R-Net

Uses scaled multiplicative attention from 'Attention Is All You Need' to reduce memory consumption compared to the original additive attention, improving training feasibility on limited hardware.

Implements variational dropout as per referenced papers, enhancing model generalization and reducing overfitting in recurrent neural networks.

Leverages bucketing and CudnnGRU to accelerate training, with benchmarks showing up to 10x speedup on GPUs like TITAN X compared to native implementations.

Automatically halves the learning rate when dev set loss increases, stabilizing training without manual intervention and preventing overshooting.