mw-redirect" title="Recurrent neural networks">Recurrent neural networks</a> (RNNs) are universal computers. In 1993, <a href="J%C3%BCrgen_Schmidhuber" title="Jürgen Schmidhuber">Jürgen Schmidhuber</a> showed how "self-referential" RNNs can in principle learn by <a href="Backpropagation" title="Backpropagation">backpropagation</a> to run their own weight change algorithm, which may be quite different from backpropagation.<sup id="cite_ref-sch1993_18-0" class="reference"><a href="#cite_note-sch1993-18"><span class="cite-bracket">[</span>18<span class="cite-bracket">]</span></a></sup> In 2001, <a href="Sepp_Hochreiter" title="Sepp Hochreiter">Sepp Hochreiter</a> & A.S. Younger & P.R. Conwell built a successful supervised meta-learner based on <a href="Long_short-term_memory" title="Long short-term memory">Long short-term memory</a> RNNs. It learned through backpropagation a learning algorithm for quadratic functions that is much faster than backpropagation.<sup id="cite_ref-hoch2001_19-0" class="reference"><a href="#cite_note-hoch2001-19"><span class="cite-bracket">[</span>19<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-scholarpedia_2-1" class="reference"><a href="#cite_note-scholarpedia-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> Researchers at <a href="Deepmind" class="mw-redirect" title="Deepmind">Deepmind</a> (Marcin Andrychowicz et al.) extended this approach to optimization in 2017.<sup id="cite_ref-marcin2017_20-0" class="reference"><a href="#cite_note-marcin2017-20"><span class="cite-bracket">[</span>20<span class="cite-bracket">]</span></a></sup></li>

<li>In the 1990s, Meta <a href="Reinforcement_Learning" class="mw-redirect" title="Reinforcement Learning">Reinforcement Learning</a> 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.<sup id="cite_ref-sch1994_21-0" class="reference"><a href="#cite_note-sch1994-21"><span class="cite-bracket">[</span>21<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-sch1997_22-0" class="reference"><a href="#cite_note-sch1997-22"><span class="cite-bracket">[</span>22<span class="cite-bracket">]</span></a></sup></li>

<li>An extreme type of Meta <a href="Reinforcement_Learning" class="mw-redirect" title="Reinforcement Learning">Reinforcement Learning</a> is embodied by the <a href="G%C3%B6del_machine" title="Gödel machine">Gödel machine</a>, a theoretical construct which can inspect and modify any part of its own software which also contains a general <a href="Automated_theorem_proving" title="Automated theorem proving">theorem prover</a>. It can achieve <a href="Recursive_self-improvement" title="Recursive self-improvement">recursive self-improvement</a> in a provably optimal way.<sup id="cite_ref-goedelmachine_23-0" class="reference"><a href="#cite_note-goedelmachine-23"><span class="cite-bracket">[</span>23<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-scholarpedia_2-2" class="reference"><a href="#cite_note-scholarpedia-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup></li>

<li><i>Model-Agnostic Meta-Learning</i> (MAML) was introduced in 2017 by <a href="Chelsea_Finn" title="Chelsea Finn">Chelsea Finn</a> et al.<sup id="cite_ref-maml_16-1" class="reference"><a href="#cite_note-maml-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> 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."<sup id="cite_ref-maml_16-2" class="reference"><a href="#cite_note-maml-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> MAML was successfully applied to few-shot image classification benchmarks and to policy-gradient-based reinforcement learning.<sup id="cite_ref-maml_16-3" class="reference"><a href="#cite_note-maml-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup></li>

<li><i>Variational Bayes-Adaptive Deep RL</i> (VariBAD) was introduced in 2019.<sup id="cite_ref-24" class="reference"><a href="#cite_note-24"><span class="cite-bracket">[</span>24<span class="cite-bracket">]</span></a></sup> While MAML is optimization-based, VariBAD is a model-based method for meta reinforcement learning, and leverages a <a href="Variational_autoencoder" title="Variational autoencoder">variational autoencoder</a> to capture the task information in an internal memory, thus conditioning its decision making on the task.</li>

<li>When addressing a set of tasks, most meta learning approaches optimize the average score across all tasks. Hence, certain tasks may be sacrificed in favor of the average score, which is often unacceptable in real-world applications. By contrast, <i>Robust Meta Reinforcement Learning</i> (RoML) focuses on improving low-score tasks, increasing robustness to the selection of task.<sup id="cite_ref-25" class="reference"><a href="#cite_note-25"><span class="cite-bracket">[</span>25<span class="cite-bracket">]</span></a></sup> RoML works as a meta-algorithm, as it can be applied on top of other meta learning algorithms (such as MAML and VariBAD) to increase their robustness. It is applicable to both supervised meta learning and meta <a href="Reinforcement_learning" title="Reinforcement learning">reinforcement learning</a>.</li>

<li><i>Discovering <a href="Meta-knowledge" class="mw-redirect" title="Meta-knowledge">meta-knowledge</a></i> works by inducing knowledge (e.g. rules) that expresses how each learning method will perform on different learning problems. The metadata is formed by characteristics of the data (general, statistical, information-theoretic,... ) in the learning problem, and characteristics of the learning algorithm (type, parameter settings, performance measures,...). Another learning algorithm then learns how the data characteristics relate to the algorithm characteristics. Given a new learning problem, the data characteristics are measured, and the performance of different learning algorithms are predicted. Hence, one can predict the algorithms best suited for the new problem.</li>

<li><i>Stacked generalisation</i> works by combining multiple (different) learning algorithms. The metadata is formed by the predictions of those different algorithms. Another learning algorithm learns from this metadata to predict which combinations of algorithms give generally good results. Given a new learning problem, the predictions of the selected set of algorithms are combined (e.g. by (weighted) voting) to provide the final prediction. Since each algorithm is deemed to work on a subset of problems, a combination is hoped to be more flexible and able to make good predictions.</li>

<li><i><a href="Boosting_(meta-algorithm)" class="mw-redirect" title="Boosting (meta-algorithm)">Boosting</a></i> is related to stacked generalisation, but uses the same algorithm multiple times, where the examples in the training data get different weights over each run. This yields different predictions, each focused on rightly predicting a subset of the data, and combining those predictions leads to better (but more expensive) results.</li>

<li><i>Dynamic bias selection</i> works by altering the inductive bias of a learning algorithm to match the given problem. This is done by altering key aspects of the learning algorithm, such as the hypothesis representation, heuristic formulae, or parameters. Many different approaches exist.</li>

<li><i><a href="Inductive_transfer" class="mw-redirect" title="Inductive transfer">Inductive transfer</a></i> 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 <a href="Learning_to_learn" class="mw-redirect" title="Learning to learn">learning to learn</a>, in which the goal is to use acquired knowledge from one domain to help learning in other domains.</li>

<li>Other approaches using metadata to improve automatic learning are <a href="Learning_classifier_system" title="Learning classifier system">learning classifier systems</a>, <a href="Case-based_reasoning" title="Case-based reasoning">case-based reasoning</a> and <a href="Constraint_satisfaction" title="Constraint satisfaction">constraint satisfaction</a>.</li>

<li>Some initial, theoretical work has been initiated to use <i><a href="Applied_Behavioral_Analysis" class="mw-redirect" title="Applied Behavioral Analysis">Applied Behavioral Analysis</a></i> as a foundation for agent-mediated meta-learning about the performances of human learners, and adjust the instructional course of an artificial agent.<sup id="cite_ref-Begoli,_PRS-ABA,_ABA_Ontology_26-0" class="reference"><a href="#cite_note-Begoli,_PRS-ABA,_ABA_Ontology-26"><span class="cite-bracket">[</span>26<span class="cite-bracket">]</span></a></sup></li>

<li><a href="AutoML" class="mw-redirect" title="AutoML">AutoML</a> such as Google Brain's "AI building AI" project, which according to Google briefly exceeded existing <a href="ImageNet" title="ImageNet">ImageNet</a> benchmarks in 2017.<sup id="cite_ref-27" class="reference"><a href="#cite_note-27"><span class="cite-bracket">[</span>27<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-28" class="reference"><a href="#cite_note-28"><span class="cite-bracket">[</span>28<span class="cite-bracket">]</span></a></sup></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

<ul><li><a rel="nofollow" class="external text" href="http://www.scholarpedia.org/article/Metalearning">Metalearning</a> article in <a href="Scholarpedia" title="Scholarpedia">Scholarpedia</a></li>

<li><cite id="CITEREFVilaltaDrissi2002" class="citation journal cs1">Vilalta, R.; Drissi, Y. (2002). <a rel="nofollow" class="external text" href="http://axon.cs.byu.edu/Dan/478/misc/Vilalta.pdf">"A perspective view and survey of meta-learning"</a> <span class="cs1-format">(PDF)</span>. <i>Artificial Intelligence Review</i>. <b>18</b> (2): <span class="nowrap">77–</span>95. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1023%2FA%3A1019956318069">10.1023/A:1019956318069</a>.</cite></li>

<li><cite id="CITEREFGiraud-CarrierKeller2002" class="citation book cs1">Giraud-Carrier, C.; Keller, J. (2002). "Meta-Learning". In Meij, J. (ed.). <a rel="nofollow" class="external text" href="https://stt.nl/en/futures-studies/dealing-with-the-data-flood/stt65-dealing-with-the-data-flood-mining-data-text-and-multimedia"><i>Dealing with the data flood</i></a>. The Hague: STT/Beweton.</cite></li>

<li><cite id="CITEREFBrazdilGiraud-CarrierSoaresVilalta2009" class="citation book cs1">Brazdil, P.; Giraud-Carrier, C.; Soares, C.; Vilalta, R. (2009). "Metalearning: Concepts and Systems". <a rel="nofollow" class="external text" href="https://books.google.com/books?id=-Gsi_cxZGpcC"><i>Metalearning: applications to data mining</i></a>. Springer. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-3-540-73262-4</bdi>.</cite></li>

<li>Video courses about Meta-Learning with step-by-step explanation of <a rel="nofollow" class="external text" href="https://www.youtube.com/watch?v=IkDw22a8BDE">MAML</a>, <a rel="nofollow" class="external text" href="https://www.youtube.com/watch?v=rHGPfl0pvLY">Prototypical Networks</a>, and <a rel="nofollow" class="external text" href="https://www.youtube.com/watch?v=j8qDaVfrO_c">Relation Networks</a>.</li></ul></div><div><div class="zim-footer">

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