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Researchers have proposed various activation functions.
A logical calculus of the ideas immanent in nervous activity
W. S. McCulloch and W. Pitts · 1943
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Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
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Neural networks for pattern recognition
C. M. Bishop et al · 1995
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A unified architecture for natural language processing: Deep neural networks with multitask learning
R. Collobert and J. Weston · 2008
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Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
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Replicated softmax: an undirected topic model
G. E. Hinton and R. R. Salakhutdinov · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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MNIST handwritten digit database
Y. LeCun and C. Cortes · 2010
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Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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Speech recognition with deep recurrent neural networks
A. Graves, A.-r. Mohamed, and G. Hinton · 2013
Earlier work this paper cites.
Learning activation functions to improve deep neural networks
F. Agostinelli, M. Hoffman, P. Sadowski, and P. Baldi · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
Cited alongside, same era.
The pascal visual object classes challenge: A retrospective
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Parametric exponential linear unit for deep convolutional neural networks
L. Trottier, P. Gigu, B. Chaib-draa, et al · 2017
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Leveraging filter correlations for deep model compression
P. Singh, V. K. Verma, P. Rai, and V. P. Namboodiri · 2018
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Cpwc: Contextual point wise convolution for object recognition
P. Mazumder, P. Singh, and V. Namboodiri · 2019
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Falf convnets: Fatuous auxiliary loss based filter-pruning for efficient deep cnns
P. Singh, V. S. R. Kadi, and V. P. Namboodiri · 2019
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Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Empirical evaluation of rectified activations in convolutional network
B. Xu, N. Wang, T. Chen, and M. Li · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Cited alongside, same era.
Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng
Cited in the paper.
Stability based filter pruning for accelerating deep cnns
P. Singh, V. S. R. Kadi, N. Verma, and V. P. Namboodiri · 2019
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Multi-layer pruning framework for compressing single shot multibox detector
P. Singh, R. Manikandan, N. Matiyali, and V. Namboodiri · 2019
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Accuracy booster: Performance boosting using feature map re-calibration
P. Singh, P. Mazumder, and V. P. Namboodiri · 2019
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Hetconv: Beyond homogeneous convolution kernels for deep cnns
P. Singh, V. K. Verma, P. Rai, and V. P. Namboodiri · 2019
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Hetconv: Heterogeneous kernel-based convolutions for deep cnns
P. Singh, V. K. Verma, P. Rai, and V. P. Namboodiri · 2019
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Play and prune: Adaptive filter pruning for deep model compression
P. Singh, V. K. Verma, P. Rai, and V. P. Namboodiri · 2019
Later among the works it cites.