Reformer matches standard Transformer accuracy on long sequences while using far less memory and running faster via LSH attention and reversible residual layers.
Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, and Armand Joulin
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Geminet learns a topology-agnostic iterative process based on gradient descent and edge dual variables to enable lightweight ML-based traffic engineering that handles dynamic topologies with far lower resource use than prior methods.
citing papers explorer
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Reformer: The Efficient Transformer
Reformer matches standard Transformer accuracy on long sequences while using far less memory and running faster via LSH attention and reversible residual layers.
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Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies
Geminet learns a topology-agnostic iterative process based on gradient descent and edge dual variables to enable lightweight ML-based traffic engineering that handles dynamic topologies with far lower resource use than prior methods.