[31] J. Behrmann, D. K. Duvenaud, and J.-H. Jacobsen. Invertible Residual Networks. In Proc. Int. Conf. on Machine Learning (ICML), 2018.
[32] R. T. Q. Chen, J. Behrmann, D. K. Duvenaud, and J.-H. Jacobsen. Residual Flows for Invertible Generative Modeling. In Proc. Conf. on Neural Information Processing Systems (NeurIPS), 2019.
[33] R. M. Neal. Annealed Importance Sampling. Statistics and Computing, 11:125-139, 1998.
[34] Y. Burda, R. B. Grosse, and R. Salakhutdinov. Accurate and Conservative Estimates of MRF Log-Likelihood using Reverse Annealing. volume abs/1412.8566, 2015.
[35] W. Grathwohl, R. T. Q. Chen, J. Bettencourt, I. Sutskever, and D. K. Duvenaud. FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models. In Int. Conf. on Learning Representations (ICLR), 2018.
[36] Y. Song and S. Ermon. Improved Techniques for Training Score-Based Generative Models. In Proc. Conf. on Neural Information Processing Systems (NeurIPS), 2020.
[37] A. Krizhevsky. Learning Multiple Layers of Features from Tiny Images. 2009.
[38] A. V. D. Oord, N. Kalchbrenner, L. Espeholt, K. Kavukcuoglu, O. Vinyals, and A. Graves. Conditional Image Generation with PixelCNN Decoders. In Proc. Conf. on Neural Information Processing Systems (NeurIPS), 2016.
[39] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Proc. Conf. on Neural Information Processing Systems (NeurIPS), 2019.
[40] A. Griewank and A. Walther. Evaluating Derivatives - Principles and Techniques of Algorithmic Differentiation, Second Edition. In Frontiers in applied mathematics, 2000.
[41] A. M Nguyen, J. Clune, Y. Bengio, A. Dosovitskiy, and J. Yosinski. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. In Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR), 2016.
[42] Z. Liu, P. Luo, X. Wang, and X. Tang. Deep Learning Face Attributes in the Wild. In Proc. Int. Conf. on Computer Vision (ICCV), December 2015.
[43] T. Clanuwat, M. Bober-Irizar, A. Kitamoto, A. Lamb, K. Yamamoto, and D. Ha. Deep Learning for Classical Japanese Literature. In Proc. Conf. on Neural Information Processing Systems (NeurIPS), 2018.
[44] T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forr'e, and Max Welling. Self Normalizing Flows. In Proc. Int. Conf. on Machine Learning (ICML), 2020.
[45] C.-H. Chao, W.-F. Sun, B.-W. Cheng, Y.-C. Lo, C.-C. Chang, Y.-L. Liu, Y.-L. Chang, C.-P. Chen, and C.-Y. Lee. Denoising Likelihood Score Matching for Conditional Score-based Data Generation. In Proc. Int. Conf. on Learning Representations (ICLR), 2022.
[46] Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. CoRR, abs/1412.6980, 2014.
[47] I. Loshchilov and F. Hutter. Decoupled Weight Decay Regularization. In Proc. Int. Conf. on Learning Representations (ICLR), 2017.
[48] M. Ning, E. Sangineto, A. Porrello, S. Calderara, and R. Cucchiara. Input Perturbation Reduces Exposure Bias in Diffusion Models. In Proc. Int. Conf. on Machine Learning (ICML), 2023.