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SpikeSEE: An Energy-Efficient Dynamic Scenes Processing Framework for Retinal Prostheses
In this paper, we propose an energy-efficient dynamic scenes processing framework (SpikeSEE) that combines a spike representation encoding technique and a bio-inspired spiking recurrent neural network (SRNN) model to achieve intelligent processing and extreme low-power computation for retinal prostheses.
Attributed Rhetorical Structure Grammar for Domain Text Summarization
We have also shown that the knowledge based approach may be made more powerful by introducing grammar parsing and RST as inference engine.
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