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Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences.

, , and . NIPS, page 3882-3890. (2016)

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Delta Networks for Optimized Recurrent Network Computation., , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 2584-2593. PMLR, (2017)Live demonstration: Event-driven real-time spoken digit recognition system., , , , and . ISCAS, page 1. IEEE, (2017)DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction., , , , and . ITSC, page 1-6. IEEE, (2020)Multi-channel Attention for End-to-End Speech Recognition., , , , and . INTERSPEECH, page 17-21. ISCA, (2018)Lip Reading Deep Network Exploiting Multi-Modal Spiking Visual and Auditory Sensors., , , and . ISCAS, page 1-5. IEEE, (2019)Sensor Transformation Attention Networks., , , , and . CoRR, (2017)Deep Neural Networks and Hardware Systems for Event-driven Data.. ETH Zurich, Zürich, Switzerland, (2017)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/168865).Attention-driven Multi-sensor Selection., , , , and . IJCNN, page 1-8. IEEE, (2019)Temporal sequence recognition in a self-organizing recurrent network., , , and . EBCCSP, page 1-4. IEEE, (2016)A curriculum learning method for improved noise robustness in automatic speech recognition., , and . EUSIPCO, page 548-552. IEEE, (2017)