Taxonomy of Methods for Deep Meta Learning

Carlos E. Perez
Mar 5, 2017 · 8 min read
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Credit: https://unsplash.com/search/search?photo=ICE__bo2Vws
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Hyper-parameter optimization generates networks selecting weight initialization and learning algorithm parameters.
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CoDeepNeat generates networks selecting weight initialization, learning hyperparameters and layers.
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MetaQNN uses RL to generate networks selecting layers and connections of a network.
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Neural Architecture Search — RL trains LSTM to select layers and connections of a CNN or LSTM.
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Hyper Network — Learns a neural network that generated the weights of another network.
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Learning to Learn — Replaced SGD with a trained LSTM
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PathNet selects new connections (not layers) while using a combination of SGD and GA.
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Taxonomy of Meta-Learning methods
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Exploit Deep Learning: The Deep Learning AI Playbook

Intuition Machine

Deep Learning Patterns, Methodology and Strategy

Carlos E. Perez

Written by

Author of Artificial Intuition and the Deep Learning Playbook — http://linkedin.com/in/ceperez https://twitter.com/IntuitMachine

Intuition Machine

Deep Learning Patterns, Methodology and Strategy

Carlos E. Perez

Written by

Author of Artificial Intuition and the Deep Learning Playbook — http://linkedin.com/in/ceperez https://twitter.com/IntuitMachine

Intuition Machine

Deep Learning Patterns, Methodology and Strategy

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