論文精度（1）-- Lipschitz constrained parameter initialization for deep transformers
概覽 改變residual connection與layer normalization的位置可以緩解深層Transformer難以優化的問題。 作者比較了計算順序（residual connection與layer normalization的位置）上的細微差別，並提出了一種參數初始化方法，該方法利用Lipschitz約束對Transformer的參數進行初始化。 即使不調整原來的計算順序，應用
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相關文章
- 1. 論文淺嘗 | BERT：Pre-training of Deep Bidirectional Transformers
- 2. 論文閱讀：BERT：Pre-training of Deep Bidirectional Transformers for Language Understanding
- 3. 論文閱讀筆記：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 4. Bert：論文閱讀-BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 5. BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding 論文翻譯
- 6. 論文翻譯：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- 7. 論文筆記《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
- 8. 論文筆記：NAACL-HLT 2018 BERT Pre-training of Deep Bidirectional Transformers for
- 9. 論文學習《BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding》
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