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20192023
most citedTCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks

26 citations · 82 across the 11 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.NE2019★ 7 cited

Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition

Jibin Wu, Emre Yilmaz, Malu Zhang +2

Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi…

cs.NE2019★ 7 cited

Neural Population Coding for Effective Temporal Classification

Zihan Pan, Jibin Wu, Yansong Chua +2

Neural encoding plays an important role in faithfully describing the temporally rich patterns, whose instances include human speech and environmental sounds. For tasks that involve…

cs.SD2019★ 5 cited

An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks

Zihan Pan, Yansong Chua, Jibin Wu +3

Auditory front-end is an integral part of a spiking neural network (SNN) when performing auditory cognitive tasks. It encodes the temporal dynamic stimulus, such as speech and audi…

cs.NE2019★ 16 cited

A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks

Jibin Wu, Yansong Chua, Malu Zhang +3

Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures. However, due to the non-differentia…

cs.NE2019★ 11 cited

Deep Spiking Neural Network with Spike Count based Learning Rule

Jibin Wu, Yansong Chua, Malu Zhang +3

Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synch…