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‘MrsFormer’ Employs a Novel Multiresolution-Head Attention Mechanism to Cut Transformers’ Compute and Memory Costs
Nov. 14, 2022, 5:28 p.m. | Synced
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In the new paper Transformers with Multiresolution Attention Heads (currently under double-blind review for ICLR 2023), researchers propose MrsFormer, a novel transformer architecture that uses Multiresolution-head Attention to approximate output sequences and significantly reduces head redundancy without sacrificing accuracy.
The post ‘MrsFormer’ Employs a Novel Multiresolution-Head Attention Mechanism to Cut Transformers’ Compute and Memory Costs first appeared on Synced.
ai artificial intelligence attention attention mechanisms compute costs deep-neural-networks head machine learning machine learning & data science memory ml research technology transformers
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