{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:21:13Z","timestamp":1743142873789,"version":"3.40.3"},"publisher-location":"Cham","reference-count":74,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031189067"},{"type":"electronic","value":"9783031189074"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18907-4_61","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:03:53Z","timestamp":1666825433000},"page":"788-813","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Single Deterministic Neural Network with\u00a0Hierarchical Gaussian Mixture Model for\u00a0Uncertainty Quantification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2260-4107","authenticated-orcid":false,"given":"Chunlin","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6226-2810","authenticated-orcid":false,"given":"Dingwei","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"61_CR1","unstructured":"Allen, K., Shelhamer, E., Shin, H., Tenenbaum, J.: Infinite mixture prototypes for few-shot learning. In: Proceedings of the 36th International Conference on Machine Learning, pp. 232\u2013241 (2019)"},{"key":"61_CR2","doi-asserted-by":"crossref","unstructured":"Alvarez-Melis, D., Jaakkola, T.: A causal framework for explaining the predictions of black-box sequence-to-sequence models. In: EMNLP (2017)","DOI":"10.18653\/v1\/D17-1042"},{"key":"61_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-5056-2","volume-title":"Differential-Geometrical Methods in Statistics","author":"SI Amari","year":"1985","unstructured":"Amari, S.I.: Differential-Geometrical Methods in Statistics, vol. 28. Springer, New York (1985). https:\/\/doi.org\/10.1007\/978-1-4612-5056-2"},{"key":"61_CR4","unstructured":"Amari, S.: Neural learning in structured parameter spaces-natural Riemannian gradient. Adv. Neural Inf. Process. Syst. 127\u2013133 (1997)"},{"key":"61_CR5","unstructured":"van Amersfoort, J.R., Smith, L., Teh, Y.W., Gal, Y.: Simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network. In: ICML (2020)"},{"key":"61_CR6","first-page":"1345","volume":"6","author":"A Banerjee","year":"2005","unstructured":"Banerjee, A., Dhillon, I.S., Ghosh, J., Sra, S.: Clustering on the unit hypersphere using von Mises-Fisher distributions. J. Mach. Learn. Res. 6, 1345\u20131382 (2005)","journal-title":"J. Mach. Learn. Res."},{"key":"61_CR7","doi-asserted-by":"crossref","unstructured":"Beecks, C., Ivanescu, A.M., Kirchhoff, S., Seidl, T.: Modeling image similarity by gaussian mixture models and the signature quadratic form distance. In: 2011 International Conference on Computer Vision, pp. 1754\u20131761. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126440"},{"key":"61_CR8","doi-asserted-by":"crossref","unstructured":"Bendale, A., Boult, T.E.: Towards open set deep networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1563\u20131572 (2016)","DOI":"10.1109\/CVPR.2016.173"},{"issue":"1","key":"61_CR9","first-page":"121","volume":"1","author":"D Blei","year":"2004","unstructured":"Blei, D., Jordan, M.: Variational inference for Dirichlet process mixtures. Bayesian Anal. 1(1), 121\u2013144 (2004)","journal-title":"Bayesian Anal."},{"key":"61_CR10","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural networks. arXiv abs\/1505.05424 (2015)"},{"key":"61_CR11","unstructured":"Bojarski, M., et al.: End to end learning for self-driving cars. arXiv abs\/1604.07316 (2016)"},{"key":"61_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","volume":"78","author":"GW Brier","year":"1950","unstructured":"Brier, G.W.: Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78, 1\u20133 (1950)","journal-title":"Mon. Weather Rev."},{"issue":"5","key":"61_CR13","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1109\/LSP.2006.870086","volume":"13","author":"WM Campbell","year":"2006","unstructured":"Campbell, W.M., Sturim, D.E., Reynolds, D.A.: Support vector machines using GMM supervectors for speaker verification. IEEE Sig. Process. Lett. 13(5), 308\u2013311 (2006)","journal-title":"IEEE Sig. Process. Lett."},{"issue":"3","key":"61_CR14","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TPAMI.2007.61","volume":"29","author":"G Carneiro","year":"2007","unstructured":"Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 394\u2013410 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"61_CR15","doi-asserted-by":"crossref","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41, 15:1\u201315:58 (2009)","DOI":"10.1145\/1541880.1541882"},{"key":"61_CR16","unstructured":"Chen, T., Fox, E.B., Guestrin, C.: Stochastic gradient Hamiltonian Monte Carlo. arXiv abs\/1402.4102 (2014)"},{"key":"61_CR17","unstructured":"Chen, T., Navr\u00e1til, J., Iyengar, V., Shanmugam, K.: Confidence scoring using whitebox meta-models with linear classifier probes. arXiv abs\/1805.05396 (2019)"},{"key":"61_CR18","unstructured":"Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., Ha, D.: Deep learning for classical Japanese literature. arXiv abs\/1812.01718 (2018)"},{"key":"61_CR19","unstructured":"Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: AISTATS (2011)"},{"key":"61_CR20","doi-asserted-by":"crossref","unstructured":"Cortes, C., DeSalvo, G., Mohri, M.: Learning with rejection. In: ALT (2016)","DOI":"10.1007\/978-3-319-46379-7_5"},{"key":"61_CR21","unstructured":"Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper. J. Roy. Stat. Soc. Ser. B (Methodol.) 1\u201338 (1997)"},{"key":"61_CR22","unstructured":"Dusenberry, M.W., et al.: Efficient and scalable Bayesian neural nets with rank-1 factors. arXiv abs\/2005.07186 (2020)"},{"key":"61_CR23","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1080\/01621459.1994.10476468","volume":"89","author":"M Escobar","year":"1994","unstructured":"Escobar, M.: Estimating normal means with a Dirichlet process prior. J. Am. Stat. Assoc. 89, 268\u2013277 (1994)","journal-title":"J. Am. Stat. Assoc."},{"key":"61_CR24","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017)","journal-title":"Nature"},{"key":"61_CR25","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. arXiv abs\/1506.02142 (2016)"},{"key":"61_CR26","unstructured":"Geifman, Y., El-Yaniv, R.: Selective classification for deep neural networks. In: NIPS (2017)"},{"key":"61_CR27","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1111\/j.1467-9868.2007.00587.x","volume":"69","author":"T Gneiting","year":"2007","unstructured":"Gneiting, T., Balabdaoui, F., Raftery, A.E.: Probabilistic forecasts, calibration and sharpness. J. Roy. Stat. Soc. Ser. B-Stat. Methodol. 69, 243\u2013268 (2007)","journal-title":"J. Roy. Stat. Soc. Ser. B-Stat. Methodol."},{"key":"61_CR28","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1198\/016214506000001437","volume":"102","author":"T Gneiting","year":"2007","unstructured":"Gneiting, T., Raftery, A.E.: Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102, 359\u2013378 (2007)","journal-title":"J. Am. Stat. Assoc."},{"key":"61_CR29","unstructured":"Graves, A.: Practical variational inference for neural networks. In: NIPS (2011)"},{"key":"61_CR30","doi-asserted-by":"publisher","first-page":"43992","DOI":"10.1109\/ACCESS.2020.2977671","volume":"8","author":"C Guo","year":"2020","unstructured":"Guo, C., Zhou, J., Chen, H., Ying, N., Zhang, J., Zhou, D.: Variational autoencoder with optimizing gaussian mixture model priors. IEEE Access 8, 43992\u201344005 (2020)","journal-title":"IEEE Access"},{"key":"61_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"61_CR32","doi-asserted-by":"crossref","unstructured":"Hein, M., Andriushchenko, M., Bitterwolf, J.: Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 41\u201350 (2019)","DOI":"10.1109\/CVPR.2019.00013"},{"key":"61_CR33","unstructured":"Hern\u00e1ndez-Lobato, J.M., Adams, R.P.: Probabilistic backpropagation for scalable learning of Bayesian neural networks. In: ICML (2015)"},{"key":"61_CR34","unstructured":"Hern\u00e1ndez-Lobato, J.M., Adams, R.P.: Probabilistic backpropagation for scalable learning of Bayesian neural networks. In: Proceedings of the International Conference on Machine Learning (2015)"},{"key":"61_CR35","doi-asserted-by":"crossref","unstructured":"Hershey, J., Olsen, P.: Approximating the Kullback Leibler divergence between Gaussian mixture models. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP 2007, vol. 4, pp. IV-317\u2013IV-320 (2007)","DOI":"10.1109\/ICASSP.2007.366913"},{"key":"61_CR36","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1023\/B:AIRE.0000045502.10941.a9","volume":"22","author":"VJ Hodge","year":"2004","unstructured":"Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22, 85\u2013126 (2004)","journal-title":"Artif. Intell. Rev."},{"key":"61_CR37","unstructured":"Houlsby, N., Husz\u00e1r, F., Ghahramani, Z., Lengyel, M.: Bayesian active learning for classification and preference learning. arXiv abs\/1112.5745 (2011)"},{"key":"61_CR38","unstructured":"Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D.P., Wilson, A.G.: Averaging weights leads to wider optima and better generalization. arXiv abs\/1803.05407 (2018)"},{"key":"61_CR39","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: NIPS (2017)"},{"key":"61_CR40","doi-asserted-by":"crossref","unstructured":"Khan, M.E., Nielsen, D.: Fast yet simple natural-gradient descent for variational inference in complex models. In: 2018 International Symposium on Information Theory and Its Applications (ISITA), pp. 31\u201335. IEEE (2018)","DOI":"10.23919\/ISITA.2018.8664326"},{"key":"61_CR41","unstructured":"Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. arXiv abs\/1506.02557 (2015)"},{"key":"61_CR42","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: NIPS (2017)"},{"key":"61_CR43","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition (1998)","DOI":"10.1109\/5.726791"},{"key":"61_CR44","doi-asserted-by":"crossref","unstructured":"Lin, W., Khan, M.E., Schmidt, M.: Fast and simple natural-gradient variational inference with mixture of exponential-family approximations. In: ICML (2019)","DOI":"10.23919\/ISITA.2018.8664326"},{"key":"61_CR45","unstructured":"Lin, W., Schmidt, M.W., Khan, M.E.: Handling the positive-definite constraint in the Bayesian learning rule. In: ICML (2020)"},{"key":"61_CR46","unstructured":"Liu, J.Z., Lin, Z., Padhy, S., Tran, D., Bedrax-Weiss, T., Lakshminarayanan, B.: Simple and principled uncertainty estimation with deterministic deep learning via distance awareness. arXiv abs\/2006.10108 (2020)"},{"key":"61_CR47","unstructured":"Lopez-Paz, D.: From dependence to causation. arXiv Machine Learning (2016)"},{"key":"61_CR48","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","volume":"4","author":"DJC Mackay","year":"1992","unstructured":"Mackay, D.J.C.: A practical Bayesian framework for backpropagation networks. Neural Comput. 4, 448\u2013472 (1992)","journal-title":"Neural Comput."},{"key":"61_CR49","unstructured":"Maddox, W., Garipov, T., Izmailov, P., Vetrov, D.P., Wilson, A.G.: A simple baseline for Bayesian uncertainty in deep learning. In: NeurIPS (2019)"},{"key":"61_CR50","unstructured":"Malinin, A., Gales, M.J.F.: Predictive uncertainty estimation via prior networks. In: NeurIPS (2018)"},{"key":"61_CR51","unstructured":"Naeini, M.P., Cooper, G.F., Hauskrecht, M.: Obtaining well calibrated probabilities using Bayesian binning. In: Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence 2015, pp. 2901\u20132907 (2015)"},{"key":"61_CR52","unstructured":"Nalisnick, E.T., Matsukawa, A., Teh, Y.W., G\u00f6r\u00fcr, D., Lakshminarayanan, B.: Do deep generative models know what they don\u2019t know? arXiv abs\/1810.09136 (2019)"},{"key":"61_CR53","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1023\/A:1008923215028","volume":"11","author":"R Neal","year":"2001","unstructured":"Neal, R.: Annealed importance sampling. Stat. Comp. 11, 125\u2013139 (2001)","journal-title":"Stat. Comp."},{"key":"61_CR54","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.: Reading digits in natural images with unsupervised feature learning (2011)"},{"key":"61_CR55","unstructured":"Osband, I., Blundell, C., Pritzel, A., Roy, B.V.: Deep exploration via bootstrapped DQN. In: NIPS (2016)"},{"key":"61_CR56","unstructured":"Ovadia, Y., et al.: Can you trust your model\u2019s uncertainty? Evaluating predictive uncertainty under dataset shift. In: NeurIPS (2019)"},{"key":"61_CR57","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)"},{"key":"61_CR58","unstructured":"Pearce, T., Brintrup, A., Zaki, M., Neely, A.D.: High-quality prediction intervals for deep learning: a distribution-free, ensembled approach. In: ICML (2018)"},{"issue":"4","key":"61_CR59","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.patcog.2005.10.028","volume":"39","author":"H Permuter","year":"2006","unstructured":"Permuter, H., Francos, J., Jermyn, I.: A study of gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recogn. 39(4), 695\u2013706 (2006)","journal-title":"Pattern Recogn."},{"key":"61_CR60","doi-asserted-by":"crossref","unstructured":"Povey, D., et al.: Subspace gaussian mixture models for speech recognition. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4330\u20134333. IEEE (2010)","DOI":"10.1109\/ICASSP.2010.5495662"},{"key":"61_CR61","doi-asserted-by":"crossref","unstructured":"Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.: Dataset shift in machine learning (2009)","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"61_CR62","unstructured":"Ren, J., et al.: Likelihood ratios for out-of-distribution detection. In: NeurIPS (2019)"},{"key":"61_CR63","unstructured":"Shafaei, A., Schmidt, M.W., Little, J.: Does your model know the digit 6 is not a cat? A less biased evaluation of \u201coutlier\u201d detectors. arXiv abs\/1809.04729 (2018)"},{"key":"61_CR64","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: NIPS (2017)"},{"key":"61_CR65","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. CoRR abs\/1312.6199 (2014)"},{"key":"61_CR66","unstructured":"Tagasovska, N., Lopez-Paz, D.: Single-model uncertainties for deep learning. In: NeurIPS (2019)"},{"key":"61_CR67","unstructured":"Teye, M., Azizpour, H., Smith, K.: Bayesian uncertainty estimation for batch normalized deep networks. In: ICML (2018)"},{"key":"61_CR68","unstructured":"Vadera, M.P., Cobb, A.D., Jalaeian, B., Marlin, B.M.: Ursabench: comprehensive benchmarking of approximate Bayesian inference methods for deep neural networks. arXiv abs\/2007.04466 (2020)"},{"key":"61_CR69","unstructured":"Vasconcelos, N., Lippman, A.: Learning mixture hierarchies. Adv. Neural Inf. Process. Syst. 11 (1998)"},{"key":"61_CR70","unstructured":"Welling, M., Teh, Y.W.: Bayesian learning via stochastic gradient Langevin dynamics. In: ICML (2011)"},{"key":"61_CR71","unstructured":"Wen, Y., Tran, D., Ba, J.: Batchensemble: an alternative approach to efficient ensemble and lifelong learning. arXiv abs\/2002.06715 (2020)"},{"key":"61_CR72","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv abs\/1708.07747 (2017)"},{"issue":"5","key":"61_CR73","first-page":"2481","volume":"21","author":"G Yu","year":"2011","unstructured":"Yu, G., Sapiro, G., Mallat, S.: Solving inverse problems with piecewise linear estimators: from gaussian mixture models to structured sparsity. IEEE Trans. Image Process. 21(5), 2481\u20132499 (2011)","journal-title":"IEEE Trans. Image Process."},{"key":"61_CR74","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Pal, D.K., Savvides, M.: Ring loss: convex feature normalization for face recognition. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5089\u20135097 (2018)","DOI":"10.1109\/CVPR.2018.00534"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18907-4_61","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T23:15:05Z","timestamp":1666826105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18907-4_61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189067","9783031189074"],"references-count":74,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18907-4_61","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"233","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.03","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.35","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}