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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Journal ArticleDOI
Relaxation LIF: A gradient-based spiking neuron for direct training deep spiking neural networks
TL;DR: In this article , a gradient-based spiking neuron named Relaxation Leaky Integrate-and-Fire (RLIF) was designed to enable the direct training of deep SNNs.
Journal ArticleDOI
Applications of Machine Learning in Knowledge Management System: A Comprehensive Review
TL;DR: A knowledge management–machine learning framework is conceived based on the review done on each algorithm earlier and to bridge the gap between the two literatures, highlighting how machine learning algorithm can play a part in different areas of knowledge management.
Proceedings ArticleDOI
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks
TL;DR: In this paper, the authors propose techniques for knowledge distillation in spiking neural networks for the task of image classification, which is a model compression technique that enables transferring the learning of a large machine learning model to a smaller model with minimal loss in performance.
Journal ArticleDOI
Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
TL;DR: In this article , a knowledge distillation method was proposed to improve the performance of SNN through constructing deeper structures in a high-throughput fashion, with potential usage for light and efficient brain-inspired computing of practical scenarios.
Proceedings ArticleDOI
An Analysis on Deep Learning Approach Performance in Classifying Big Data Set
TL;DR: Evaluating the capability of deep learning in analyzing big data sets revealed that deep learning has outperformed SVM in classifying big data set and can be categorized as one of the best machine learning approaches to be used in decision analysis process.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
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.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
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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