1.1k citations · 2.2k across the 33 of their papers we have counts for
3 papers · 1 filter
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…
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…
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
Daniel Neil, Michael Pfeiffer, Shih-Chii Liu
Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for extracting patterns from temporal sequences. However, current RNN models are ill-suited to process irre…