A fuzzy encoder-decoder architecture reduces information loss in spiking Q-learning and narrows the performance gap with conventional multi-modal networks on HighwayEnv driving tasks.
High-performance temporal reversible spiking neural networks with o(l) training memory and o(1) inference cost
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Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving
A fuzzy encoder-decoder architecture reduces information loss in spiking Q-learning and narrows the performance gap with conventional multi-modal networks on HighwayEnv driving tasks.
- SVL: Empowering Spiking Neural Networks for Efficient 3D Open-World Understanding