1. Agarwal, R., Frosst, N., Zhang, X., Caruana, R., Hinton, G.E.: Neural additive models: interpretable machine learning with neural nets. arXiv (2020)
2. Altintas, Y., Kersting, P., Biermann, D., Budak, E., Denkena, B., Lazoglu, I.: Virtual process systems for part machining operations. CIRP Annals 63(2), 585–605 (2014)
3. Belbute-Peres, F., Economon, T.D., Kolter, J.Z.: Combining differentiable PDE solvers and graph neural networks for fluid flow prediction. arXiv (2020)
4. Benouamer, M.O., Michelucci, D.: Bridging the gap between CSG and BREP via a triple ray representation. In: Proceedings of the Fourth ACM Symposium on Solid Modeling and Applications, pp. 68–79 (1997)
5. Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc. (2011). https://proceedings.neurips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf