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Recurrent Neural Networks in the Eye of Differential Equations
To understand the fundamental trade-offs between training stability, temporal dynamics and architectural complexity of recurrent neural networks~(RNNs), we directly analyze RNN architectures using numerical methods of ordinary differential equations~(ODEs).
Unification of Recurrent Neural Network Architectures and Quantum Inspired Stable Design
We show that most of the existing proposals of RNN architectures belong to different orders of $n$-$t$-ORNNs.
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