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Found 3 papers, 1 papers with code
Discrete Event, Continuous Time RNNs
The CT-GRU arises by interpreting the gates of a GRU as selecting a time scale of memory, and the CT-GRU generalizes the GRU by incorporating multiple time scales of memory and performing context-dependent selection of time scales for information storage and retrieval.
State-Denoised Recurrent Neural Networks
Attractor dynamics are incorporated into the hidden state to `clean up' representations at each step of a sequence.
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Machine learning promises methods that generalize well from finite labeled data.
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