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Deep Learning Accelerators are prone to faults which manifest in the form of errors in Neural Networks.
Maximally fault tolerant neural networks
Neti, C., Schneider, M. H., and Young, E. D · 1992
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Determining and improving the fault tolerance of multilayer perceptrons in a pattern-recognition application
Emmerson, M. D., and Damper, R. I · 1993
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Training techniques to obtain fault-tolerant neural networks
Ching-Tai Chin, Mehrotra, K., Mohan, C. K., and Rankat, S · 1994
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Enhanced mlp performance and fault tolerance resulting from synaptic weight noise during training
Murray, A. F., and Edwards, P. J · 1994
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Training with noise is equivalent to tikhonov regularization
Bishop, C. M · 1995
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Complete and partial fault tolerance of feedforward neural nets
Phatak, D. S., and Koren, I · 1995
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On fault tolerant training of feedforward neural networks
Arad, B. S., and El-Amawy, A · 1997
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Penalty terms for fault tolerance
Edwards, P. J., and Murray, A. F · 1997
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Synthesis of fault-tolerant feedforward neural networks using minimax optimization
Deodhare, D., Vidyasagar, M., and Sathiya Keethi, S · 1998
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Analysis of fault tolerance in artificial neural networks
Piuri, V · 2001
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Investigating the fault tolerance of neural networks
Tchernev, E. B., Mulvaney, R. G., and Phatak, D. S · 2005
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A fault-tolerant regularizer for rbf networks
Leung, C., and Sum, J. P · 2008
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Why does unsupervised pre-training help deep learning?
Erhan, D., Courville, A., Bengio, Y., and Vincent, P · 2010
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Convergence and objective functions of some fault/noise-injection-based online learning algorithms for rbf networks
Ho, K. I. ., Leung, C., and Sum, J · 2010
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A defect-tolerant accelerator for emerging high-performance applications
Temam, O · 2012
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Dropout training as adaptive regularization
Wager, S., Wang, S., and Liang, P · 2013
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Cited alongside, same era.
Efficient processing of deep neural networks: A tutorial and survey
Sze, V., Chen, Y., Yang, T., and Emer, J. S · 2017
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Fault and error tolerance in neural networks: A review
Torres-Huitzil, C., and Girau, B · 2017
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A convergence analysis of gradient descent for deep linear neural networks, 2018
Arora, S., Cohen, N., Golowich, N., and Hu, W · 2018
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Regularizing multilayer perceptron for robustness
Dey, P., Nag, K., Pal, T., and Pal, N. R · 2018
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Admm-based algorithm for training fault tolerant rbf networks and selecting centers
Wang, H., Feng, R., Han, Z., and Leung, C · 2018
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Noise as a resource for computation and learning in networks of spiking neurons
Maass, W · 2014
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Adversarial autoencoders
Makhzani, A., Shlens, J., Jaitly, N., and Goodfellow, I · 2016
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The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm
Su, F., Yuan, P., Wang, Y., and Zhang, C · 2016
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Understanding error propagation in deep learning neural network (dnn) accelerators and applications
Li, G., Hari, S. K. S., Sullivan, M., Tsai, T., Pattabiraman, K., Emer, J., and Keckler, S. W · 2017
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Regularizing deep neural networks by noise: Its interpretation and optimization
Noh, H., You, T., Mun, J., and Han, B · 2017
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The difficulty of training deep architectures and the effect of unsupervised pre-training
Erhan, D., Manzagol, P.-A., Bengio, Y., Bengio, S., and Vincent, P
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Hoang, L.-H., Hanif, M. A., and Shafique, M · 2019
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Evaluating fault resiliency of compressed deep neural networks
Sabbagh, M., Gongye, C., Fei, Y., and Wang, Y · 2019
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On approximating dropout noise injection
Schluter, N · 2019
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Weight noise injection-based mlps with group lasso penalty: Asymptotic convergence and application to node pruning
Wang, J., Chang, Q., Chang, Q., Liu, Y., and Pal, N. R · 2019
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Fault tolerance of neural networks in adversarial settings
Duddu, V., Rajesh Pillai, N., Rao, D. V., and Balas, V. E · 2020
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