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Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions.
Dream to control: Learning behaviors by latent imagination
Hafner, D., Lillicrap, T., Ba, J., and Norouzi, M · 1912
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The well-calibrated bayesian
Dawid, A. P · 1982
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The comparison and evaluation of forecasters
DeGroot, M. H. and Fienberg, S. E · 1983
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Natural gradient works efficiently in learning
Amari, S.-I · 1998
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A view of the em algorithm that justifies incremental, sparse, and other variants
Neal, R. M. and Hinton, G. E · 1998
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An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K · 1999
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Reinforcement learning of motor skills with policy gradients
Peters, J. and Schaal, S · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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A recurrent latent variable model for sequential data
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., and Bengio, Y · 2015
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Draw: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., and Wierstra, D · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Learning structured output representation using deep conditional generative models
Sohn, K., Lee, H., and Yan, X · 2015
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Embed to control: A locally linear latent dynamics model for control from raw images
Watter, M., Springenberg, J., Boedecker, J., and Riedmiller, M · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
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Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Edwards, H. and Storkey, A · 2016
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Towards conceptual compression
Gregor, K., Besse, F., Rezende, D. J., Danihelka, I., and Wierstra, D · 2016
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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 · 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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Discrete variational autoencoders
Rolfe, J. T · 2016
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Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O · 2016
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A note on the evaluation of generative models
Theis, L., Oord, A. v. d., and Bethge, M · 2016
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Alemi, A. A., Poole, B., Fischer, I., Dillon, J. V., Saurous, R. A., and Murphy, K · 2017
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Latent space oddity: on the curvature of deep generative models
Leveraging the exact likelihood of deep latent variable models
Mattei, P.-A. and Frellsen, J · 2018
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Peng, X. B., Kanazawa, A., Toyer, S., Abbeel, P., and Levine, S · 2018
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Rezende, D. J. and Viola, F · 2018
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Distribution matching in variational inference
Rosca, M., Lakshminarayanan, B., and Mohamed, S · 2018
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Amortized inference regularization
Shu, R., Bui, H. H., Zhao, S., Kochenderfer, M. J., and Ermon, S · 2018
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Arvanitidis, G., Hansen, L. K., and Hauberg, S · 2017
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Latent constraints: Learning to generate conditionally from unconditional generative models
Engel, J., Hoffman, M., and Roberts, A · 2017
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Deep visual foresight for planning robot motion
Finn, C. and Levine, S · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
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Student-t variational autoencoder for robust density estimation
Takahashi, H., Iwata, T., Yamanaka, Y., Yamada, M., and Yagi, S · 2018
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A general and adaptive robust loss function
Barron, J. T · 2019
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Rate-regularization and generalization in vaes
Bozkurt, A., Esmaeili, B., Tristan, J.-B., Brooks, D. H., Dy, J. G., and van de Meent, J.-W · 2019
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Improved conditional vrnns for video prediction
Castrejon, L., Ballas, N., and Courville, A · 2019
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Diagnosing and enhancing vae models
Dai, B. and Wipf, D · 2019
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From variational to deterministic autoencoders
Ghosh, P., Sajjadi, M. S., Vergari, A., Black, M., and Schölkopf, B · 2019
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Model-predictive policy learning with uncertainty regularization for driving in dense traffic
Henaff, M., Canziani, A., and LeCun, Y · 2019
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Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
Lee, A. X., Nagabandi, A., Abbeel, P., and Levine, S · 2019
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Don’t blame the elbo! a linear vae perspective on posterior collapse
Lucas, J., Tucker, G., Grosse, R. B., and Norouzi, M · 2019
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Biva: A very deep hierarchy of latent variables for generative modeling
Maaløe, L., Fraccaro, M., Liévin, V., and Winther, O · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Expressive body capture: 3d hands, face, and body from a single image
Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A., Tzionas, D., and Black, M. J · 2019
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Skew-fit: State-covering self-supervised reinforcement learning
Pong, V. H., Dalal, M., Lin, S., Nair, A., Bahl, S., and Levine, S · 2019
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Jukebox: A generative model for music
Dhariwal, P., Jun, H., Payne, C., Kim, J. W., Radford, A., and Sutskever, I · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Variational variance: Simple and reliable predictive variance parameterization
Stirn, A. and Knowles, D. A · 2020
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