1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2019
Reducing state updates via Gaussian-gated LSTMs
Matthew Thornton, Jithendar Anumula, Shih-Chii Liu
Recurrent neural networks can be difficult to train on long sequence data due to the well-known vanishing gradient problem. Some architectures incorporate methods to reduce RNN sta…
cs.LG2017★ 1 cited
Sensor Transformation Attention Networks
Stefan Braun, Daniel Neil, Enea Ceolini +2
Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal and spatial attention mechanisms into neural networks increases the…