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- W2162953669 abstract "Introduces gradient descent methods applied to meta-learning (learning how to learn) in neural networks. Meta-learning has been of interest in the machine learning field for decades because of its appealing applications to intelligent agents, non-stationary time series, autonomous robots, and improved learning algorithms. Many previous neural network-based approaches toward meta-learning have been based on evolutionary methods. We show how to use gradient descent for meta-learning in recurrent neural networks. Based on previous work on fixed-weight learning neural networks, we hypothesize that any recurrent network topology and its corresponding learning algorithm(s) is a potential meta-learning system. We tested several recurrent neural network topologies and their corresponding forms of backpropagation for their ability to meta-learn. One of our systems, based on the long short-term memory neural network developed a learning algorithm that could learn any two-dimensional quadratic function (from a set of such functions) after only 30 training examples." @default.
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- W2162953669 date "2002-11-13" @default.
- W2162953669 modified "2023-10-02" @default.
- W2162953669 title "Meta-learning with backpropagation" @default.
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- W2162953669 doi "https://doi.org/10.1109/ijcnn.2001.938471" @default.
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