auto-attention mechanism
自注意力机制
using auto-attention
使用自注意力
auto-attention layer
自注意力层
with auto-attention
带有自注意力
auto-attention scores
自注意力分数
auto-attention weights
自注意权重
apply auto-attention
应用自注意力
auto-attention model
自注意力模型
auto-attention network
自注意网络
auto-attention improved
自注意改进
the model leverages auto-attention to focus on relevant input features.
模型利用自注意力机制来关注相关的输入特征。
we incorporated auto-attention into the transformer architecture for improved performance.
我们将自注意力机制融入了transformer架构，以提高性能。
auto-attention allows the network to weigh different parts of the input sequence.
自注意力机制允许网络对输入序列的不同部分进行加权处理。
the auto-attention mechanism significantly boosted the machine translation accuracy.
自注意力机制显著提高了机器翻译的准确性。
visual auto-attention helps the model understand image context better.
视觉自注意力机制有助于模型更好地理解图像上下文。
we observed that auto-attention captured long-range dependencies effectively.
我们观察到自注意力机制能够有效地捕捉长距离依赖关系。
the research explored the application of auto-attention in sentiment analysis.
该研究探讨了自注意力机制在情感分析中的应用。
sparse auto-attention reduces computational complexity without sacrificing accuracy.
稀疏自注意力机制在不牺牲准确性的前提下降低了计算复杂度。
auto-attention layers are crucial for understanding complex relationships in data.
自注意力层对于理解数据中复杂的相互关系至关重要。
the auto-attention scores highlight the most important words in the sentence.
自注意力的分数突出了句子中最重要的词语。
compared to previous methods, auto-attention demonstrated superior contextual understanding.
与之前的技术相比，自注意力机制表现出更优越的上下文理解能力。
auto-attention mechanism
自注意力机制
using auto-attention
使用自注意力
auto-attention layer
自注意力层
with auto-attention
带有自注意力
auto-attention scores
自注意力分数
auto-attention weights
自注意权重
apply auto-attention
应用自注意力
auto-attention model
自注意力模型
auto-attention network
自注意网络
auto-attention improved
自注意改进
the model leverages auto-attention to focus on relevant input features.
模型利用自注意力机制来关注相关的输入特征。
we incorporated auto-attention into the transformer architecture for improved performance.
我们将自注意力机制融入了transformer架构，以提高性能。
auto-attention allows the network to weigh different parts of the input sequence.
自注意力机制允许网络对输入序列的不同部分进行加权处理。
the auto-attention mechanism significantly boosted the machine translation accuracy.
自注意力机制显著提高了机器翻译的准确性。
visual auto-attention helps the model understand image context better.
视觉自注意力机制有助于模型更好地理解图像上下文。
we observed that auto-attention captured long-range dependencies effectively.
我们观察到自注意力机制能够有效地捕捉长距离依赖关系。
the research explored the application of auto-attention in sentiment analysis.
该研究探讨了自注意力机制在情感分析中的应用。
sparse auto-attention reduces computational complexity without sacrificing accuracy.
稀疏自注意力机制在不牺牲准确性的前提下降低了计算复杂度。
auto-attention layers are crucial for understanding complex relationships in data.
自注意力层对于理解数据中复杂的相互关系至关重要。
the auto-attention scores highlight the most important words in the sentence.
自注意力的分数突出了句子中最重要的词语。
compared to previous methods, auto-attention demonstrated superior contextual understanding.
与之前的技术相比，自注意力机制表现出更优越的上下文理解能力。
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