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Research map / Architectures and efficiency / RNNs and normalization

Recurrently Controlled Recurrent Networks

NeurIPS 2018 · Architectures and efficiency › RNNs and normalization · approach: Efficient recurrent architectures · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace a BiLSTM encoder with RCRN (ablation)classification models · Amazon Reviews (16 datasets, macro average)accuracy: +2.9%Helped
replace a 3L-BiLSTM encoder with RCRN (ablation)classification models · Amazon Reviews (16 datasets, macro average)accuracy: +1.1%Helped
replace a BiLSTM encoder with RCRN (ablation)sentiment classification · IMDbaccuracy: +1.9%Helped
replace a 3L-BiLSTM encoder with RCRN (ablation)sentiment classification · IMDbaccuracy: +1%Helped
replace the 3L-BiGRU encoder with GRU-based RCRN (ablation)R-NET · NarrativeQABLEU-1: 38.1 vs 34.9Helped
replace stacked recurrent layers with a controller-listener recurrent encodersequence encoders · 26 NLP datasetstask scoreHelped
add CUDA-level optimization to RCRN (ablation)RCRN · IMDb runtime benchmark, sequence lengths 16, 32, 64, 128, 256training time: 19, 29, 53, 101, 219 -> 10, 13, 21, 40, 78Helped

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