Mar 15, 2022 · We propose a new mix-head attention (Mixhead) which mixes multiple attention heads to improve the expressive power of the model.
Mar 15, 2022 · We propose a new mix-head attention (Mixhead) which mixes multiple attention heads to improve the expressive power of the model.
To tackle this problem, we propose a mix-head attention (Mixhead) which mixes multiple attention heads by learnable mixing weights to improve the expressive ...
Feb 17, 2020 · Our analysis highlights that the scaling between the number of heads and the size of each head in the current architecture gives rise to a low-rank bottleneck ...
To tackle this problem, we propose a mix-head attention (Mixhead) which mixes multiple attention heads by learnable mixing weights to improve the expressive ...
Main code for the paper "Mixhead: Breaking The Low-Rank Bottleneck in Multi-Head Attention Language Models". Replace the multihead_attention.py of ...
Mixhead: Breaking the low-rank bottleneck in multi-head attention language models. Zhong Zhang, Nian Shao, Chongming Gao, Rui Miao, Qinli ...
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Recent advances in using the self attention models in natural language tasks have been made by first using a language modeling task to pre-train the models and ...
Missing: Mixhead: Breaking
Mixhead: Breaking the low-rank bottleneck in multi-head attention language models · 作者： · 论文关键词：Language model,Multi-head attention,Low-rank bottleneck.
This paper identifies one of the important factors contributing to the large embedding size requirement for tokens, and proposes to set the head size of an ...
Missing: Mixhead: | Show results with:Mixhead: