{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T16:45:01Z","timestamp":1756572301280,"version":"3.37.3"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s10994-021-06092-6","type":"journal-article","created":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T21:02:22Z","timestamp":1637269342000},"page":"1597-1620","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving sequential latent variable models with autoregressive flows"],"prefix":"10.1007","volume":"111","author":[{"given":"Joseph","family":"Marino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1996-3264","authenticated-orcid":false,"given":"Jiawei","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephan","family":"Mandt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"unstructured":"Agrawal, S., & Dukkipati, A. (2016). Deep variational inference without pixel-wise reconstruction. arXiv preprint arXiv:161105209","key":"6092_CR1"},{"doi-asserted-by":"crossref","unstructured":"Agustsson, E., Minnen, D., Johnston, N., Balle, J., Hwang, S. J., & Toderici, G. (2020). Scale-space flow for end-to-end optimized video compression. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 8503\u20138512).","key":"6092_CR2","DOI":"10.1109\/CVPR42600.2020.00853"},{"unstructured":"de\u00a0Almeida\u00a0Freitas, F., Peres, S. M., de\u00a0Moraes\u00a0Lima, C. A., & Barbosa, F. V. (2014). Grammatical facial expressions recognition with machine learning. In The Twenty-Seventh International Flairs Conference.","key":"6092_CR3"},{"issue":"3","key":"6092_CR4","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/TASSP.1979.1163237","volume":"27","author":"B Atal","year":"1979","unstructured":"Atal, B., & Schroeder, M. (1979). Predictive coding of speech signals and subjective error criteria. IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(3), 247\u2013254.","journal-title":"IEEE Transactions on Acoustics, Speech, and Signal Processing"},{"key":"6092_CR5","first-page":"217","volume":"1","author":"HB Barlow","year":"1961","unstructured":"Barlow, H. B., et al. (1961). Possible principles underlying the transformation of sensory messages. Sensory communication, 1, 217\u2013234.","journal-title":"Sensory communication"},{"doi-asserted-by":"crossref","unstructured":"Barsocchi, P., Crivello, A., La\u00a0Rosa, D., & Palumbo, F. (2016). A multisource and multivariate dataset for indoor localization methods based on wlan and geo-magnetic field fingerprinting. In 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp 1\u20138). IEEE.","key":"6092_CR6","DOI":"10.1109\/IPIN.2016.7743678"},{"unstructured":"Bayer, J., & Osendorfer, C. (2014). Learning stochastic recurrent networks. In NeurIPS 2014 Workshop on Advances in Variational Inference.","key":"6092_CR7"},{"unstructured":"Bengio, Y., & Bengio, S. (2000). Modeling high-dimensional discrete data with multi-layer neural networks. In Advances in Neural Information Processing Systems (pp. 400\u2013406).","key":"6092_CR8"},{"unstructured":"Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. Wiley.","key":"6092_CR9"},{"unstructured":"Chen, S. S., & Gopinath, R. A. (2001). Gaussianization. In Advances in Neural Information Processing Systems (pp. 423\u2013429).","key":"6092_CR10"},{"unstructured":"Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018). Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Advances in Neural Information Processing Systems (pp. 4754\u20134765).","key":"6092_CR11"},{"unstructured":"Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., & Bengio, Y. (2015). A recurrent latent variable model for sequential data. In Advances in Neural Information processing Systems (pp. 2980\u20132988).","key":"6092_CR12"},{"unstructured":"Clevert, D. A., Unterthiner, T., & Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:151107289","key":"6092_CR13"},{"unstructured":"Deco, G., & Brauer, W. (1995). Higher order statistical decorrelation without information loss. In Advances in Neural Information Processing Systems (pp. 247\u2013254)","key":"6092_CR14"},{"unstructured":"Denton, E., & Fergus, R. (2018). Stochastic video generation with a learned prior. In International Conference on Machine Learning (pp. 1182\u20131191).","key":"6092_CR15"},{"unstructured":"Dinh, L., Krueger, D., & Bengio, Y. (2015). Nice: Non-linear independent components estimation. In International Conference on Learning Representations.","key":"6092_CR16"},{"unstructured":"Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2017). Density estimation using real nvp. In International Conference on Learning Representations.","key":"6092_CR17"},{"unstructured":"Durkan, C., Bekasov, A., Murray, I., & Papamakarios, G. (2019). Neural spline flows. In Advances in Neural Information Processing Systems.","key":"6092_CR18"},{"unstructured":"Ebert, F., Finn, C., Lee, A. X., & Levine, S. (2017). Self-supervised visual planning with temporal skip connections. In Conference on Robot Learning.","key":"6092_CR19"},{"unstructured":"Fraccaro, M., S\u00f8nderby, S. K., Paquet, U., & Winther, O. (2016). Sequential neural models with stochastic layers. In Advances in Neural Information Processing Systems (pp. 2199\u20132207).","key":"6092_CR20"},{"unstructured":"Frey, B. J., Hinton, G. E., & Dayan, P. (1996). Does the wake-sleep algorithm produce good density estimators? In Advances in Neural Information Processing Systems (pp. 661\u2013667).","key":"6092_CR21"},{"issue":"397","key":"6092_CR22","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1080\/01621459.1987.10478427","volume":"82","author":"JH Friedman","year":"1987","unstructured":"Friedman, J. H. (1987). Exploratory projection pursuit. Journal of the American statistical association, 82(397), 249\u2013266.","journal-title":"Journal of the American statistical association"},{"issue":"11","key":"6092_CR23","doi-asserted-by":"publisher","first-page":"e1000211","DOI":"10.1371\/journal.pcbi.1000211","volume":"4","author":"K Friston","year":"2008","unstructured":"Friston, K. (2008). Hierarchical models in the brain. PLoS Computational Biology, 4(11), e1000211.","journal-title":"PLoS Computational Biology"},{"unstructured":"Gan, Z., Li, C., Henao, R., Carlson, D. E., & Carin, L. (2015). Deep temporal sigmoid belief networks for sequence modeling. In Advances in Neural Information Processing Systems.","key":"6092_CR24"},{"unstructured":"Gemici, M., Hung, C. C., Santoro, A., Wayne, G., Mohamed, S., Rezende, D. J., Amos, D., & Lillicrap, T. (2017) .Generative temporal models with memory. arXiv preprint arXiv:170204649","key":"6092_CR25"},{"unstructured":"Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:13080850","key":"6092_CR26"},{"unstructured":"Ha, D., & Schmidhuber, J. (2018). Recurrent world models facilitate policy evolution. In Advances in Neural Information Processing Systems (pp. 2450\u20132462).","key":"6092_CR27"},{"unstructured":"Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., & Davidson, J. (2019). Learning latent dynamics for planning from pixels. In International Conference on Machine Learning (pp. 2555\u20132565).","key":"6092_CR28"},{"key":"6092_CR29","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctv14jx6sm","volume-title":"Time series analysis","author":"JD Hamilton","year":"2020","unstructured":"Hamilton, J. D. (2020). Time series analysis. Princeton University Press."},{"doi-asserted-by":"crossref","unstructured":"He, J., Lehrmann, A., Marino, J., Mori, G., & Sigal, L. (2018). Probabilistic video generation using holistic attribute control. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 452\u2013467).","key":"6092_CR30","DOI":"10.1007\/978-3-030-01228-1_28"},{"doi-asserted-by":"crossref","unstructured":"Henter, G. E., Alexanderson, S., & Beskow, J. (2019). Moglow: Probabilistic and controllable motion synthesis using normalising flows. arXiv preprint arXiv:190506598","key":"6092_CR31","DOI":"10.1145\/3414685.3417836"},{"issue":"8","key":"6092_CR32","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780.","journal-title":"Neural Computation"},{"unstructured":"Huang, C. W., Touati, A., Dinh, L., Drozdzal, M., Havaei, M., Charlin, L., & Courville, A. (2017). Learnable explicit density for continuous latent space and variational inference. arXiv preprint arXiv:171002248","key":"6092_CR33"},{"unstructured":"Huang, C. W., Krueger, D., Lacoste, A., & Courville, A. (2018) Neural autoregressive flows. In International Conference on Machine Learning (pp. 2083\u20132092).","key":"6092_CR34"},{"issue":"4\u20135","key":"6092_CR35","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","volume":"13","author":"A Hyv\u00e4rinen","year":"2000","unstructured":"Hyv\u00e4rinen, A., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural Networks, 13(4\u20135), 411\u2013430.","journal-title":"Neural Networks"},{"unstructured":"Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning (pp. 448\u2013456).","key":"6092_CR36"},{"unstructured":"Jaini, P., Selby, K. A., & Yu, Y. (2019). Sum-of-squares polynomial flow. In International Conference on Machine Learning (pp. 3009\u20133018).","key":"6092_CR37"},{"key":"6092_CR38","first-page":"105","volume":"89","author":"MI Jordan","year":"1998","unstructured":"Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1998). An introduction to variational methods for graphical models. Nato Asi Series D Behavioural And Social Sciences, 89, 105\u2013162.","journal-title":"Nato Asi Series D Behavioural And Social Sciences"},{"unstructured":"Karl, M., Soelch, M., Bayer, J., van\u00a0der Smagt, P. (2017). Deep variational bayes filters: Unsupervised learning of state space models from raw data. In International Conference on Learning Representations.","key":"6092_CR39"},{"unstructured":"Kim, S., Lee, S. G., Song, J., Kim, J., & Yoon, S. (2019). Flowavenet: A generative flow for raw audio. In International Conference on Machine Learning (pp. 3370\u20133378).","key":"6092_CR40"},{"unstructured":"Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In International Conference on Learning Representations.","key":"6092_CR41"},{"unstructured":"Kingma, D. P., & Dhariwal, P. (2018). Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems (pp. 10215\u201310224).","key":"6092_CR42"},{"unstructured":"Kingma, D. P., & Welling, M. (2014). Stochastic gradient vb and the variational auto-encoder. In Proceedings of the International Conference on Learning Representations.","key":"6092_CR43"},{"unstructured":"Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., & Welling, M. (2016). Improved variational inference with inverse autoregressive flow. In Advances in Neural Information Processing Systems (pp. 4743\u20134751).","key":"6092_CR44"},{"unstructured":"Kumar, M., Babaeizadeh, M., Erhan, D., Finn, C., Levine, S., Dinh, L., & Kingma, D. (2020). Videoflow: A flow-based generative model for video. In International Conference on Learning Representations.","key":"6092_CR45"},{"issue":"4","key":"6092_CR46","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1109\/TNN.2011.2106511","volume":"22","author":"V Laparra","year":"2011","unstructured":"Laparra, V., Camps-Valls, G., & Malo, J. (2011). Iterative gaussianization: from ica to random rotations. IEEE transactions on neural networks, 22(4), 537\u2013549.","journal-title":"IEEE transactions on neural networks"},{"unstructured":"Li, Y., & Mandt, S. (2018). A deep generative model for disentangled representations of sequential data. In International Conference on Machine Learning.","key":"6092_CR47"},{"unstructured":"Lombardo, S., Han, J., Schroers, C., & Mandt, S. (2019). Deep generative video compression. In Advances in Neural Information Processing Systems (pp. 9283\u20139294).","key":"6092_CR48"},{"unstructured":"Marino, J., Cvitkovic, M., & Yue, Y. (2018). A general method for amortizing variational filtering. In Advances in Neural Information Processing Systems (pp. 7857\u20137868).","key":"6092_CR49"},{"key":"6092_CR50","volume-title":"Machine learning: a probabilistic perspective","author":"KP Murphy","year":"2012","unstructured":"Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press."},{"unstructured":"Oliva, J., Dubey, A., Zaheer, M., Poczos, B., Salakhutdinov, R., Xing, E., & Schneider, J. (2018). Transformation autoregressive networks. In International Conference on Machine Learning (pp. 3895\u20133904).","key":"6092_CR51"},{"issue":"4","key":"6092_CR52","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1002\/j.1538-7305.1952.tb01403.x","volume":"31","author":"B Oliver","year":"1952","unstructured":"Oliver, B. (1952). Efficient coding. The Bell System Technical Journal, 31(4), 724\u2013750.","journal-title":"The Bell System Technical Journal"},{"unstructured":"van\u00a0den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016a). Wavenet: A generative model for raw audio. arXiv preprint arXiv:160903499","key":"6092_CR53"},{"unstructured":"van\u00a0den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016b). Pixel recurrent neural networks. In International Conference on Machine Learning (pp. 1747\u20131756).","key":"6092_CR54"},{"unstructured":"van\u00a0den Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., Driessche, G., Lockhart, E., Cobo, L., Stimberg, F., et\u00a0al. (2018). Parallel wavenet: Fast high-fidelity speech synthesis. In International Conference on Machine Learning (pp. 3915\u20133923).","key":"6092_CR55"},{"issue":"2","key":"6092_CR56","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3233\/AIS-160372","volume":"8","author":"F Palumbo","year":"2016","unstructured":"Palumbo, F., Gallicchio, C., Pucci, R., & Micheli, A. (2016). Human activity recognition using multisensor data fusion based on reservoir computing. Journal of Ambient Intelligence and Smart Environments, 8(2), 87\u2013107.","journal-title":"Journal of Ambient Intelligence and Smart Environments"},{"unstructured":"Papamakarios, G., Pavlakou, T., & Murray, I. (2017). Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems (pp. 2338\u20132347).","key":"6092_CR57"},{"unstructured":"Ping, W., Peng, K., & Chen, J. (2019). Clarinet: Parallel wave generation in end-to-end text-to-speech. In International Conference on Learning Representations.","key":"6092_CR58"},{"doi-asserted-by":"crossref","unstructured":"Pourahmadi, M. (2011). Covariance estimation: The glm and regularization perspectives. Statistical Science (pp. 369\u2013387).","key":"6092_CR59","DOI":"10.1214\/11-STS358"},{"doi-asserted-by":"crossref","unstructured":"Prenger, R., Valle, R., & Catanzaro, B. (2019). Waveglow: A flow-based generative network for speech synthesis. ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3617\u20133621). IEEE.","key":"6092_CR60","DOI":"10.1109\/ICASSP.2019.8683143"},{"unstructured":"Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:151106434","key":"6092_CR61"},{"unstructured":"Rezende, D., & Mohamed, S. (2015). Variational inference with normalizing flows. In International Conference on Machine Learning (pp. 1530\u20131538).","key":"6092_CR62"},{"unstructured":"Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. In Proceedings of the International Conference on Machine Learning (pp. 1278\u20131286).","key":"6092_CR63"},{"doi-asserted-by":"crossref","unstructured":"Rhinehart, N., Kitani, K. M., & Vernaza, P. (2018) R2p2: A reparameterized pushforward policy for diverse, precise generative path forecasting. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 772\u2013788).","key":"6092_CR64","DOI":"10.1007\/978-3-030-01261-8_47"},{"doi-asserted-by":"crossref","unstructured":"Rhinehart, N., McAllister, R., Kitani, K., & Levine, S. (2019). Precog: Prediction conditioned on goals in visual multi-agent settings. In Proceedings of the International Conference on Computer Vision (ICCV).","key":"6092_CR65","DOI":"10.1109\/ICCV.2019.00291"},{"unstructured":"Rippel, O., & Adams, R. P. (2013). High-dimensional probability estimation with deep density models. arXiv preprint arXiv:13025125","key":"6092_CR66"},{"doi-asserted-by":"crossref","unstructured":"Schmidt, F., Mandt, S., & Hofmann, T. (2019). Autoregressive text generation beyond feedback loops. In Empirical Methods in Natural Language Processing (pp. 3391\u20133397).","key":"6092_CR67","DOI":"10.18653\/v1\/D19-1338"},{"doi-asserted-by":"crossref","unstructured":"Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: A local svm approach. In International Conference on Pattern Recognition.","key":"6092_CR68","DOI":"10.1109\/ICPR.2004.1334462"},{"issue":"1205","key":"6092_CR69","first-page":"427","volume":"216","author":"MV Srinivasan","year":"1982","unstructured":"Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982). Predictive coding: A fresh view of inhibition in the retina. Proceedings of the Royal Society of London Series B Biological Sciences, 216(1205), 427\u2013459.","journal-title":"Proceedings of the Royal Society of London Series B Biological Sciences"},{"unstructured":"Srivastava, N., Mansimov, E., & Salakhudinov, R. (2015a). Unsupervised learning of video representations using lstms. In International conference on Machine Learning (pp. 843\u2013852).","key":"6092_CR70"},{"unstructured":"Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015b). Training very deep networks. In Advances in neural information processing systems (NIPS) (pp. 2377\u20132385).","key":"6092_CR71"},{"unstructured":"Vaswani, A., Bengio, S., Brevdo, E., Chollet, F., Gomez, A. N., Gouws, S., Jones, L., Kaiser, L., Kalchbrenner, N., Parmar, N., Sepassi, R., Shazeer, N., & Uszkoreit, J. (2018). Tensor2tensor for neural machine translation. CoRR abs\/1803.07416, arXiv:1803.07416","key":"6092_CR72"},{"issue":"7","key":"6092_CR73","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1109\/TCSVT.2003.815165","volume":"13","author":"T Wiegand","year":"2003","unstructured":"Wiegand, T., Sullivan, G. J., Bjontegaard, G., & Luthra, A. (2003). Overview of the h. 264\/avc video coding standard. IEEE Transactions on circuits and systems for video technology, 13(7), 560\u2013576.","journal-title":"IEEE Transactions on circuits and systems for video technology"},{"unstructured":"Winkler, C., Worrall, D., Hoogeboom, E., & Welling, M. (2019). Learning likelihoods with conditional normalizing flows. arXiv preprint arXiv:191200042","key":"6092_CR74"},{"unstructured":"Xue, T., Wu, J., Bouman, K., & Freeman, B. (2016). Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks. In Advances in Neural Information Processing Systems.","key":"6092_CR75"},{"unstructured":"Yang, R., Yang, Y., Marino, J., & Mandt, S. (2021). Hierarchical autoregressive modeling for neural video compression. In International Conference on Learning Representations.","key":"6092_CR76"},{"unstructured":"Ziegler, Z., & Rush, A. (2019) Latent normalizing flows for discrete sequences. In International Conference on Machine Learning (pp. 7673\u20137682).","key":"6092_CR77"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-06092-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-021-06092-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-06092-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T01:10:54Z","timestamp":1668733854000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-021-06092-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,18]]},"references-count":77,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["6092"],"URL":"https:\/\/doi.org\/10.1007\/s10994-021-06092-6","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"type":"print","value":"0885-6125"},{"type":"electronic","value":"1573-0565"}],"subject":[],"published":{"date-parts":[[2021,11,18]]},"assertion":[{"value":"15 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"California Institute of Technology (caltech.edu); Simon Fraser University (sfu.ca); University of California Irvine (uci.edu); Disney Research (disneyresearch.com); Borealis AI (borealisai.com); DeepMind (deepmind.com, google.com)","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Source code is available at .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}