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Two apparently unrelated fields -- normalizing flows and causality -- have recently received considerable attention in the machine learning community.
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G. (2019a) · 1906
Earlier work this paper cites.
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G. (2019b) · 1906
Earlier work this paper cites.
Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. (2019) · 1912
Earlier work this paper cites.
IX. On the problem of the most efficient tests of statistical hypotheses
Neyman, J. and Pearson, E. S. (1933) · 1933
Earlier work this paper cites.
Approximation capabilities of multilayer feedforward networks
Hornik, K. (1991) · 1991
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, A. and Pajunen, P. (1999) · 1999
Earlier work this paper cites.
Causation, Prediction, and Search
Spirtes, P., Glymour, C. N., Scheines, R., and Heckerman, D. (2000) · 2000
Earlier work this paper cites.
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models
Khemakhem, I., Monti, R. P., Kingma, D. P., and Hyvärinen, A. (2020b) · 2002
Earlier work this paper cites.
Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms
Dornhege, G., Blankertz, B., Curio, G., and Müller, K.-R. (2004) · 2004
Earlier work this paper cites.
Deep structural causal models for tractable counterfactual inference
Pawlowski, N., Castro, D. C., and Glocker, B. (2020) · 2006
Earlier work this paper cites.
Wehenkel, A. and Louppe, G. (2020) · 2006
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J., and Schölkopf, B. (2009) · 2009
Earlier work this paper cites.
On the identifiability of the post-nonlinear causal model
Zhang, K. and Hyvarinen, A. (2009) · 2009
Earlier work this paper cites.
Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python
Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., and Ghosh, S. S. (2011) · 2011
Earlier work this paper cites.
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y., Washio, T., Hoyer, P. O., and Bollen, K. (2011) · 2011
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Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models
Hyvärinen, A. and Smith, S. M. (2013) · 2013
Cited alongside, same era.
Machine learning for neuroimaging with scikit-learn
Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., and Varoquaux, G. (2014) · 2014
Cited alongside, same era.
NICE: Non-linear Independent Components Estimation
Dinh, L., Krueger, D., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
Causal Discovery with Continuous Additive Noise Models
Peters, J., Mooij, J., Janzing, D., and Schölkopf, B. (2014) · 2014
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Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination
Zhang, K., Huang, B., Zhang, J., Glymour, C., and Schölkopf, B. (2017) · 2017
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Cause-effect inference by comparing regression errors
Bloebaum, P., Janzing, D., Washio, T., Shimizu, S., and Schölkopf, B. (2018) · 2018
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Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A. (2018) · 2018
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Glow: Generative Flow with Invertible 1x1 Convolutions
Kingma, D. P. and Dhariwal, P. (2018) · 2018
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Masked Autoregressive Flow for Density Estimation
Papamakarios, G., Pavlakou, T., and Murray, I. (2018) · 2018
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MADE: Masked Autoencoder for Distribution Estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H. (2015) · 2015
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Variational Inference with Normalizing Flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
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Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2016) · 2016
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Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
Hyvärinen, A. and Morioka, H. (2016) · 2016
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Improving Variational Inference with Inverse Autoregressive Flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M. (2016) · 2016
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Distinguishing cause from effect using observational data: Methods and benchmarks
Mooij, J. M., Peters, J., Janzing, D., Zscheischler, J., and Schölkopf, B. (2016) · 2016
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fmriprep: a robust preprocessing pipeline for functional mri
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Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
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Causal discovery with general non-linear relationships using non-linear ICA
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Cingulate Cortex
Vogt, B. A. (2019) · 2019
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Normalizing Flows: An Introduction and Review of Current Methods
Kobyzev, I., Prince, S. J. D., and Brubaker, M. A. (2020) · 2020
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Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI
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