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20162026
most citedLearning Open Set Network with Discriminative Reciprocal Points

241 citations · 1.4k across the 50 of their papers we have counts for

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6 papers · 1 filter

cs.NE2024★ 2 cited

Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learning

Ding Chen, Peixi Peng, Tiejun Huang +1

With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a prom…

cs.NE2022★ 15 cited

Deep Reinforcement Learning with Spiking Q-learning

Ding Chen, Peixi Peng, Tiejun Huang +1

With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption. It provides a prom…

cs.NE2021★ 49 cited

Pruning of Deep Spiking Neural Networks through Gradient Rewiring

Yanqi Chen, Zhaofei Yu, Wei Fang +2

Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are usually…

cs.NE2021★ 13 cited

Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks

Jianhao Ding, Zhaofei Yu, Yonghong Tian +1

Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry. The most efficient way to train dee…

cs.NE2021

Deep Residual Learning in Spiking Neural Networks

Wei Fang, Zhaofei Yu, Yanqi Chen +3

Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Conside…

cs.NE2020

Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks

Wei Fang, Zhaofei Yu, Yanqi Chen +3

Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility.…