no code implementations • 22 Jan 2019 • Matthew Thornton, Jithendar Anumula, Shih-Chii Liu
Finally, by employing a temporal curriculum learning schedule for the g-LSTM, we can reduce the convergence time of the equivalent LSTM network on long sequences.
no code implementations • 27 Sep 2018 • Matthew Thornton, Jithendar Anumula, Shih-Chii Liu
Finally, by employing a temporal curriculum learning schedule for the g-LSTM, we can reduce the convergence time of the equivalent LSTM network on long sequences.
no code implementations • ICLR 2018 • Yuhuang Hu, Adrian Huber, Jithendar Anumula, Shih-Chii Liu
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) deliver promising results in many sequence learning tasks through sophisticated network designs.
no code implementations • ICLR 2018 • Stefan Braun, Daniel Neil, Enea Ceolini, Jithendar Anumula, Shih-Chii Liu
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 performance of the system substantially.