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We present an approach to identify concise equations from data using a shallow neural network approach.
Model predictive control: Theory and practice—a survey
García, C. E., Prett, D. M., and Morari, M · 1989
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Regression shrinkage and selection via the lasso
Tibshirani, R · 1996
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Causality
Pearl, J · 2000
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Support vector regression
Basak, D., Pal, S., and Patranabis, D. C · 2007
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Covariate shift adaptation by importance weighted cross validation
Sugiyama, M., Krauledat, M., and Müller, K.-R · 2007
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Lazy sparse stochastic gradient descent for regularized multinomial logistic regression
Carpenter, B · 2008
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Dataset shift in machine learning
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D · 2009
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Causal discovery with continuous additive noise models
Peters, J., Mooij, J., Janzing, D., and Schölkopf, B · 2014
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Zaremba, W., Kurach, K., and Fergus, R · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Extrapolation and learning equations, 2016
Martius, G. and Lampert, C. H · 2016
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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
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Data-driven discovery of partial differential equations
Rudy, S. H., Brunton, S. L., Proctor, J. L., and Kutz, J. N · 2017
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Eureqa desktop software
Nutonian · 2018
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Cartpole-v0
OpenAI Gym · 2018
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