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Normalising flows are tractable probabilistic models that leverage the power of deep learning to describe a wide parametric family of distributions, all while remaining trainable using maximum likelihood.
The asymptotic efficiency of a maximum likelihood estimator
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Robust Statistics: The Approach Based on Influence Functions
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Robust statistical modeling using the t t distribution
Lange, K. L., Little, R. J. A., and Taylor, J. M. G · 1989
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Mixture density networks
Bishop, C. M · 1994
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Robustness of the Student t t based M-estimator
Lucas, A · 1997
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The MNIST database of handwritten digits
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Carnegie Mellon University motion capture database
CMU Graphics Lab · 2003
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Multivariate t t Distributions and Their Applications
Kotz, S. and Nadarajah, S · 2004
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Documentation mocap database HDM05
Müller, M., Röder, T., Clausen, M., Eberhardt, B., Krüger, B., and Weber, A · 2007
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Robust Statistics
Huber, P. J. and Ronchetti, E. M · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Robust inversion, dimensionality reduction, and randomized sampling
Aravkin, A., Friedlander, M. P., Herrmann, F. J., and van Leeuwen, T · 2012
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Scoring rules, Divergences and Information in Bayesian Machine Learning
Huszár, F · 2013
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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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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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Minimum entropy rate simplification of stochastic processes
Henter, G. E. and Kleijn, W. B · 2016
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Robust TTS duration modelling using DNNs
Henter, G. E., Ronanki, S., Watts, O., Wester, M., Wu, Z., and King, S · 2016
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Learning in implicit generative models
Mohamed, S. and Lakshminarayanan, B · 2016
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f-GAN: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R · 2016
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A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M · 2016
Optimal subsampling with influence functions
Ting, D. and Brochu, E · 2018
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Semi-conditional normalizing flows for semi-supervised learning
Atanov, A., Volokhova, A., Ashukha, A., Sosnovik, I., and Vetrov, D · 2019
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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Neural spline flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G · 2019
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MoGlow: Probabilistic and controllable motion synthesis using normalising flows
Henter, G. E., Alexanderson, S., and Beskow, J · 2019
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Fast, compact, and high quality LSTM-RNN based statistical parametric speech synthesizers for mobile devices
Zen, H., Agiomyrgiannakis, Y., Egberts, N., Henderson, F., and Szczepaniak, P · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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A recurrent variational autoencoder for human motion synthesis
Habibie, I., Holden, D., Schwarz, J., Yearsley, J., and Komura, T · 2017
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Investigating the use of recurrent motion modelling for speech gesture generation
Ferstl, Y. and McDonnell, R · 2018
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Jaini, P., Kobyzev, I., Brubaker, M., and Yu, Y · 2019
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Adaptive density estimation for generative models
Lucas, T., Shmelkov, K., Alahari, K., Schmid, C., and Verbeek, J · 2019
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Neural importance sampling
Müller, T., Mcwilliams, B., Rousselle, F., Gross, M., and Novák, J · 2019
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2019
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Do ImageNet classifiers generalize to ImageNet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
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Style-controllable speech-driven gesture synthesis using normalising flows
Alexanderson, S., Henter, G. E., Kucherenko, T., and Beskow, J · 2020
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Semi-supervised learning with normalizing flows
Izmailov, P., Kirichenko, P., Finzi, M., and Wilson, A. G · 2020
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Tails of Lipschitz triangular flows
Jaini, P., Kobyzev, I., Yu, Y., and Brubaker, M · 2020
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VideoFlow: A conditional flow-based model for stochastic video generation
Kumar, M., Babaeizadeh, M., Erhan, D., Finn, C., Levine, S., Dinh, L., and Kingma, D · 2020
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