Resources

For additional information on the use of invertible neural networks, please refer to the following select papers:

• 4. Kingma, P. Dhariwal, Glow: Generative flow with invertible 1x1 convolutions, in: Advances in Neural Information Processing Systems, 2018, pp. 10215–10224. [Link]

• 12. Dinh, J. Sohl-Dickstein, S. Bengio, Density estimation using real nvp, arXiv preprint arXiv:1605.08803 [Link]

• 12. Dinh, D. Krueger, Y. Bengio, Nice: Non-linear independent components estimation, arXiv preprint arXiv:1410.8516. [Link]

• 25. Zhu, N. Zabaras, P.-S. Koutsourelakis, P. Perdikaris, Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data, Journal of Computational Physics 394 (2019) 56 – 81. [Link]