【論文筆記】LightRNN: Memory and Computation-Efficient Recurrent Neural Networks
論文主題：LightRNN 解決的問題是在 perplexity 差不多的情況下 減少模型大小(model size) + 加快訓練速度(computational complexity)。論文在多個基準數據集進行語言建模任務來評價 LightRNN這是一篇關於語言模型的文章。提出了使用二分量共享詞向量來減少模型大小參數數量來提高運行時間的論文。 前提知識： perplexity :Perplex
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相關文章
- 1. Attention and Augmented Recurrent Neural Networks
- 2. 019 Recurrent Neural Networks
- 3. 論文筆記：Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- 4. [論文筆記] Non-local Neural Networks
- 5. Recurrent Neural Networks 簡述
- 6. 論文筆記：Recurrent Convolutional Neural Networks for Text Classification
- 7. 《C-RNN-GAN: Continuous recurrent neural networks with adversarial training》論文筆記
- 8. Attention and Augmented Recurrent Neural Networks【譯文】
- 9. Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks論文筆記
- 10. 【Binarized Neural Networks】論文筆記