Towards Effective Training of Robust Spiking Recurrent Neural Networks Under General Input Noise via Provable Analysis
Author:
Affiliation:
1. School of Computer Science and Engineering, Sun Yat-sen University,Guangzhou,China
2. Umeå University,Umeå,Sweden
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10323590/10323543/10323789.pdf?arnumber=10323789
Reference33 articles.
1. DCT-SNN: Using DCT to Distribute Spatial Information over Time for Low-Latency Spiking Neural Networks
2. Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks
3. T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
4. A time-to-first-spike coding and conversion aware training for energy-efficient deep spiking neural network processor design
5. Adam: A method for stochastic optimization;kingma;Proceedings of 3rd International Conference on Learning Represen-tations (ICLR),0
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