Research map / Architectures and efficiency / RNNs and normalization
Recurrently Controlled Recurrent Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| 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 · IMDb | accuracy: +1.9% | Helped |
| replace a 3L-BiLSTM encoder with RCRN (ablation) | sentiment classification · IMDb | accuracy: +1% | Helped |
| replace the 3L-BiGRU encoder with GRU-based RCRN (ablation) | R-NET · NarrativeQA | BLEU-1: 38.1 vs 34.9 | Helped |
| replace stacked recurrent layers with a controller-listener recurrent encoder | sequence encoders · 26 NLP datasets | task score | Helped |
| add CUDA-level optimization to RCRN (ablation) | RCRN · IMDb runtime benchmark, sequence lengths 16, 32, 64, 128, 256 | training time: 19, 29, 53, 101, 219 -> 10, 13, 21, 40, 78 | Helped |
Closest papers by meaning
- Quasi-Recurrent Neural Networks (ICLR 2017 · similarity 0.88)
- Focused Hierarchical RNNs for Conditional Sequence Processing (ICML 2018 · similarity 0.88)
- Parallelizing Linear Recurrent Neural Nets Over Sequence Length (ICLR 2018 · similarity 0.88)
- Improving Bilinear RNN with Closed-loop Control (NeurIPS 2025 · similarity 0.86)
- A Tensor Decomposition Perspective on Second-order RNNs (ICML 2024 · similarity 0.86)
- Exploring the Promise and Limits of Real-Time Recurrent Learning (ICLR 2024 · similarity 0.86)
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