Meta learning

What is meta-learning?

Meta-learning is essentially learning how to learn. Meta-learning algorithms learn from the outputs of other learning algorithms which learn from data. It usually involves making use of machine learning algorithms that are able to learn how to most effectively combine the predictions made by other machine learning algorithms in the field of ensemble learning.

However, meta-learning could also refer to model selection and algorithm tuning being performed manually by a practitioner on a machine learning project that modern automl algorithms aim to automate. 

It even refers to multi-task learning which involves learning across several related predictive modeling tasks. Here, meta-learning algorithms are able to learn how to learn. 

Meta-learning algorithms make their own predictions by using the output or predictions of existing machine learning algorithms as input and then predicting a number or class label.

You could say that meta-learning occurs one level above machine learning.

Machine learning learns the best way to use the information found in data to make predictions, while meta-learning learns the best way to use the predictions that machine learning algorithms have made to make predictions.

A meta-learning algorithm or a meta-learning machine learning algorithm can be simply referred to as a meta-algorithm or a meta-learner in short-hand.

Meta-learning can be used to observe the performance of several machine learning models about learning tasks, learn from metadata, and speed up the learning process for new tasks.

What is the importance of meta-learning?

Meta-learning is very important because it helps machine learning algorithms overcome certain challenges like the requirement of large datasets for the purpose of training, the high operational costs caused by the vast amount of experiments conducted during the training phase, and the fact that a lot of time is spent conducting the experiments to identify which models perform the best for specific datasets.

Meta-learning helps by optimizing the learning algorithms and identifying the ones that perform the best.

What is a meta classifier in machine learning?

In machine learning, a meta-classifier is essentially a meta-learning algorithm that is used for classification predictive modeling tasks. 

A meta-classifier is the classifier that makes a final prediction among all the predictions by using those predictions as features. It uses the classes predicted by several other classifiers and selects the final one as the result that is needed.

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What is a meta-regressor?

A meta-regressor is a meta-learning algorithm that is used for regression predictive modeling tasks. It makes use of regression analysis to combine, compare, and synthesize the research findings from several studies while adjusting for the effects of available covariates on a response variable. 

A meta-regression analysis seeks to reconcile conflicting research studies or to corroborate ones that are consistent with each other.


What techniques are used in meta-learning?

Here are some of the approaches that are used in meta-learning:

Metric Learning

This refers to learning a metric space for predictions. It delivers good results in few-shot classification tasks. The main idea in metric learning is very similar to nearest neighbors algorithms (k-Nearest Neighbors classifier and k-means clustering). 

Model-Agnostic Meta-Learning (MAML)

In MAML, the neural network is trained with the use of examples to adapt the model to new tasks at a quicker pace. It is a general optimization and task-agnostic algorithm which is employed for the purpose of training the parameters of a model for quick learning with a small number of gradient updates.


Recurrent Neural Networks (RNNs)

Recurrent neural networks are a type of artificial intelligence. These RNNs are applied to several machine learning problems. They are especially used on problems that have sequential data or time-series data.

You’ll generally find them used for language translation, speech recognition, and handwriting recognition tasks. 

In meta-learning, an RNN is used as an alternative to creating a recurrent model that has the ability to gather data sequentially from datasets and process this data as new inputs.

Stacking or Stacked Generalization

Stacking is a sub-field of ensemble learning and is used in meta-learning models. Supervised as well as unsupervised learning gain advantages from stacking. Here is the process involved in stacking:

  • Training learning algorithms using the data that is available.
  • Creating a combiner algorithm to combine the predictions of the learning algorithms (known as ensemble members).
  • Make use of the combiner algorithm for the purpose of making the final predictions.


Convolutional Siamese Neural Network

A convolutional siamese neural network comprises of two twin networks. Their outputs are trained jointly on top using a function to understand the relationship between pairs of input data samples.

The twin networks share the same weights and network parameters. They refer to the same embedding network that learns an efficient embedding to reveal the relationship between the pairs of data points.


Matching networks

Matching networks learn a classifier for any small support set. The classifier defines a probability distribution over output labels with a specific test example. It essentially maps a small labeled support set and an unlabelled example to its label, eliminating the need to fine-tune for adapting to new class types.

LSTM Meta-Learner

An LSTM meta-learning algorithm finds the exact optimization algorithm that is employed for training another learner neural network classifier in the few-shot regime. The parametrization makes it possible for it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made. It even learns a general initialization of the learner network that enables the quick convergence of training.


What are the advantages of meta-learning?

Meta-learning algorithms help enhance machine learning solutions. Here are the most significant advantages of meta-learning:

Increased model prediction accuracy

Meta-learning optimizes learning algorithms. The meta-learning algorithm automates the optimization task which is normally performed by a human.


It speeds up the training process and makes it more economical

Meta-learning allows learning from fewer examples. It also boosts the speed of learning processes by reducing the number of experiments required. 


Helps build generalized models

Meta-learning does not concentrate on training one model on one specific dataset. It aims for learning that can be used to solve multiple tasks instead of just one task.

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Meta learning

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is meta-learning?

Meta-learning is essentially learning how to learn. Meta-learning algorithms learn from the outputs of other learning algorithms which learn from data. It usually involves making use of machine learning algorithms that are able to learn how to most effectively combine the predictions made by other machine learning algorithms in the field of ensemble learning.

However, meta-learning could also refer to model selection and algorithm tuning being performed manually by a practitioner on a machine learning project that modern automl algorithms aim to automate. 

It even refers to multi-task learning which involves learning across several related predictive modeling tasks. Here, meta-learning algorithms are able to learn how to learn. 

Meta-learning algorithms make their own predictions by using the output or predictions of existing machine learning algorithms as input and then predicting a number or class label.

You could say that meta-learning occurs one level above machine learning.

Machine learning learns the best way to use the information found in data to make predictions, while meta-learning learns the best way to use the predictions that machine learning algorithms have made to make predictions.

A meta-learning algorithm or a meta-learning machine learning algorithm can be simply referred to as a meta-algorithm or a meta-learner in short-hand.

Meta-learning can be used to observe the performance of several machine learning models about learning tasks, learn from metadata, and speed up the learning process for new tasks.

What is the importance of meta-learning?

Meta-learning is very important because it helps machine learning algorithms overcome certain challenges like the requirement of large datasets for the purpose of training, the high operational costs caused by the vast amount of experiments conducted during the training phase, and the fact that a lot of time is spent conducting the experiments to identify which models perform the best for specific datasets.

Meta-learning helps by optimizing the learning algorithms and identifying the ones that perform the best.

What is a meta classifier in machine learning?

In machine learning, a meta-classifier is essentially a meta-learning algorithm that is used for classification predictive modeling tasks. 

A meta-classifier is the classifier that makes a final prediction among all the predictions by using those predictions as features. It uses the classes predicted by several other classifiers and selects the final one as the result that is needed.

Build an AI chatbot to engage your always-on customers


What is a meta-regressor?

A meta-regressor is a meta-learning algorithm that is used for regression predictive modeling tasks. It makes use of regression analysis to combine, compare, and synthesize the research findings from several studies while adjusting for the effects of available covariates on a response variable. 

A meta-regression analysis seeks to reconcile conflicting research studies or to corroborate ones that are consistent with each other.


What techniques are used in meta-learning?

Here are some of the approaches that are used in meta-learning:

Metric Learning

This refers to learning a metric space for predictions. It delivers good results in few-shot classification tasks. The main idea in metric learning is very similar to nearest neighbors algorithms (k-Nearest Neighbors classifier and k-means clustering). 

Model-Agnostic Meta-Learning (MAML)

In MAML, the neural network is trained with the use of examples to adapt the model to new tasks at a quicker pace. It is a general optimization and task-agnostic algorithm which is employed for the purpose of training the parameters of a model for quick learning with a small number of gradient updates.


Recurrent Neural Networks (RNNs)

Recurrent neural networks are a type of artificial intelligence. These RNNs are applied to several machine learning problems. They are especially used on problems that have sequential data or time-series data.

You’ll generally find them used for language translation, speech recognition, and handwriting recognition tasks. 

In meta-learning, an RNN is used as an alternative to creating a recurrent model that has the ability to gather data sequentially from datasets and process this data as new inputs.

Stacking or Stacked Generalization

Stacking is a sub-field of ensemble learning and is used in meta-learning models. Supervised as well as unsupervised learning gain advantages from stacking. Here is the process involved in stacking:

  • Training learning algorithms using the data that is available.
  • Creating a combiner algorithm to combine the predictions of the learning algorithms (known as ensemble members).
  • Make use of the combiner algorithm for the purpose of making the final predictions.


Convolutional Siamese Neural Network

A convolutional siamese neural network comprises of two twin networks. Their outputs are trained jointly on top using a function to understand the relationship between pairs of input data samples.

The twin networks share the same weights and network parameters. They refer to the same embedding network that learns an efficient embedding to reveal the relationship between the pairs of data points.


Matching networks

Matching networks learn a classifier for any small support set. The classifier defines a probability distribution over output labels with a specific test example. It essentially maps a small labeled support set and an unlabelled example to its label, eliminating the need to fine-tune for adapting to new class types.

LSTM Meta-Learner

An LSTM meta-learning algorithm finds the exact optimization algorithm that is employed for training another learner neural network classifier in the few-shot regime. The parametrization makes it possible for it to learn appropriate parameter updates specifically for the scenario where a set amount of updates will be made. It even learns a general initialization of the learner network that enables the quick convergence of training.


What are the advantages of meta-learning?

Meta-learning algorithms help enhance machine learning solutions. Here are the most significant advantages of meta-learning:

Increased model prediction accuracy

Meta-learning optimizes learning algorithms. The meta-learning algorithm automates the optimization task which is normally performed by a human.


It speeds up the training process and makes it more economical

Meta-learning allows learning from fewer examples. It also boosts the speed of learning processes by reducing the number of experiments required. 


Helps build generalized models

Meta-learning does not concentrate on training one model on one specific dataset. It aims for learning that can be used to solve multiple tasks instead of just one task.

Let's build your first AI Chatbot today!


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