[ICLR19] ORDERED NEURONS: INTEGRATING TREE STRUCTURES INTO RECURRENT NEURAL NETWORKS
本篇是ICLR2019的兩篇Best Paper之一。另一篇：THE LOTTERY TICKET HYPOTHESIS： FINDING SPARSE, TRAINABLE NEURAL NETWORKS ABSTRACT 自然語言是分層結構的：較小的單元(例如短語)嵌套在較大的單元(例如子句)中。當較大的組件結束時，嵌套在其中的所有較小的組件也必須關閉。雖然標準的LSTM體系結構允許不同的神經
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相關文章
- 1. [ICLR19] ORDERED NEURONS: INTEGRATING TREE STRUCTURES INTO RECURRENT NEURAL NETWORKS
- 2. (zhuan) Recurrent Neural Network
- 3. 讀書筆記26：adding attentiveness to the neurons in recurrent neural networks
- 4. [ICLR2017]Tree-Structured Deconding with Doubly-Recurrent Neural Networks
- 5. [ICLR19] THE LOTTERY TICKET HYPOTHESIS： FINDING SPARSE, TRAINABLE NEURAL NETWORKS
- 6. The Unreasonable Effectiveness of Recurrent Neural Networks
- 7. Attention and Augmented Recurrent Neural Networks
- 8. 019 Recurrent Neural Networks
- 9. RNN(Recurrent Neural Networks)
- 10. Recurrent Neural Networks