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Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread research interest because of their event-driven and energy-efficient characteristics.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Going deeper with directly-trained larger spiking neural networks
Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, and Guoqi Li · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Peter U. Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, and Shih Chii Liu · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Peter U. Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, and Shih Chii Liu · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware
Peter U Diehl, Guido Zarrella, Andrew Cassidy, Bruno U Pedroni, and Emre Neftci · 2016
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Convolutional networks for fast, energy-efficient neuromorphic computing
Steven K Esser, Paul A Merolla, John V Arthur, Andrew S Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J Berg, Jeffrey L McKinstry, Timothy Melano, Davis R Barch, et al · 2016
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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
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Theory and tools for the conversion of analog to spiking convolutional neural networks
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, and Michael Pfeiffer · 2016
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Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware
Peter U Diehl, Guido Zarrella, Andrew Cassidy, Bruno U Pedroni, and Emre Neftci · 2016
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Convolutional networks for fast, energy-efficient neuromorphic computing
Steven K Esser, Paul A Merolla, John V Arthur, Andrew S Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J Berg, Jeffrey L McKinstry, Timothy Melano, Davis R Barch, et al · 2016
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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
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Theory and tools for the conversion of analog to spiking convolutional neural networks
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, and Michael Pfeiffer · 2016
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Cifar10-dvs: an event-stream dataset for object classification
Hongmin Li, Hanchao Liu, Xiangyang Ji, Guoqi Li, and Luping Shi · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Cifar10-dvs: an event-stream dataset for object classification
Hongmin Li, Hanchao Liu, Xiangyang Ji, Guoqi Li, and Luping Shi · 2017
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Loihi: A neuromorphic manycore processor with on-chip learning
Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, et al · 2018
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On the insufficiency of existing momentum schemes for stochastic optimization
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, and Sham Kakade · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
Cited alongside, same era.
How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
Cited alongside, same era.
Going deeper in spiking neural networks: Vgg and residual architectures
Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy · 2018
Cited alongside, same era.
Slayer: Spike layer error reassignment in time
Sumit Bam Shrestha and Garrick Orchard · 2018
Cited alongside, same era.
Spatio-temporal backpropagation for training high-performance spiking neural networks
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi · 2018
Cited alongside, same era.
Loihi: A neuromorphic manycore processor with on-chip learning
Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, et al · 2018
Nitin Rathi and Kaushik Roy · 2020
Later among the works it cites.
Convolutional spiking neural networks for spatio-temporal feature extraction
Ali Samadzadeh, Fatemeh Sadat Tabatabaei Far, Ali Javadi, Ahmad Nickabadi, and Morteza Haghir Chehreghani · 2020
Later among the works it cites.
Temporal spike sequence learning via backpropagation for deep spiking neural networks
Wenrui Zhang and Peng Li · 2020
Later among the works it cites.
Rethinking the performance comparison between snns and anns
Lei Deng, Yujie Wu, Xing Hu, Ling Liang, Yufei Ding, Guoqi Li, Guangshe Zhao, Peng Li, and Yuan Xie · 2020
Later among the works it cites.
Optimal conversion of conventional artificial neural networks to spiking neural networks
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Cited alongside, same era.
On the insufficiency of existing momentum schemes for stochastic optimization
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, and Sham Kakade · 2018
Cited alongside, same era.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
Cited alongside, same era.
How does batch normalization help optimization?
Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, and Aleksander Madry · 2018
Cited alongside, same era.
Going deeper in spiking neural networks: Vgg and residual architectures
Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy · 2018
Cited alongside, same era.
Slayer: Spike layer error reassignment in time
Sumit Bam Shrestha and Garrick Orchard · 2018
Cited alongside, same era.
Spatio-temporal backpropagation for training high-performance spiking neural networks
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi · 2018
Cited alongside, same era.
Shikuang Deng and Shi Gu · 2020
Later among the works it cites.
Deep spiking neural network: Energy efficiency through time based coding
Bing Han and Kaushik Roy · 2020
Later among the works it cites.
Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
Bing Han, Gopalakrishnan Srinivasan, and Kaushik Roy · 2020
Later among the works it cites.
Efficient processing of spatio-temporal data streams with spiking neural networks
Alexander Kugele, Thomas Pfeil, Michael Pfeiffer, and Elisabetta Chicca · 2020
Later among the works it cites.
Nitin Rathi and Kaushik Roy · 2020
Later among the works it cites.
Convolutional spiking neural networks for spatio-temporal feature extraction
Ali Samadzadeh, Fatemeh Sadat Tabatabaei Far, Ali Javadi, Ahmad Nickabadi, and Morteza Haghir Chehreghani · 2020
Later among the works it cites.
Temporal spike sequence learning via backpropagation for deep spiking neural networks
Wenrui Zhang and Peng Li · 2020
Later among the works it cites.
Deep residual learning in spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian · 2021
Later among the works it cites.
Visual explanations from spiking neural networks using inter-spike intervals
Youngeun Kim and Priyadarshini Panda · 2021
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Privatesnn: Fully privacy-preserving spiking neural networks
Youngeun Kim, Yeshwanth Venkatesha, and Priyadarshini Panda · 2021
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Liaf-net: Leaky integrate and analog fire network for lightweight and efficient spatiotemporal information processing
Zhenzhi Wu, Hehui Zhang, Yihan Lin, Guoqi Li, Meng Wang, and Ye Tang · 2021
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Backpropagated neighborhood aggregation for accurate training of spiking neural networks
Yukun Yang, Wenrui Zhang, and Peng Li · 2021
Later among the works it cites.
Going deeper with directly-trained larger spiking neural networks
Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, and Guoqi Li · 2021
Later among the works it cites.
Deep residual learning in spiking neural networks
Wei Fang, Zhaofei Yu, Yanqi Chen, Tiejun Huang, Timothée Masquelier, and Yonghong Tian · 2021
Later among the works it cites.
Visual explanations from spiking neural networks using inter-spike intervals
Youngeun Kim and Priyadarshini Panda · 2021
Later among the works it cites.
Privatesnn: Fully privacy-preserving spiking neural networks
Youngeun Kim, Yeshwanth Venkatesha, and Priyadarshini Panda · 2021
Later among the works it cites.
Liaf-net: Leaky integrate and analog fire network for lightweight and efficient spatiotemporal information processing
Zhenzhi Wu, Hehui Zhang, Yihan Lin, Guoqi Li, Meng Wang, and Ye Tang · 2021
Later among the works it cites.
Backpropagated neighborhood aggregation for accurate training of spiking neural networks
Yukun Yang, Wenrui Zhang, and Peng Li · 2021
Later among the works it cites.
Neural architecture search for spiking neural networks
Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, and Priyadarshini Panda · 2022
Closest in time.
Neuromorphic data augmentation for training spiking neural networks
Yuhang Li, Youngeun Kim, Hyoungseob Park, Tamar Geller, and Priyadarshini Panda · 2022
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Neural architecture search for spiking neural networks
Youngeun Kim, Yuhang Li, Hyoungseob Park, Yeshwanth Venkatesha, and Priyadarshini Panda · 2022
Closest in time.
Neuromorphic data augmentation for training spiking neural networks
Yuhang Li, Youngeun Kim, Hyoungseob Park, Tamar Geller, and Priyadarshini Panda · 2022
Closest in time.