Semanlink - Facebook ; Guillaume Lample AND Deep Learning

Facebook ; Guillaume Lample AND Deep Learning

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3 Documents

Guillaume Lample sur Twitter : "Last year, we showed that you can outperform a 24-layer transformer in language modeling with just...

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2020-10-10 About

[1912.01412] Deep Learning for Symbolic Mathematics

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2019-12-09 About

[1907.05242] Large Memory Layers with Product Keys

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> **a structured memory which can be easily integrated into a neural network.** The memory is very large by design and therefore significantly increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead. Its design and access pattern is based on **product keys**, which enable fast and exact nearest neighbor search. The ability to increase the number of parameters while keeping the same computational budget lets the overall system strike a better trade-off between prediction accuracy and computation efficiency both at training and test time.

> a key-value memory layer that can increase model capacity for a negligible computational cost. A 12-layer transformer with a memory outperforms a 24-layer transformer, and is 2x faster!

[Implementation](/doc/2019/08/product_key_memory_pkm_minima)

TODO: compare with [[2007.00849] Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge](doc:2020/07/2007_00849_facts_as_experts_)

2019-07-13 About