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Normalizing Flows are generative models which produce tractable distributions where both sampling and density evaluation can be efficient and exact.
M. Spivak, Calculus on Manifolds: A Modern Approach to Classical Theorems of Advanced Calculus. Print, 1965
1965
Earlier work this paper cites.
V. Arnold, Ordinary Differential Equations . The MIT Press, 1978
1978
Earlier work this paper cites.
J. Gregory and R. Delbourgo, “Piecewise rational quadratic interpolation to monotonic data,” IMA Journal of Numerical Analysis , vol. 2, no. 2, pp. 123–130, 1982
1982
Earlier work this paper cites.
B. Oksendal, Stochastic Differential Equations (3rd Ed.): An Introduction with Applications . Berlin, Heidelberg: Springer-Verlag, 1992
1992
Earlier work this paper cites.
A. Katok and B. Hasselblatt, Introduction to the modern theory of dynamical systems . Cambridge University Press, New York, 1995
1995
Earlier work this paper cites.
J. Suykens, H. Verrelst, and J. Vandewalle, “On-Line Learning Fokker-Planck Machine,” Neural Processing Letters , vol. 7, pp. 81–89, 1998
1998
Earlier work this paper cites.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proceedings of the 8th International Conference on Computer Vision, ICCV , 2001
2001
Earlier work this paper cites.
J. Arango and A. Gómez, “Diffeomorphisms as time one maps,” Aequationes Math. , vol. 64, pp. 304–314, 2002
2002
Earlier work this paper cites.
C. Villani, Topics in optimal transportation (Graduate Studies in Mathematics 58) . American Mathematical Society, Providence, RI, 2003
2003
Earlier work this paper cites.
V. Bogachev, A. Kolesnikov, and K. Medvedev, “Triangular transformations of measures,” Sbornik Math. , vol. 196, no. 3-4, pp. 309–335, 2005
2005
Earlier work this paper cites.
K. V. Medvedev, “Certain properties of triangular transformations of measures,” Theory Stoch. Process. , vol. 14(30), pp. 95–99, 2008
2008
Earlier work this paper cites.
D. Koller and N. Friedman, Probabilistic Graphical Models . Massachusetts: MIT Press, 2009
2009
Earlier work this paper cites.
J. Agnelli, M. Cadeiras, E. Tabak, T. Cristina, and E. Vanden-Eijnden, “Clustering and classification through normalizing flows in feature space,” Multiscale Modeling and Simulation , vol. 8, pp. 1784–1802, 2010
2010
Earlier work this paper cites.
E. G. Tabak and E. Vanden-Eijnden, “Density Estimation by Dual Ascent of the Log-Likelihood,” Communications in Mathematical Sciences , vol. 8, no. 1, pp. 217–233, 2010
2010
Earlier work this paper cites.
M. Welling and Y. W. Teh, “Bayesian Learning via Stochastic Gradient Langevin Dynamics,” in ICML , 2011
2011
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier Nonlinearities Improve Neural Network Acoustic Models,” in ICML , 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
E. G. Tabak and C. V. Turner, “A Family of Nonparametric Density Estimation Algorithms,” Communications on Pure and Applied Mathematics , vol. 66, no. 2, pp. 145–164, 2013
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative Adversarial Nets,” Advances in Neural Information Processing Systems , 2014
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proceedings of the 2nd International Conference on Learning Representations, ICLR , 2014
2014
Earlier work this paper cites.
P. M. Laurence, R. J. Pignol, and E. G. Tabak, “Constrained density estimation,” Proceedings of the 2011 Wolfgang Pauli Institute conference on energy and commodity trading, Springer Verlag , pp. 259–284, 2014
2014
Earlier work this paper cites.
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Józefowicz, and S. Bengio, “Generating sentences from a continuous space,” in CoNLL , 2015
2015
Earlier work this paper cites.
L. Dinh, D. Krueger, and Y. Bengio, “NICE: Non-linear Independent Components Estimation,” in ICLR Workshop , 2015
2015
Earlier work this paper cites.
M. Germain, K. Gregor, I. Murray, and H. Larochelle, “MADE: Masked Autoencoder for Distribution Estimation,” in ICML , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification,” in ICCV , 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in ICML , 2015
2015
Earlier work this paper cites.
D. J. Rezende and S. Mohamed, “Variational Inference with Normalizing Flows,” in ICML , 2015
2015
Earlier work this paper cites.
T. Salimans, A. Diederik, D. P. Kingma, and M. Welling, “Markov Chain Monte Carlo and Variational Inference: Bridging the Gap,” in ICML , 2015
2015
Earlier work this paper cites.
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in Proceedings of the 32nd International Conference on Machine Learning, ICML , 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
——, “Deep Residual Learning for Image Recognition,” in CVPR , 2016
2016
Earlier work this paper cites.
D. P. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever, and M. Welling, “Improved Variational Inference with Inverse Autoregressive Flow,” in NIPS , 2016
2016
Earlier work this paper cites.
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved Techniques for Training GANs,” in NIPS , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Z. M. Ziegler and A. M. Rush, “Latent Normalizing Flows for Discrete Sequences,” in Proceedings of the 36th International Conference on Machine Learning, ICML , 2019
2016
Earlier work this paper cites.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein Generative Adversarial Networks,” in ICML , 2017
2017
Cited alongside, same era.
L. Dinh, J. Sohl-Dickstein, and S. Bengio, “Density Estimation using Real NVP,” in ICLR , 2017
2017
Cited alongside, same era.
D. Dua and C. Graff, “UCI Machine Learning Repository,” 2017
2017
Cited alongside, same era.
W. E, “A proposal on machine learning via dynamical systems,” Communications in Mathematics and Statistics , vol. 5, pp. 1–11, 2017
2017
Cited alongside, same era.
A. N. Gomez, M. Ren, R. Urtasun, and R. B. Grosse, “The Reversible Residual Network: Backpropagation Without Storing Activations,” Advances in Neural Information Processing Systems , 2017
2017
Cited alongside, same era.
E. Dupont, A. Doucet, and Y. W. Teh, “Augmented Neural ODEs,” Advances in Neural Information Processing Systems , 2019
2019
Closest in time.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, “Cubic-spline flows,” in Workshop on Invertible Neural Networks and Normalizing Flows, ICML , 2019
2019
Closest in time.
——, “Neural Spline Flows,” Advances in Neural Information Processing Systems , 2019
2019
Closest in time.
2019
Closest in time.
2019
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L. Hasenclever, J. M. Tomczak, R. Van Den Berg, and M. Welling, “Variational Inference with Orthogonal Normalizing Flows,” in Workshop on Bayesian Deep Learning, NIPS , 2017
2017
Cited alongside, same era.
G. Papamakarios, T. Pavlakou, and I. Murray, “Masked Autoregressive Flow for Density Estimation,” in NIPS , 2017
2017
Cited alongside, same era.
A. Spantini, D. Bigoni, and Y. Marzouk, “Inference via low-dimensional couplings,” Journal of Machine Learning Research , vol. 19, 03 2017
2017
Cited alongside, same era.
J. Tomczak and M. Welling, “Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow,” Benelearn , 2017
2017
Cited alongside, same era.
B. L. Trippe and R. E. Turner, “Conditional Density Estimation with Bayesian Normalising Flows,” in Workshop on Bayesian Deep Learning, NIPS , 2017
2017
Cited alongside, same era.
A. van den Oord, Y. Li, I. Babuschkin, K. Simonyan, O. Vinyals, K. Kavukcuoglu, G. van den Driessche, E. Lockhart, L. C. Cobo, F. Stimberg, N. Casagrande, D. Grewe, S. Noury, S. Dieleman, E. Elsen, N. Kalchbrenner, H. Zen, A. Graves, H. King, T. Walters, D. Belov, and D. Hassabis, “Parallel wavenet: Fast high-fidelity speech synthesis,” in ICML , 2017
2017
Cited alongside, same era.
K. Wang, C. Gou, Y. Duan, Y. Lin, X. Zheng, and F. yue Wang, “Generative adversarial networks: introduction and outlook,” IEEE/CAA Journal of Automatica Sinica , vol. 4, pp. 588–598, 2017
2017
Cited alongside, same era.
Closest in time.
W. Grathwohl, R. T. Q Chen, J. Bettencourt, I. Sutskever, and D. Duvenaud, “FFJORD: Free-form continuous dynamics for scalable reversible generative models,” in ICLR , 2019
2019
Closest in time.
J. Ho, X. Chen, A. Srinivas, Y. Duan, and P. Abbeel, “Flow++: Improving flow-based generative models with variational dequantization and architecture design,” in Proceedings of the 36th International Conference on Machine Learning, ICML , 2019
2019
Closest in time.
E. Hoogeboom, R. V. D. Berg, and M. Welling, “Emerging Convolutions for Generative Normalizing Flows,” in Proceedings of the 36th International Conference on Machine Learning, ICML , 2019
2019
Closest in time.
E. Hoogeboom, J. W. Peters, R. van den Berg, and M. Welling, “Integer discrete flows and lossless compression,” in NeurIPS , 2019
2019
Closest in time.
2019
Closest in time.
P. Jaini, K. A. Selby, and Y. Yu, “Sum-of-squares polynomial flow,” in Proceedings of the 36th International Conference on Machine Learning, ICML , 5 2019
2019
Closest in time.
——, “An Introduction to Variational Autoencoders,” arXiv preprint, arXiv:1906.02691 , 2019
2019
Closest in time.
J. Köhler, L. Klein, and F. Noé, “Equivariant flows: sampling configurations for multi-body systems with symmetric energies,” in Workshop on Machine Learning and the Physical Sciences, NeurIPS , 2019
2019
Closest in time.
M. Kumar, M. Babaeizadeh, D. Erhan, C. Finn, S. Levine, L. Dinh, and D. Kingma, “VideoFlow: A Flow-Based Generative Model for Video,” in Workshop on Invertible Neural Nets and Normalizing Flows, ICML , 2019
2019
Closest in time.
A. Liutkus, U. Simsekli, S. Majewski, A. Durmus, and F.-R. Stöter, “Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions,” in Proceedings of the 36th International Conference on Machine Learning, ICML , 2019
2019
Closest in time.
2019
Closest in time.
B. Mazoure, T. Doan, A. Durand, J. Pineau, and R. D. Hjelm, “Leveraging exploration in off-policy algorithms via normalizing flows,” in 3rd Conference on Robot Learning (CoRL 2019) , 2019
2019
Closest in time.
P. Nadeem Ward, A. Smofsky, and A. Joey Bose, “Improving exploration in soft-actor-critic with normalizing flows policies,” in Workshop on Invertible Neural Networks and Normalizing Flows, ICML , 2019
2019
Closest in time.
F. Noé, S. Olsson, J. Köhler, and H. Wu, “Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning,” Science , vol. 365, 2019
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in ICASSP , 2019
2019
Closest in time.
A. Touati, H. Satija, J. Romoff, J. Pineau, and P. Vincent, “Randomized value functions via multiplicative normalizing flows,” in UAI2019: Conference on Uncertainty in Artificial Intelligence , 2019
2019
Closest in time.
D. Tran, K. Vafa, K. Agrawal, L. Dinh, and B. Poole, “Discrete Flows: Invertible Generative Models of Discrete Data,” in ICLR Workshop , 2019
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.