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Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-efficiently process spatio-temporal information through discrete and sparse spikes, thereby receiving considerable attention.
Networks of spiking neurons: the third generation of neural network models
Maass, W · 1997
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Error-backpropagation in temporally encoded networks of spiking neurons
Bohte, S. M., Kok, J. N., and La Poutre, H · 2002
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Spiking neuron models: Single neurons, populations, plasticity
Gerstner, W. and Kistler, W. M · 2002
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Network in network
Lin, M., Chen, Q., and Yan, S · 2014
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A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Guo, C., Nakamura, Y., et al · 2014
Earlier work this paper cites.
Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Diehl, P. U. and Cook, M · 2015
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S.-C., and Pfeiffer, M · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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A low power, fully event-based gesture recognition system
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al · 2017
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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Cifar10-dvs: an event-stream dataset for object classification
Li, H., Liu, H., Ji, X., Li, G., and Shi, L · 2017
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Large-scale evolution of image classifiers
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y. L., Tan, J., Le, Q. V., and Kurakin, A · 2017
Earlier work this paper cites.
Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Rueckauer, B., Lungu, I.-A., Hu, Y., Pfeiffer, M., and Liu, S.-C · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
Bender, G., Kindermans, P.-J., Zoph, B., Vasudevan, V., and Le, Q · 2018
Cited alongside, same era.
Loihi: A neuromorphic manycore processor with on-chip learning
Davies, M., Srinivasa, N., Lin, T.-H., Chinya, G., Cao, Y., Choday, S. H., Dimou, G., Joshi, P., Imam, N., Jain, S., et al · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., and Dean, J · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Comparing snns and rnns on neuromorphic vision datasets: similarities and differences
He, W., Wu, Y., Deng, L., Li, G., Wang, H., Tian, Y., Ding, W., Wang, W., and Xie, Y · 2020
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Sp-nas: Serial-to-parallel backbone search for object detection
Jiang, C., Xu, H., Zhang, W., Liang, X., and Li, Z · 2020
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Synaptic plasticity dynamics for deep continuous local learning (decolle)
Kaiser, J., Mostafa, H., and Neftci, E · 2020
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Revisiting batch normalization for training low-latency deep spiking neural networks from scratch
Kim, Y. and Panda, P · 2020
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Enabling spike-based backpropagation for training deep neural network architectures
Lee, C., Sarwar, S. S., Panda, P., Srinivasan, G., and Roy, K · 2020
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Random search and reproducibility for neural architecture search
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Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018
Cited alongside, same era.
Proxylessnas: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 2019
Cited alongside, same era.
Detnas: Backbone search for object detection
Chen, Y., Yang, T., Zhang, X., Meng, G., Xiao, X., and Sun, J · 2019
Cited alongside, same era.
Searching for a robust neural architecture in four gpu hours
Dong, X. and Yang, Y · 2019
Cited alongside, same era.
Darts: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2019
Cited alongside, same era.
Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks
Neftci, E. O., Mostafa, H., and Zenke, F · 2019
Cited alongside, same era.
Li, L. and Talwalkar, A · 2020
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T2fsnn: deep spiking neural networks with time-to-first-spike coding
Park, S., Kim, S., Na, B., and Yoon, S · 2020
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Cream of the crop: Distilling prioritized paths for one-shot neural architecture search
Peng, H., Du, H., Yu, H., Li, Q., Liao, J., and Fu, J · 2020
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Weight-sharing neural architecture search: A battle to shrink the optimization gap, 2020
Xie, L., Chen, X., Bi, K., Wei, L., Xu, Y., Chen, Z., Wang, L., Xiao, A., Chang, J., Zhang, X., and Tian, Q · 2020
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Greedynas: Towards fast one-shot nas with greedy supernet
You, S., Huang, T., Yang, M., Wang, F., Qian, C., and Zhang, C · 2020
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One-shot neural architecture search via novelty driven sampling
Zhang, M., Li, H., Pan, S., Liu, T., and Su, S. W · 2020
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Advancing neuromorphic computing with loihi: A survey of results and outlook
Davies, M., Wild, A., Orchard, G., Sandamirskaya, Y., Guerra, G. A. F., Joshi, P., Plank, P., and Risbud, S. R · 2021
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Hr-nas: Searching efficient high-resolution neural architectures with lightweight transformers
Ding, M., Lian, X., Yang, L., Wang, P., Jin, X., Lu, Z., and Luo, P · 2021
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Optimizing deeper spiking neural networks for dynamic vision sensing
Kim, Y. and Panda, P · 2021
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Low-activity supervised convolutional spiking neural networks applied to speech commands recognition
Pellegrini, T., Zimmer, R., and Masquelier, T · 2021
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Lighttrack: Finding lightweight neural networks for object tracking via one-shot architecture search
Yan, B., Peng, H., Wu, K., Wang, D., Fu, J., and Lu, H · 2021
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Going deeper with directly-trained larger spiking neural networks
Zheng, H., Wu, Y., Deng, L., Hu, Y., and Li, G · 2021
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Neural architecture search for spiking neural networks
Kim, Y., Li, Y., Park, H., Venkatesha, Y., and Panda, P · 2022
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