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We consider the identifiability theory of probabilistic models and establish sufficient conditions under which the representations learned by a very broad family of conditional energy-based models are unique in function space, up to a simple transformation.
Causal discovery with general non-linear relationships using non-linear ica
Monti, R. P., Zhang, K., and Hyvarinen, A. (2019) · 1904
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Flow contrastive estimation of energy-based models
Gao, R., Nijkamp, E., Kingma, D. P., Xu, Z., Dai, A. M., and Wu, Y. N. (2019) · 1912
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Lin, G., Milan, A., Shen, C., and Reid, I. (2017) · 1934
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Kingma, D. P. and Welling, M. (2013) · 2013
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P. (2018) · 2018
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Monti, R. P. and Hyvärinen, A. (2018) · 2018
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Deep Energy Estimator Networks
Saremi, S., Mehrjou, A., Schölkopf, B., and Hyvärinen, A. (2018) · 2018
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Neural spline flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G. (2019) · 2019
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Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Khemakhem, I., Kingma, D. P., Monti, R. P., and Hyvärinen, A. (2020) · 2020
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