Meta-learning techniques
what are the meta-learning approaches (methods)?
are bagging, boosting, ... meta-learning techniques?
is there a good reference for meta-learning techniques?
Please give a description in your answer.
machine-learning data-mining
asked 11 hours ago
jimmy
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what are the meta-learning approaches (methods)?
are bagging, boosting, ... meta-learning techniques?
is there a good reference for meta-learning techniques?
Please give a description in your answer.
machine-learning data-mining
asked 11 hours ago
jimmy
61
New contributor
jimmy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
what are the meta-learning approaches (methods)?
are bagging, boosting, ... meta-learning techniques?
is there a good reference for meta-learning techniques?
Please give a description in your answer.
machine-learning data-mining
asked 11 hours ago
jimmy
61
New contributor
jimmy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
what are the meta-learning approaches (methods)?
are bagging, boosting, ... meta-learning techniques?
is there a good reference for meta-learning techniques?
Please give a description in your answer.
machine-learning data-mining
machine-learning data-mining
asked 11 hours ago
jimmy
61
New contributor
jimmy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 hours ago
jimmy
61
New contributor
jimmy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 hours ago
jimmy
61
New contributor
jimmy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 11 hours ago
jimmy
61
asked 11 hours ago
jimmy
61
61
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jimmy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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1 Answer
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I'm familiar with two meanings of "meta-learning."
- Learning methods which allow a model to quickly adapt and fit new data. One example is MAML and related models.
"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" by Chelsea Finn, Pieter Abbeel, Sergey Levine
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.
- The second meaning of meta-learning is hyper-parameter tuning, such as using LIPO or Bayesian optimization to find the best parameters of a machine learning model (neural network, SVM, boosted tree ensemble). I don't have a reference at hand for this usage, since I've only seen it used this way on internet fora (comments on stats.SE posts, or threads in r/MachineLearning).
I'm not familiar with a usage of "meta-learning" which includes bagging and boosting as examples. Bagging and boosting are typically used with ensemble methods (such as random forest or boosted trees).
answered 9 hours ago
Sycorax
38.8k1197192
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1 Answer
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1 Answer
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active
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active
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I'm familiar with two meanings of "meta-learning."
- Learning methods which allow a model to quickly adapt and fit new data. One example is MAML and related models.
"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" by Chelsea Finn, Pieter Abbeel, Sergey Levine
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.
- The second meaning of meta-learning is hyper-parameter tuning, such as using LIPO or Bayesian optimization to find the best parameters of a machine learning model (neural network, SVM, boosted tree ensemble). I don't have a reference at hand for this usage, since I've only seen it used this way on internet fora (comments on stats.SE posts, or threads in r/MachineLearning).
I'm not familiar with a usage of "meta-learning" which includes bagging and boosting as examples. Bagging and boosting are typically used with ensemble methods (such as random forest or boosted trees).
answered 9 hours ago
Sycorax
38.8k1197192
add a comment |
I'm familiar with two meanings of "meta-learning."
- Learning methods which allow a model to quickly adapt and fit new data. One example is MAML and related models.
"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" by Chelsea Finn, Pieter Abbeel, Sergey Levine
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.
- The second meaning of meta-learning is hyper-parameter tuning, such as using LIPO or Bayesian optimization to find the best parameters of a machine learning model (neural network, SVM, boosted tree ensemble). I don't have a reference at hand for this usage, since I've only seen it used this way on internet fora (comments on stats.SE posts, or threads in r/MachineLearning).
I'm not familiar with a usage of "meta-learning" which includes bagging and boosting as examples. Bagging and boosting are typically used with ensemble methods (such as random forest or boosted trees).
answered 9 hours ago
Sycorax
38.8k1197192
add a comment |
I'm familiar with two meanings of "meta-learning."
- Learning methods which allow a model to quickly adapt and fit new data. One example is MAML and related models.
"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" by Chelsea Finn, Pieter Abbeel, Sergey Levine
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.
- The second meaning of meta-learning is hyper-parameter tuning, such as using LIPO or Bayesian optimization to find the best parameters of a machine learning model (neural network, SVM, boosted tree ensemble). I don't have a reference at hand for this usage, since I've only seen it used this way on internet fora (comments on stats.SE posts, or threads in r/MachineLearning).
I'm not familiar with a usage of "meta-learning" which includes bagging and boosting as examples. Bagging and boosting are typically used with ensemble methods (such as random forest or boosted trees).
answered 9 hours ago
Sycorax
38.8k1197192
I'm familiar with two meanings of "meta-learning."
- Learning methods which allow a model to quickly adapt and fit new data. One example is MAML and related models.
"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" by Chelsea Finn, Pieter Abbeel, Sergey Levine
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two few-shot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.
- The second meaning of meta-learning is hyper-parameter tuning, such as using LIPO or Bayesian optimization to find the best parameters of a machine learning model (neural network, SVM, boosted tree ensemble). I don't have a reference at hand for this usage, since I've only seen it used this way on internet fora (comments on stats.SE posts, or threads in r/MachineLearning).
I'm not familiar with a usage of "meta-learning" which includes bagging and boosting as examples. Bagging and boosting are typically used with ensemble methods (such as random forest or boosted trees).
answered 9 hours ago
Sycorax
38.8k1197192
edited 8 hours ago
answered 9 hours ago
Sycorax
38.8k1197192
answered 9 hours ago
Sycorax
38.8k1197192
answered 9 hours ago
Sycorax
38.8k1197192
38.8k1197192
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