论文精度（1）-- Lipschitz constrained parameter initialization for deep transformers

时间 2020-12-23

标签 Deep NetWork paper 参数初始化论文机器翻译 nlp 栏目系统网络繁體版

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概览改变residual connection与layer normalization的位置可以缓解深层Transformer难以优化的问题。作者比较了计算顺序（residual connection与layer normalization的位置）上的细微差别，并提出了一种参数初始化方法，该方法利用Lipschitz约束对Transformer的参数进行初始化。即使不调整原来的计算顺序，应用

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1. 论文浅尝 | BERT：Pre-training of Deep Bidirectional Transformers
2. 论文阅读：BERT：Pre-training of Deep Bidirectional Transformers for Language Understanding
3. 论文阅读笔记：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
4. Bert：论文阅读-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
5. BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding 论文翻译
6. 论文翻译：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
7. 论文笔记《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
8. 论文笔记：NAACL-HLT 2018 BERT Pre-training of Deep Bidirectional Transformers for
9. 论文学习《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
10. 深度学习论文精读（1）：ResNet

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