{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T08:13:46Z","timestamp":1762071226604,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031197741"},{"type":"electronic","value":"9783031197758"}],"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\/978-3-031-19775-8_11","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T12:12:59Z","timestamp":1666440779000},"page":"173-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7809-9897","authenticated-orcid":false,"given":"Ahmet Caner","family":"Y\u00fcz\u00fcg\u00fcler","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3955-111X","authenticated-orcid":false,"given":"Nikolaos","family":"Dimitriadis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4010-714X","authenticated-orcid":false,"given":"Pascal","family":"Frossard","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Bannon, P., Venkataramanan, G., Sarma, D.D., Talpes, E.: Computer and redundancy solution for the full self-driving computer. In: 2019 IEEE Hot Chips 31 Symposium (HCS), Cupertino, CA, USA, 18\u201320 August 2019, pp. 1\u201322. IEEE (2019)","DOI":"10.1109\/HOTCHIPS.2019.8875645"},{"key":"11_CR2","unstructured":"Bender, G., Kindermans, P., Zoph, B., Vasudevan, V., Le, Q.V.: Understanding and simplifying one-shot architecture search. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, 10\u201315 July 2018, vol. 80, pp. 549\u2013558. PMLR (2018)"},{"key":"11_CR3","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12\u201314 December 2011, Granada, Spain, pp. 2546\u20132554 (2011)"},{"key":"11_CR4","unstructured":"Cai, H., Zhu, L., Han, S.: Proxylessnas: direct neural architecture search on target task and hardware. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019)"},{"key":"11_CR5","unstructured":"Chang, J., Zhang, X., Guo, Y., Meng, G., Xiang, S., Pan, C.: DATA: differentiable architecture approximation. In: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8\u201314 December 2019, Vancouver, BC, Canada, pp. 874\u2013884 (2019)"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Y., Emer, J.S., Sze, V.: Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks. In: 43rd ACM\/IEEE Annual International Symposium on Computer Architecture, ISCA 2016, Seoul, South Korea, 18\u201322 June 2016, pp. 367\u2013379. IEEE Computer Society (2016)","DOI":"10.1109\/ISCA.2016.40"},{"issue":"5s","key":"11_CR7","doi-asserted-by":"publisher","first-page":"53:1","DOI":"10.1145\/3476984","volume":"20","author":"H Cho","year":"2021","unstructured":"Cho, H.: Risa: a reinforced systolic array for depthwise convolutions and embedded tensor reshaping. ACM Trans. Embed. Comput. Syst. 20(5s), 53:1-53:20 (2021)","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Choi, K., Hong, D., Yoon, H., Yu, J., Kim, Y., Lee, J.: DANCE: differentiable accelerator\/network co-exploration. In: 58th ACM\/IEEE Design Automation Conference, DAC 2021, San Francisco, CA, USA, 5\u20139 December 2021, pp. 337\u2013342. IEEE (2021)","DOI":"10.1109\/DAC18074.2021.9586121"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Dai, X., et al.: Chamnet: towards efficient network design through platform-aware model adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 11398\u201311407. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.01166"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20\u201325 June 2009, Miami, Florida, USA, pp. 248\u2013255. IEEE Computer Society (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.crma.2012.03.014","volume":"350","author":"JA D\u00e9sid\u00e9ri","year":"2012","unstructured":"D\u00e9sid\u00e9ri, J.A.: Multiple-gradient descent algorithm (MGDA) for multiobjective optimization. C.R. Math. 350, 313\u2013318 (2012)","journal-title":"C.R. Math."},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Gordon, A., et al.: Morphnet: fast & simple resource-constrained structure learning of deep networks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 1586\u20131595. Computer Vision Foundation\/IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00171"},{"key":"11_CR13","unstructured":"Gupta, S., Akin, B.: Accelerator-aware neural network design using automl. CoRR abs\/2003.02838 (2020)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. In: IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, 22\u201329 October 2017, pp. 2980\u20132988. IEEE Computer Society (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"11_CR15","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017)"},{"key":"11_CR16","doi-asserted-by":"publisher","first-page":"70461","DOI":"10.1109\/ACCESS.2019.2918851","volume":"7","author":"M Jord\u00e0","year":"2019","unstructured":"Jord\u00e0, M., Valero-Lara, P., Pe\u00f1a, A.J.: Performance evaluation of cudnn convolution algorithms on NVIDIA volta gpus. IEEE Access 7, 70461\u201370473 (2019)","journal-title":"IEEE Access"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Jouppi, N.P., et al.: Ten lessons from three generations shaped Google\u2019s TPUv4i: Industrial product. In: 48th ACM\/IEEE Annual International Symposium on Computer Architecture, ISCA 2021, Valencia, Spain, 14\u201318 June 2021, pp. 1\u201314. IEEE (2021)","DOI":"10.1109\/ISCA52012.2021.00010"},{"key":"11_CR18","unstructured":"Jouppi, N.P., et al.: In-datacenter performance analysis of a tensor processing unit. In: Proceedings of the 44th Annual International Symposium on Computer Architecture, ISCA 2017, Toronto, ON, Canada, 24\u201328 June 2017, pp. 1\u201312. ACM (2017)"},{"key":"11_CR19","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015)"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Kokkinos, I.: Ubernet: training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21\u201326 July 2017, pp. 5454\u20135463. IEEE Computer Society (2017)","DOI":"10.1109\/CVPR.2017.579"},{"key":"11_CR21","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Tech. rep. (2009)"},{"issue":"1","key":"11_CR22","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1109\/MC.1982.1653825","volume":"15","author":"HT Kung","year":"1982","unstructured":"Kung, H.T.: Why systolic architectures? Computer 15(1), 37\u201346 (1982)","journal-title":"Computer"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Li, S., et al.: Searching for fast model families on datacenter accelerators. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, Virtual, 19\u201325 June 2021, pp. 8085\u20138095. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00799"},{"key":"11_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-030-01246-5_2","volume-title":"Computer Vision \u2013 ECCV 2018","author":"C Liu","year":"2018","unstructured":"Liu, C., et al.: Progressive neural architecture search. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 19\u201335. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_2"},{"key":"11_CR25","unstructured":"Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019)"},{"key":"11_CR26","unstructured":"Marchisio, A., Massa, A., Mrazek, V., Bussolino, B., Martina, M., Shafique, M.: Nascaps: a framework for neural architecture search to optimize the accuracy and hardware efficiency of convolutional capsule networks. In: IEEE\/ACM International Conference On Computer Aided Design, ICCAD 2020, San Diego, CA, USA, 2\u20135 November 2020, pp. 114:1\u2013114:9. IEEE (2020)"},{"key":"11_CR27","unstructured":"Nayman, N., Noy, A., Ridnik, T., Friedman, I., Jin, R., Zelnik-Manor, L.: XNAS: neural architecture search with expert advice. In: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December, pp. 1975\u20131985 (2019)"},{"key":"11_CR28","unstructured":"Pham, H., Guan, M.Y., Zoph, B., Le, Q.V., Dean, J.: Efficient neural architecture search via parameter sharing. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, 10\u201315 July 2018. Proceedings of Machine Learning Research, vol. 80, pp. 4092\u20134101. PMLR (2018)"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. In: The 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, The 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, 27 January - 1 February 2019, pp. 4780\u20134789. AAAI Press (2019)","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Samajdar, A., Joseph, J.M., Zhu, Y., Whatmough, P.N., Mattina, M., Krishna, T.: A systematic methodology for characterizing scalability of DNN accelerators using scale-sim. In: IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2020, Boston, MA, USA, 23\u201325 August 2020, pp. 58\u201368. IEEE (2020)","DOI":"10.1109\/ISPASS48437.2020.00016"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 4510\u20134520. Computer Vision Foundation\/IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Smithson, S.C., Yang, G., Gross, W.J., Meyer, B.H.: Neural networks designing neural networks: multi-objective hyper-parameter optimization. In: Proceedings of the 35th International Conference on Computer-Aided Design, ICCAD 2016, Austin, TX, USA, 7\u201310 November 2016, p. 104. ACM (2016)","DOI":"10.1145\/2966986.2967058"},{"key":"11_CR33","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/978-3-030-46147-8_29","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"D Stamoulis","year":"2020","unstructured":"Stamoulis, D., et al.: Single-path nas: designing hardware-efficient convnets in less than 4 hours. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11907, pp. 481\u2013497. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46147-8_29"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Tan, M., et al.: Mnasnet: platform-aware neural architecture search for mobile. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 2820\u20132828. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00293"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Wan, A., et al.: Fbnetv2: differentiable neural architecture search for spatial and channel dimensions. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13\u201319 June 2020, pp. 12962\u201312971. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01298"},{"issue":"4","key":"11_CR36","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1145\/1498765.1498785","volume":"52","author":"S Williams","year":"2009","unstructured":"Williams, S., Waterman, A., Patterson, D.A.: Roofline: an insightful visual performance model for multicore architectures. Commun. ACM 52(4), 65\u201376 (2009)","journal-title":"Commun. ACM"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Wu, B., et al.: Fbnet: hardware-aware efficient convnet design via differentiable neural architecture search. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, 16\u201320 June 2019, pp. 10734\u201310742. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.01099"},{"key":"11_CR38","unstructured":"Xie, S., Zheng, H., Liu, C., Lin, L.: SNAS: stochastic neural architecture search. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019)"},{"key":"11_CR39","doi-asserted-by":"crossref","unstructured":"Xiong, Y., et al.: Mobiledets: searching for object detection architectures for mobile accelerators. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, 19\u201325 June 2021, pp. 3825\u20133834. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00382"},{"key":"11_CR40","unstructured":"Xu, Y., et al.: PC-DARTS: partial channel connections for memory-efficient architecture search. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26\u201330 April 2020. OpenReview.net (2020)"},{"key":"11_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/978-3-030-01249-6_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"T-J Yang","year":"2018","unstructured":"Yang, T.-J., et al.: NetAdapt: platform-aware neural network adaptation for mobile applications. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 289\u2013304. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_18"},{"key":"11_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, L.L., Yang, Y., Jiang, Y., Zhu, W., Liu, Y.: Fast hardware-aware neural architecture search. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2020, Seattle, WA, USA, 14\u201319 June 2020, pp. 2959\u20132967. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPRW50498.2020.00354"},{"issue":"4","key":"11_CR43","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/4235.797969","volume":"3","author":"E Zitzler","year":"1999","unstructured":"Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257\u2013271 (1999)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"11_CR44","unstructured":"Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017)"},{"key":"11_CR45","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, 18\u201322 June 2018, pp. 8697\u20138710. Computer Vision Foundation\/IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00907"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19775-8_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:36:47Z","timestamp":1710337007000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19775-8_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031197741","9783031197758"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19775-8_11","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":"23 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}