CMRU restores gradient flow in BMRU via cumulative state updates with skip-connections through time, yielding better convergence and benchmark performance while retaining quantized persistent memory.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages=
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Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
CMRU restores gradient flow in BMRU via cumulative state updates with skip-connections through time, yielding better convergence and benchmark performance while retaining quantized persistent memory.