81 citations · 82 across the 3 of their papers we have counts for
3 papers
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…
Overcoming the vanishing gradient problem in plain recurrent networks
Yuhuang Hu, Adrian Huber, Jithendar Anumula +1
Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU)…
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…