Ainsworth SK, Foti NJ, Lee AK, Fox EB (2018) oi-vae: output interpretable vaes for nonlinear group factor analysis. In: international conference on machine learning, pp. 119–128

Approximation by superpositions of a sigmoidal function - Cybenko, G. (Math Control Signals Syst, 1989)

Regularization paths for generalized linear models via coordinate descent - Friedman, J.; Hastie, T.; Tibshirani, R. (J Stat Softw, 2010)

Projection pursuit regression - Friedman, J. H.; Stuetzle, W. (J Am Stat Assoc, 1981)

Generalized additive models - Hastie, T. J.; Tibshirani, R. J. (CRC Press, 1990)

Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprintarXiv:1412.6980

Leclerc G, Vartak M, Fernandez RC, Kraska T, Madden S (2018) Smallify: Learning network size while training. arXiv preprintarXiv:1806.03723

Lym S, Choukse E, Zangeneh S, Wen W, Erez M, Shanghavi S (2019) Prunetrain: Gradual structured pruning from scratch for faster neural network training. arXiv preprintarXiv:1901.09290

Adaptive lasso echo state network based on modified bayesian information criterion for nonlinear system modeling - Qiao, J.; Wang, L.; Yang, C. (Neural Comput Appl, 2018)

Monotone operators and the proximal point algorithm - Rockafellar, R. T. (SIAM J Control Optim, 1976)

Group sparse regularization for deep neural networks - Scardapane, S.; Comminiello, D.; Hussain, A.; Uncini, A. (Neurocomputing, 2017)

Simonyan K, Vedaldi A, Zisserman A (2013) Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprintarXiv:1312.6034

Design and application of a variable selection method for multilayer perceptron neural network with lasso - Sun, K.; Huang, S. -. H.; Wong, D. S. -. H.; Jang, S. -. S. (IEEE Trans Neural Netw Learn Syst, 2017)

Tank A, Cover I, Foti NJ, Shojaie A, Fox EB (2017) An interpretable and sparse neural network model for nonlinear granger causality discovery. arXiv preprintarXiv:1711.08160

Regression shrinkage and selection via the lasso - Tibshirani, R. (J R Stat Soc Ser B (Methodol), 1996)

Vaughan J, Sudjianto A, Brahimi E, Chen J, Nair VN (2018) Explainable neural networks based on additive index models. arXiv preprintarXiv:1806.01933

A novel pruning algorithm for smoothing feedforward neural networks based on group lasso method - Wang, J.; Xu, C.; Yang, X.; Zurada, J. M. (IEEE Trans Neural Netw Learn Syst, 2018)

Wang Y, Su H, Zhang B, Hu X (2018b) Interpret neural networks by identifying critical data routing paths. In: proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8906–8914

Generalized additive models for gigadata: modeling the uk black smoke network daily data - Wood, S. N.; Li, Z.; Shaddick, G.; Augustin, N. H. (J Am Stat Assoc, 2017)

Adaptive elastic echo state network for multivariate time series prediction - Xu, M.; Han, M. (IEEE Trans Cybern, 2016)

Model selection and estimation in regression with grouped variables - Yuan, M.; Lin, Y. (J R Stat Soc Ser B (Stat Methodol), 2006)

Feature selection for neural networks using group lasso regularization - Zhang, H.; Wang, J.; Sun, Z.; Zurada, J. M.; Pal, N. R. (IEEE Trans Knowl Data Eng, 2019)

The adaptive lasso and its oracle properties - Zou, H. (J Am Stat Assoc, 2006)