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Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations.
Approximation by superposition of sigmoidal functions
Gybenko, G · 1989
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Approximation properties of a multilayered feedforward artificial neural network
Mhaskar, H. N · 1993
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Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Improved variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
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Pixel recurrent neural networks
Van Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Variational lossy autoencoder
Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2017
Cited alongside, same era.
Density estimation using real nvp
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Pixelvae: A latent variable model for natural images
Gulrajani, I., Kumar, K., Ahmed, F., Taiga, A. A., Visin, F., Vazquez, D., and Courville, A · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
Cited alongside, same era.
Neural ordinary differential equations
Chen, T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
Cited alongside, same era.
Waic, but why? generative ensembles for robust anomaly detection
Choi, H., Jang, E., and Alemi, A. A · 2018
Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Betterncourt, J., Sutskever, I., and Duvenaud, D · 2019
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
Ho, J., Chen, X., Srinivas, A., Duan, Y., and Abbeel, P · 2019
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Emerging convolutions for generative normalizing flows
Hoogeboom, E., Van Den Berg, R., and Welling, M · 2019
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Neural importance sampling
Müller, T., McWilliams, B., Rousselle, F., Gross, M., and Novák, J · 2019
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Hybrid models with deep and invertible features
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2019
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Waveglow: A flow-based generative network for speech synthesis
Prenger, R., Valle, R., and Catanzaro, B · 2019
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Cited alongside, same era.
i-revnet: Deep invertible networks
Jacobsen, J.-H., Smeulders, A., and Oyallon, E · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
Cited alongside, same era.
Invertible residual networks
Behrmann, J., Grathwohl, W., Chen, R. T., Duvenaud, D., and Jacobsen, J.-H · 2019
Cited alongside, same era.
Residual flows for invertible generative modeling
Chen, T. Q., Behrmann, J., Duvenaud, D. K., and Jacobsen, J.-H · 2019
Cited alongside, same era.
Augmented neural odes
Dupont, E., Doucet, A., and Teh, Y. W · 2019
Cited alongside, same era.
Neural spline flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G · 2019
Cited alongside, same era.
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Mintnet: Building invertible neural networks with masked convolutions
Song, Y., Meng, C., and Ermon, S · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
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Pointflow: 3d point cloud generation with continuous normalizing flows
Yang, G., Huang, X., Hao, Z., Liu, M.-Y., Belongie, S., and Hariharan, B · 2019
Later among the works it cites.
Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
Huang, C.-W., Dinh, L., and Courville, A · 2020
Closest in time.
Variational autoencoders with normalizing flow decoders, 2020
Morrow, R. and Chiu, W.-C · 2020
Closest in time.