Publication: Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
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Neil, D., Pfeiffer, M., & Liu, S.-C. (2016, December 10). Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences. Advances in Neural Information Processing Systems 29 (NIPS 2016). Neural Information and Processing Systems (NIPS), Barcelona. https://papers.nips.cc/paper/6310-phased-lstm-accelerating-recurrent-network-training-for-long-or-event-based-sequences.pdf
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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 irregularly sampled data triggered by events generated in continuous time by sensors or other neurons. Such data can occur, for example, when the input comes from novel event-driven artificial sensors that generate sparse, asynchronous streams of events or from multiple conventional sensors with different update intervals. In this work, we introduce the Phased L
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- Neil, D
- Pfeiffer, M
- Liu, S-C
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Neil, D., Pfeiffer, M., & Liu, S.-C. (2016, December 10). Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences. Advances in Neural Information Processing Systems 29 (NIPS 2016). Neural Information and Processing Systems (NIPS), Barcelona. https://papers.nips.cc/paper/6310-phased-lstm-accelerating-recurrent-network-training-for-long-or-event-based-sequences.pdf
