Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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TLDR
The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNN's typically require many fewer operations and are the better candidates to process spatio-temporal data.About:
This article is published in Neural Networks.The article was published on 2019-03-01 and is currently open access. It has received 756 citations till now. The article focuses on the topics: Spiking neural network & Artificial neural network.read more
Citations
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Two sparsities are better than one: unlocking the performance benefits of sparse-sparse networks
TL;DR: In this article , complementary sparsity is proposed to reduce the computational cost of neural networks by two orders of magnitude by combining weight sparsity and activation sparsity, which can achieve up to 100× improvement in throughput and energy efficiency.
Journal ArticleDOI
Modeling learnable electrical synapse for high precision spatio-temporal recognition
TL;DR: Zhang et al. as mentioned in this paper proposed a refined neural model ECLIF, where membrane potentials propagate to neighbor neurons via convolution operations, and trained them using a back-propagation-through-time algorithm.
Journal ArticleDOI
Event-Based Semantic Segmentation With Posterior Attention
Zexi Jia,Kaichao You,Weihua He,Yang Tian,Yongxiang Feng,Yaoyuan Wang,Xu Jia,Yihang Lou,Jingyi Zhang,Guoqiu Li,Ziyang Zhang +10 more
TL;DR: Jia et al. as discussed by the authors proposed a posterior attention module that adjusts the standard attention by the prior knowledge provided by event data, which can be readily plugged into many segmentation backbones.
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One Transistor One Electrolyte-Gated Transistor for Supervised Learning in SNNs
TL;DR:
Proceedings ArticleDOI
Aplicação Do Deep Learning Para Análise De Fissuras Em Testes De Quedas De Pelotas
Marconi Junio Henriques Magnani,Thiago R. Souza,Jorge José Fernandes Filho,Marco Antonio de Souza Leite Cuadros +3 more
TL;DR: In this article, a controle rigoroso da qualidade das pelotas for aplicacao das mesmas no processo industrial is presented.
References
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TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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