Gas recognition in E-nose system: A review
H Chen, D Huo, J Zhang - IEEE transactions on biomedical …, 2022 - ieeexplore.ieee.org
Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for
recognizing multivariate responses obtained by gas sensors in various applications. Over …
recognizing multivariate responses obtained by gas sensors in various applications. Over …
At the intersection of optics and deep learning: statistical inference, computing, and inverse design
Deep learning has been revolutionizing information processing in many fields of science
and engineering owing to the massively growing amounts of data and the advances in deep …
and engineering owing to the massively growing amounts of data and the advances in deep …
Training high-performance low-latency spiking neural networks by differentiation on spike representation
Abstract Spiking Neural Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …
Temporal efficient training of spiking neural network via gradient re-weighting
Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread
research interest because of their event-driven and energy-efficient characteristics. Still, it is …
research interest because of their event-driven and energy-efficient characteristics. Still, it is …
Incorporating learnable membrane time constant to enhance learning of spiking neural networks
Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …
temporal information processing capability, low power consumption, and high biological …
Attention spiking neural networks
Brain-inspired spiking neural networks (SNNs) are becoming a promising energy-efficient
alternative to traditional artificial neural networks (ANNs). However, the performance gap …
alternative to traditional artificial neural networks (ANNs). However, the performance gap …
Deep directly-trained spiking neural networks for object detection
Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode
information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown …
information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown …
A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration
Abstract Spiking Neural Network (SNN) has been recognized as one of the next generation
of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only …
of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only …
Temporal-wise attention spiking neural networks for event streams classification
How to effectively and efficiently deal with spatio-temporal event streams, where the events
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
Optimal conversion of conventional artificial neural networks to spiking neural networks
Spiking neural networks (SNNs) are biology-inspired artificial neural networks (ANNs) that
comprise of spiking neurons to process asynchronous discrete signals. While more efficient …
comprise of spiking neurons to process asynchronous discrete signals. While more efficient …