{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T04:47:11Z","timestamp":1773118031685,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T00:00:00Z","timestamp":1624665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,6,26]]},"DOI":"10.1145\/3449639.3459277","type":"proceedings-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T17:50:43Z","timestamp":1624297843000},"page":"305-313","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Optimizing loss functions through multi-variate taylor polynomial parameterization"],"prefix":"10.1145","author":[{"given":"Santiago","family":"Gonzalez","sequence":"first","affiliation":[{"name":"University of Texas at Austin"}]},{"given":"Risto","family":"Miikkulainen","sequence":"additional","affiliation":[{"name":"University of Texas at Austin"}]}],"member":"320","published-online":{"date-parts":[[2021,6,26]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , Manjunath Kudlur , Josh Levenberg , Rajat Monga , Sherry Moore , Derek G. Murray , Benoit Steiner , Paul Tucker , Vijay Vasudevan , Pete Warden , Martin Wicke , Yuan Yu , and Xiaoqiang Zheng . 2016 . TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) . USENIX Association, Savannah, GA, 265--283. https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA, 265--283. https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi"},{"key":"e_1_3_2_2_2_1","volume-title":"Genetic programming: An introduction","author":"Banzhaf Wolfgang","unstructured":"Wolfgang Banzhaf , Peter Nordin , Robert E Keller , and Frank D Francone . 1998. Genetic programming: An introduction . Vol. 1 . Morgan Kaufmann San Francisco . Wolfgang Banzhaf, Peter Nordin, Robert E Keller, and Frank D Francone. 1998. Genetic programming: An introduction. Vol. 1. Morgan Kaufmann San Francisco."},{"key":"e_1_3_2_2_3_1","volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference.","author":"Bingham Garrett","year":"2020","unstructured":"Garrett Bingham , William Macke , and Risto Miikkulainen . 2020 . Evolutionary Optimization of Deep Learning Activation Functions . In Proceedings of the Genetic and Evolutionary Computation Conference. Garrett Bingham, William Macke, and Risto Miikkulainen. 2020. Evolutionary Optimization of Deep Learning Activation Functions. In Proceedings of the Genetic and Evolutionary Computation Conference."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0025-5718-1973-0382928-6"},{"key":"e_1_3_2_2_5_1","volume-title":"Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552","author":"DeVries Terrance","year":"2017","unstructured":"Terrance DeVries and Graham W Taylor . 2017. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 ( 2017 ). Terrance DeVries and Graham W Taylor. 2017. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.608"},{"key":"e_1_3_2_2_7_1","first-page":"1","article-title":"Neural Architecture Search: A Survey","volume":"20","author":"Elsken Thomas","year":"2019","unstructured":"Thomas Elsken , Jan Hendrik Metzen , and Frank Hutter . 2019 . Neural Architecture Search: A Survey . Journal of Machine Learning Research 20 , 55 (2019), 1 -- 21 . Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. 2019. Neural Architecture Search: A Survey. Journal of Machine Learning Research 20, 55 (2019), 1--21.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_8_1","volume-title":"La th\u00e9orie analytique de la chaleur. M\u00e9moires de l'Acad\u00e9mie Royale des Sciences de I'Institut de France 8","author":"Fourier Joseph BJ","year":"1829","unstructured":"Joseph BJ Fourier . 1829. La th\u00e9orie analytique de la chaleur. M\u00e9moires de l'Acad\u00e9mie Royale des Sciences de I'Institut de France 8 ( 1829 ), 581--622. Joseph BJ Fourier. 1829. La th\u00e9orie analytique de la chaleur. M\u00e9moires de l'Acad\u00e9mie Royale des Sciences de I'Institut de France 8 (1829), 581--622."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00041"},{"key":"e_1_3_2_2_10_1","first-page":"10677","article-title":"Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence","volume":"32","author":"Golatkar Aditya Sharad","year":"2019","unstructured":"Aditya Sharad Golatkar , Alessandro Achille , and Stefano Soatto . 2019 . Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence . In Advances in Neural Information Processing Systems 32. 10677 -- 10687 . Aditya Sharad Golatkar, Alessandro Achille, and Stefano Soatto. 2019. Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence. In Advances in Neural Information Processing Systems 32. 10677--10687.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_11_1","unstructured":"Santiago Gonzalez. 2019. SwiftCMA. https:\/\/github.com\/sgonzalez\/SwiftCMA.  Santiago Gonzalez. 2019. SwiftCMA. https:\/\/github.com\/sgonzalez\/SwiftCMA."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8851717"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC48606.2020.9185777"},{"key":"e_1_3_2_2_14_1","volume-title":"Pad\u00e9 approximation and its applications","author":"Graves-Morris PR","unstructured":"PR Graves-Morris . 1979. The numerical calculation of Pad\u00e9 approximants . In Pad\u00e9 approximation and its applications . Springer , 231--245. PR Graves-Morris. 1979. The numerical calculation of Pad\u00e9 approximants. In Pad\u00e9 approximation and its applications. Springer, 231--245."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/0010-4655(75)90068-5"},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings of an International Conference on Genetic Algorithms and Their Applications. 112--120","author":"Grefenstette John J","year":"1985","unstructured":"John J Grefenstette and J Michael Fitzpatrick . 1985 . Genetic search with approximate function evaluations . In Proceedings of an International Conference on Genetic Algorithms and Their Applications. 112--120 . John J Grefenstette and J Michael Fitzpatrick. 1985. Genetic search with approximate function evaluations. In Proceedings of an International Conference on Genetic Algorithms and Their Applications. 112--120."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.668"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30217-9_29"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICEC.1996.542381"},{"key":"e_1_3_2_2_20_1","volume-title":"Completely derandomized self-adaptation in evolution strategies. Evolutionary computation 9, 2","author":"Hansen Nikolaus","year":"2001","unstructured":"Nikolaus Hansen and Andreas Ostermeier . 2001. Completely derandomized self-adaptation in evolution strategies. Evolutionary computation 9, 2 ( 2001 ), 159--195. Nikolaus Hansen and Andreas Ostermeier. 2001. Completely derandomized self-adaptation in evolution strategies. Evolutionary computation 9, 2 (2001), 159--195."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_3_2_2_23_1","volume-title":"Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580","author":"Hinton Geoffrey E","year":"2012","unstructured":"Geoffrey E Hinton , Nitish Srivastava , Alex Krizhevsky , Ilya Sutskever , and Ruslan R Salakhutdinov . 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 ( 2012 ). Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)."},{"key":"e_1_3_2_2_24_1","volume-title":"Robust Estimation of a Location Parameter. The Annals of Mathematical Statistics","author":"Huber Peter J","year":"1964","unstructured":"Peter J Huber . 1964. Robust Estimation of a Location Parameter. The Annals of Mathematical Statistics ( 1964 ), 73--101. Peter J Huber. 1964. Robust Estimation of a Location Parameter. The Annals of Mathematical Statistics (1964), 73--101."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2011.05.001"},{"key":"e_1_3_2_2_26_1","volume-title":"Proceedings of the Fifth International Conference on Learning Representations (ICLR).","author":"Keskar Nitish Shirish","year":"2017","unstructured":"Nitish Shirish Keskar , Dheevatsa Mudigere , Jorge Nocedal , Mikhail Smelyanskiy , and Ping Tak Peter Tang . 2017 . On large-batch training for deep learning: Generalization gap and sharp minima . In Proceedings of the Fifth International Conference on Learning Representations (ICLR). Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. 2017. On large-batch training for deep learning: Generalization gap and sharp minima. In Proceedings of the Fifth International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_27_1","volume-title":"Proceedings of the Second International Conference on Learning Representations (ICLR).","author":"Kingma Diederik","year":"2014","unstructured":"Diederik Kingma and Max Welling . 2014 . Auto-Encoding Variational Bayes . In Proceedings of the Second International Conference on Learning Representations (ICLR). Diederik Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In Proceedings of the Second International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_28_1","unstructured":"Alex Krizhevsky and Geofrey Hinton. 2009. Learning multiple layers of features from tiny images. (2009).  Alex Krizhevsky and Geofrey Hinton. 2009. Learning multiple layers of features from tiny images. (2009)."},{"key":"e_1_3_2_2_29_1","volume-title":"Advances in Neural Information Processing Systems 25","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E Hinton . 2012. ImageNet Classification with Deep Convolutional Neural Networks . In Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Curran Associates, Inc. , 1097--1105. http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 1097--1105. http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf"},{"key":"e_1_3_2_2_30_1","volume-title":"Quantifying the Carbon Emissions of Machine Learning. arXiv preprint arXiv:1910.09700","author":"Lacoste Alexandre","year":"2019","unstructured":"Alexandre Lacoste , Alexandra Luccioni , Victor Schmidt , and Thomas Dandres . 2019. Quantifying the Carbon Emissions of Machine Learning. arXiv preprint arXiv:1910.09700 ( 2019 ). Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres. 2019. Quantifying the Carbon Emissions of Machine Learning. arXiv preprint arXiv:1910.09700 (2019)."},{"key":"e_1_3_2_2_31_1","unstructured":"Yann LeCun Corinna Cortes and CJC Burges. 1998. The MNIST dataset of handwritten digits.  Yann LeCun Corinna Cortes and CJC Burges. 1998. The MNIST dataset of handwritten digits."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-013-9406-y"},{"key":"e_1_3_2_2_33_1","volume-title":"Advances in Neural Information Processing Systems 31","author":"Li Hao","unstructured":"Hao Li , Zheng Xu , Gavin Taylor , Christoph Studer , and Tom Goldstein . 2018. Visualizing the Loss Landscape of Neural Nets . In Advances in Neural Information Processing Systems 31 , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). Curran Associates, Inc. , 6389--6399. http:\/\/papers.nips.cc\/paper\/7875-visualizing-the-loss-landscape-of-neural-nets.pdf Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. 2018. Visualizing the Loss Landscape of Neural Nets. In Advances in Neural Information Processing Systems 31, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). Curran Associates, Inc., 6389--6399. http:\/\/papers.nips.cc\/paper\/7875-visualizing-the-loss-landscape-of-neural-nets.pdf"},{"key":"e_1_3_2_2_34_1","first-page":"2579","article-title":"Visualizing data using t-SNE","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geofrey Hinton . 2008 . Visualizing data using t-SNE . Journal of Machine Learning Research 9 , Nov (2008), 2579 -- 2605 . Laurens van der Maaten and Geofrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, Nov (2008), 2579--2605.","journal-title":"Journal of Machine Learning Research 9"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Risto Miikkulainen Jason Liang Elliot Meyerson Aditya Rawal Daniel Fink Olivier Francon Bala Raju Hormoz Shahrzad Arshak Navruzyan Nigel Duffy etal 2019. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing. Elsevier 293--312.  Risto Miikkulainen Jason Liang Elliot Meyerson Aditya Rawal Daniel Fink Olivier Francon Bala Raju Hormoz Shahrzad Arshak Navruzyan Nigel Duffy et al. 2019. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing. Elsevier 293--312.","DOI":"10.1016\/B978-0-12-815480-9.00015-3"},{"key":"e_1_3_2_2_36_1","unstructured":"Karl Mutch. 2017 - 2021. Studio Go Runner. https:\/\/github.com\/leaf-ai\/studio-go-runner\/tree\/0.13.1  Karl Mutch. 2017 - 2021. Studio Go Runner. https:\/\/github.com\/leaf-ai\/studio-go-runner\/tree\/0.13.1"},{"key":"e_1_3_2_2_37_1","volume-title":"Workshop on Deep Learning and Unsupervised Feature Learning","author":"Netzer Yuval","year":"2011","unstructured":"Yuval Netzer , Tao Wang , Adam Coates , Alessandro Bissacco , Bo Wu , and Andrew Y Ng . 2011 . Reading digits in natural images with unsupervised feature learning. Neural Information Processing Systems , Workshop on Deep Learning and Unsupervised Feature Learning (2011). Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. 2011. Reading digits in natural images with unsupervised feature learning. Neural Information Processing Systems, Workshop on Deep Learning and Unsupervised Feature Learning (2011)."},{"key":"e_1_3_2_2_38_1","volume-title":"Le","author":"Real Esteban","year":"2020","unstructured":"Esteban Real , Chen Liang , David R. So , and Quoc V . Le . 2020 . AutoML-Zero: Evolving Machine Learning Algorithms From Scratch . arXiv:2003.03384 (2020). Esteban Real, Chen Liang, David R. So, and Quoc V. Le. 2020. AutoML-Zero: Evolving Machine Learning Algorithms From Scratch. arXiv:2003.03384 (2020)."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1093\/beheco\/ark016"},{"key":"e_1_3_2_2_41_1","volume-title":"Empirical analysis of the Hessian of over-parametrized neural networks. arXiv preprint arXiv:1706.04454","author":"Sagun Levent","year":"2017","unstructured":"Levent Sagun , Utku Evci , V Ugur Guney , Yann Dauphin , and Leon Bottou . 2017. Empirical analysis of the Hessian of over-parametrized neural networks. arXiv preprint arXiv:1706.04454 ( 2017 ). Levent Sagun, Utku Evci, V Ugur Guney, Yann Dauphin, and Leon Bottou. 2017. Empirical analysis of the Hessian of over-parametrized neural networks. arXiv preprint arXiv:1706.04454 (2017)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.58"},{"key":"e_1_3_2_2_44_1","volume-title":"Riedmiller","author":"Springenberg Jost Tobias","year":"2015","unstructured":"Jost Tobias Springenberg , Alexey Dosovitskiy , Thomas Brox , and Martin A . Riedmiller . 2015 . Striving for Simplicity: The All Convolutional Net. CoRR abs\/1412.6806 (2015). Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller. 2015. Striving for Simplicity: The All Convolutional Net. CoRR abs\/1412.6806 (2015)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_2_46_1","unstructured":"Brook Taylor. 1715. Methodus incrementorum directa & inversa. Auctore Brook Taylor LL. D. & Regiae Societatis Secretario. typis Pearsonianis: prostant apud Gul. Innys ad Insignia Principis.  Brook Taylor. 1715. Methodus incrementorum directa & inversa. Auctore Brook Taylor LL. D. & Regiae Societatis Secretario. typis Pearsonianis: prostant apud Gul. Innys ad Insignia Principis."},{"key":"e_1_3_2_2_47_1","first-page":"1035","article-title":"Solution of incorrectly formulated problems and the regularization method","volume":"4","author":"Tikhonov Andrey N.","year":"1963","unstructured":"Andrey N. Tikhonov . 1963 . Solution of incorrectly formulated problems and the regularization method . In Proceedings of the USSR Academy of Sciences , Vol. 4. 1035 -- 1038 . Andrey N. Tikhonov. 1963. Solution of incorrectly formulated problems and the regularization method. In Proceedings of the USSR Academy of Sciences, Vol. 4. 1035--1038.","journal-title":"Proceedings of the USSR Academy of Sciences"},{"key":"e_1_3_2_2_48_1","unstructured":"United States Environmental Protection Agency. 2020. EPA eGRID2018. https:\/\/www.epa.gov\/egrid.  United States Environmental Protection Agency. 2020. EPA eGRID2018. https:\/\/www.epa.gov\/egrid."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.2307\/2332510"},{"key":"e_1_3_2_2_50_1","first-page":"198","article-title":"On a certain periodic function","volume":"3","author":"Wilbraham Henry","year":"1848","unstructured":"Henry Wilbraham . 1848 . On a certain periodic function . The Cambridge and Dublin Mathematical Journal 3 (1848), 198 -- 201 . Henry Wilbraham. 1848. On a certain periodic function. The Cambridge and Dublin Mathematical Journal 3 (1848), 198--201.","journal-title":"The Cambridge and Dublin Mathematical Journal"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"e_1_3_2_2_52_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis . 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 ( 2016 ). Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)."},{"key":"e_1_3_2_2_53_1","volume-title":"Proceedings of the 26th International Conference on Computational Linguistics (COLING), Technical Papers. 2912--2922","author":"Zhou Yao","year":"2016","unstructured":"Yao Zhou , Cong Liu , and Yan Pan . 2016 . Modelling Sentence Pairs with Tree-structured Attentive Encoder . In Proceedings of the 26th International Conference on Computational Linguistics (COLING), Technical Papers. 2912--2922 . Yao Zhou, Cong Liu, and Yan Pan. 2016. Modelling Sentence Pairs with Tree-structured Attentive Encoder. In Proceedings of the 26th International Conference on Computational Linguistics (COLING), Technical Papers. 2912--2922."}],"event":{"name":"GECCO '21: Genetic and Evolutionary Computation Conference","location":"Lille France","acronym":"GECCO '21","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"]},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3449639.3459277","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3449639.3459277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:56Z","timestamp":1750197716000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3449639.3459277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,26]]},"references-count":51,"alternative-id":["10.1145\/3449639.3459277","10.1145\/3449639"],"URL":"https:\/\/doi.org\/10.1145\/3449639.3459277","relation":{},"subject":[],"published":{"date-parts":[[2021,6,26]]},"assertion":[{"value":"2021-06-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}