Transcription of Meta-Learning with Memory-Augmented Neural Networks
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Meta-Learning with Memory-Augmented Neural Networks
Meta-Learning with Memory-Augmented Neural NetworksAdam DeepMindSergey DeepMind, National Research University Higher School of Economics (HSE)Matthew DeepMindAbstractDespite recent breakthroughs in the applicationsof deep Neural Networks , one setting that presentsa persistent challenge is that of one-shot learn-ing. Traditional gradient-based Networks requirea lot of data to learn, often through extensive it-erative training. When new data is encountered,the models must inefficiently relearn their param-eters to adequately incorporate the new informa-tion without catastrophic interference. Architec-tures with augmented memory capacities, such asNeural Turing Machines (NTMs), offer the abil-ity to quickly encode and retrieve new informa-tion, and hence can potentially obviate the down-sides of conventional models. Here, we demon-strate the ability of a Memory-Augmented neu-ral network to rapidly assimilate new data, andleverage this data to make accurate predictionsafter only a few samples.
Timothy Lillicrap COUNTZERO@GOOGLE.COM Google DeepMind Abstract Despite recent breakthroughs in the applications ofdeepneuralnetworks,onesettingthatpresents a persistent challenge is that of “one-shot learn-ing.”Traditionalgradient-basednetworksrequire a lot of data to learn, often through extensive it-erative training. When new data is ...
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