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Despite the success of neural networks (NNs), there is still a concern among many over their "black box" nature.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., et al · 1958
Earlier work this paper cites.
The stone-weierstrass property in banach algebras
Katznelso., Y., and Rudin, W · 1961
Earlier work this paper cites.
Asymptotic behavior of m m -estimators of p p regression parameters when p 2 / n p^{2}/n is large
Portnoy, S · 1984
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Hornik, K., et al · 1989
Earlier work this paper cites.
A theory of networks for approximation and learning
Poggio, T., and Girosi, F · 1989
Earlier work this paper cites.
Note on second–order polynomial regression models
Chatterjee, S., and Greenwood, A. G · 1990
Earlier work this paper cites.
Ridge polynomial networks
Shin, Y., and Ghosh, J · 1995
Earlier work this paper cites.
Are Artificial Neural Networks Black Boxes?
Benitez, J. M., et al · 1997
Earlier work this paper cites.
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., and Seung, H. S · 1999
Earlier work this paper cites.
Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping
Caruana, R., et al · 2000
Earlier work this paper cites.
The elements of statistical learning
Friedman, J., et al · 2001
Earlier work this paper cites.
Fast trigonometric functions using intel’s sse2 instructions. intel tech. rep., available online at: http://www.weblearn.hs-bremen, 2004
Nyland, L., and Snyder, M · 2004
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A study on neural networks using taylor series expansion of sigmoid activation function
Temurtas, F., et al · 2004
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Choon, O. H., et al · 2008
Cited alongside, same era.
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DasGupta, A · 2008
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Response Surface Methodology: Process and Product Optimization Using Designed Experiments
Myers, R., et al · 2009
Cited alongside, same era.
Latent variable models for dimensionality reduction
Zhang, Z., and Jordan, M. I · 2009
Toward a shared vision for cancer genomic data
Grossman, R. L., et al · 2016
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Software alchemy: Turning complex statistical computations into embrassingly-parallel ones
Matloff, N · 2016
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Statistical Regression and Classification: From Linear Models to Machine Learning
Matloff, N · 2017
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Forward Thinking: Building Deep Random Forests
Miller, K., et al · 2017
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Opening the black box of deep neural networks via information
Shwartz-Ziv, R., and Tishby, N · 2017
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Telgarsky, M · 2017
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Deep forest: Towards an alternative to deep neural networks
Zhou, Z., and Feng, J · 2017
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polyreg: Polynomial Regression
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Chollet, F., and Allaire, J · 2018
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