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Normalizing flows are a popular class of models for approximating probability distributions.
Generating Long Sequences with Sparse Transformers
Child, R., Gray, S., Radford, A., and Sutskever, I. (2019) · 1904
Earlier work this paper cites.
Efficient estimators from a slowly converging robbins-monro process
Ruppert, D. (1988) · 1988
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New stochastic approximation type procedures
Polyak, B. (1990) · 1990
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Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
Huang, C.-W., Dinh, L., and Courville, A. (2020) · 2002
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A Taste of Topology
Runde, V. (2005) · 2005
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2009) · 2009
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Density estimation by dual ascent of the log-likelihood
Tabak, E. G. and Vanden-Eijnden, E. (2010) · 2010
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A Family of Nonparametric Density Estimation Algorithms
Tabak, E. G. and Turner, C. V. (2013) · 2013
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Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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NICE: Non-linear Independent Components Estimation
Dinh, L., Krueger, D., and Bengio, Y. (2015) · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Molecular Dynamics With Deterministic and Stochastic Numerical Methods
Leimkuhler, B. and Matthews, C. (2015) · 2015
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Variational Inference with Normalizing Flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
Earlier work this paper cites.
Black-box alpha-divergence Minimization
Hernández-Lobato, J. M., Li, Y., Rowland, M., Hernández-Lobato, D., Bui, T., and Turner, R. E. (2016) · 2016
Earlier work this paper cites.
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2017) · 2017
Earlier work this paper cites.
The Reversible Residual Network: Backpropagation Without Storing Activations
Gomez, A. N., Ren, M., Urtasun, R., and Grosse, R. B. (2017) · 2017
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Improved Training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017) · 2017
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Masked Autoregressive Flow for Density Estimation
Papamakarios, G., Pavlakou, T., and Murray, I. (2017) · 2017
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PixelSNAIL: An Improved Autoregressive Generative Model
Chen, X., Mishra, N., Rohaninejad, M., and Abbeel, P. (2018) · 2018
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Variational Rejection Sampling
Grover, A., Gummadi, R., Lazaro-Gredilla, M., Schuurmans, D., and Ermon, S. (2018) · 2018
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Averaging Weights Leads to Wider Optima and Better Generalization
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G. (2018) · 2018
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H. (2019) · 2019
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Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
Ardizzone, L., Mackowiak, R., Rother, C., and Köthe, U. (2020) · 2020
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Normalizing Flows With Multi-Scale Autoregressive Priors
Bhattacharyya, A., Mahajan, S., Fritz, M., Schiele, B., and Roth, S. (2020) · 2020
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Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Cornish, R., Caterini, A. L., Deligiannidis, G., and Doucet, A. (2020) · 2020
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Semi-Supervised Learning with Normalizing Flows
Izmailov, P., Kirichenko, P., Finzi, M., and Wilson, A. G. (2020) · 2020
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SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
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Glow: Generative Flow with Invertible 1x1 Convolutions
Kingma, D. P. and Dhariwal, P. (2018) · 2018
Cited alongside, same era.
Image Transformer
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., and Tran, D. (2018) · 2018
Cited alongside, same era.
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
van den Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., van den Driessche, G., Lockhart, E., Cobo, L. C., Stimberg, F., Casagrande, N., Grewe, D., Noury, S., Dieleman, S., Elsen, E., Kalchbrenner, N., Zen, H., Graves, A., King, H., Walters, T., Belov, D., and Hassabis, D. (2018) · 2018
Cited alongside, same era.
Resampled Priors for Variational Autoencoders
Bauer, M. and Mnih, A. (2019) · 2019
Cited alongside, same era.
Invertible Residual Networks
Behrmann, J., Grathwohl, W., Chen, R. T. Q., Duvenaud, D., and Jacobsen, J.-H. (2019) · 2019
Cited alongside, same era.
Residual Flows for Invertible Generative Modeling
Chen, R. T. Q., Behrmann, J., Duvenaud, D., and Jacobsen, J.-H. (2019) · 2019
Cited alongside, same era.
Nielsen, D., Jaini, P., Hoogeboom, E., Winther, O., and Welling, M. (2020) · 2020
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Targeted free energy estimation via learned mappings
Wirnsberger, P., Ballard, A. J., Papamakarios, G., Abercrombie, S., Racanière, S., Pritzel, A., Jimenez Rezende, D., and Blundell, C. (2020) · 2020
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Stochastic normalizing flows
Wu, H., Köhler, J., and Noe, F. (2020) · 2020
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Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
Behrmann, J., Vicol, P., Wang, K.-C., Grosse, R., and Jacobsen, J.-H. (2021) · 2021
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A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization
Campbell, A., Chen, W., Stimper, V., Hernandez-Lobato, J. M., and Zhang, Y. (2021) · 2021
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Densely connected normalizing flows
Grcić, M., Grubišić, I., and Šegvić, S. (2021) · 2021
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Stabilizing invertible neural networks using mixture models
Hagemann, P. and Neumayer, S. (2021) · 2021
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Kingma, D. P., Salimans, T., Poole, B., and Ho, J. (2021) · 2021
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Smooth Normalizing Flows
Köhler, J., Krämer, A., and Noé, F. (2021) · 2021
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Normalizing Flows for Probabilistic Modeling and Inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. (2021) · 2021
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UCI machine learning repository
Dheeru, D. and Taniskidou, E. K. (2022) · 2022
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