2016 OneShotLearningwithMemoryAugmen

• (Santoro et al., 2016) ⇒ Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap. (2016). “One-shot Learning with Memory-Augmented Neural Networks.” In: Proceedings of Deep Learning Symposium (NIPS 2016) . e-print arXiv:1605.06065.

Subject Headings: Least Recently Used Access Memory; Memory-Augmented Neural Network; Dynamic Neural Turing Machine

Cited By

• http://scholar.google.com/scholar?q=%222016%22++One-shot+Learning+with+Memory-Augmented+Neural+Networks
• https://openreview.net/forum?id=HJkPb0N9
• https://www.shortscience.org/paper?bibtexKey=journals/corr/1605.06065

Quotes

Abstract

Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of " one-shot learning. “ Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 OneShotLearningwithMemoryAugmen	Matthew Botvinick Daan Wierstra Timothy Lillicrap Adam Santoro Sergey Bartunov			One-shot Learning with Memory-Augmented Neural Networks						2016