

@article{ElskenJmlr2019,
	author = {Elsken, Thomas and Metzen, Jan Hendrik and Hutter, Frank},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/journals/jmlr/ElskenMH19.bib},
	date-modified = {2022-06-12 20:01:43 +0200},
	journal = {Journal of Machine Learning Research},
	pages = {55:1--55:21},
	timestamp = {Wed, 10 Jul 2019 15:28:24 +0200},
	title = {Neural Architecture Search: {A} Survey},
	url = {http://jmlr.org/papers/v20/18-598.html},
	volume = 20,
	year = 2019,
	bdsk-url-1 = {http://jmlr.org/papers/v20/18-598.html}}

@article{RenAcm2021,
	_url = {https://doi.org/10.1145/3447582},
	author = {Ren, Pengzhen and Xiao, Yun and Chang, Xiaojun and Huang, Poyao and Li, Zhihui and Chen, Xiaojiang and Wang, Xin},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/journals/csur/RenXCHLCW21.bib},
	date-modified = {2022-06-12 20:13:54 +0200},
	doi = {10.1145/3447582},
	journal = {{ACM} Computing Surveys},
	number = 4,
	pages = {76:1--76:34},
	timestamp = {Thu, 22 Jul 2021 17:57:13 +0200},
	title = {A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions},
	volume = 54,
	year = 2021,
	bdsk-url-1 = {https://doi.org/10.1145/3447582}}

@inproceedings{LiuEccv2018,
	_editor = {Ferrari, Vittorio and Hebert, Martial and Sminchisescu, Cristian and Weiss, Yair},
	_series = {Lecture Notes in Computer Science},
	_url = {https://doi.org/10.1007/978-3-030-01246-5\_2},
	author = {Liu, Chenxi and Zoph, Barret and Neumann, Maxim and Shlens, Jonathon and Hua, Wei and Li, Li{-}Jia and Fei{-}Fei, Li and Yuille, Alan L. and Huang, Jonathan and Murphy, Kevin},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/eccv/LiuZNSHLFYHM18.bib},
	booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part {I}},
	date-modified = {2022-06-12 20:12:46 +0200},
	doi = {10.1007/978-3-030-01246-5\_2},
	pages = {19--35},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	timestamp = {Mon, 22 Jul 2019 14:55:34 +0200},
	title = {Progressive Neural Architecture Search},
	volume = 11205,
	year = 2018,
	bdsk-url-1 = {https://doi.org/10.1007/978-3-030-01246-5%5C_2}}

@inproceedings{RealAaai2019,
	_booktitle = {The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI} 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, {IAAI} 2019, The Ninth {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019},
	_url = {https://doi.org/10.1609/aaai.v33i01.33014780},
	author = {Real, Esteban and Aggarwal, Alok and Huang, Yanping and Le, Quoc V.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/aaai/RealAHL19.bib},
	booktitle = {33rd {AAAI} Conference on Artificial Intelligence, {AAAI} 2019, 31st Innovative Applications of Artificial Intelligence Conference, {IAAI} 2019, 9th {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019},
	date-modified = {2022-06-12 20:11:19 +0200},
	doi = {10.1609/aaai.v33i01.33014780},
	pages = {4780--4789},
	publisher = {{AAAI} Press},
	timestamp = {Tue, 02 Feb 2021 08:00:32 +0100},
	title = {Regularized Evolution for Image Classifier Architecture Search},
	year = {2019},
	bdsk-url-1 = {https://doi.org/10.1609/aaai.v33i01.33014780}}

@inproceedings{GhiasiCvpr2019,
	_booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2019, Long Beach, CA, USA, June 16-20, 2019},
	author = {Ghiasi, Golnaz and Lin, Tsung{-}Yi and Le, Quoc V.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/cvpr/GhiasiLL19.bib},
	booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2019, June 16-20, 2019},
	doi = {10.1109/CVPR.2019.00720},
	pages = {7036--7045},
	publisher = {Computer Vision Foundation / {IEEE}},
	timestamp = {Mon, 30 Aug 2021 17:01:14 +0200},
	title = {{NAS-FPN:} Learning Scalable Feature Pyramid Architecture for Object Detection},
	url = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Ghiasi\_NAS-FPN\_Learning\_Scalable\_Feature\_Pyramid\_Architecture\_for\_Object\_Detection\_CVPR\_2019\_paper.html},
	year = {2019},
	bdsk-url-1 = {http://openaccess.thecvf.com/content%5C_CVPR%5C_2019/html/Ghiasi%5C_NAS-FPN%5C_Learning%5C_Scalable%5C_Feature%5C_Pyramid%5C_Architecture%5C_for%5C_Object%5C_Detection%5C_CVPR%5C_2019%5C_paper.html},
	bdsk-url-2 = {https://doi.org/10.1109/CVPR.2019.00720}}

@inproceedings{YangCvpr2021,
	author = {Yang, Zhaohui and Wang, Yunhe and Chen, Xinghao and Shi, Boxin and Xu, Chao and Xu, Chunjing and Tian, Qi and Xu, Chang},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/cvpr/YangW0SXX0X20.bib},
	booktitle = {2020 {IEEE/CVF} Conference on Computer Vision and Pattern Recognition, {CVPR} 2020, Seattle, WA, USA, June 13-19, 2020},
	doi = {10.1109/CVPR42600.2020.00190},
	pages = {1826--1835},
	publisher = {Computer Vision Foundation / {IEEE}},
	timestamp = {Tue, 31 Aug 2021 14:00:04 +0200},
	title = {{CARS:} Continuous Evolution for Efficient Neural Architecture Search},
	url = {https://openaccess.thecvf.com/content\_CVPR\_2020/html/Yang\_CARS\_Continuous\_Evolution\_for\_Efficient\_Neural\_Architecture\_Search\_CVPR\_2020\_paper.html},
	year = {2020},
	bdsk-url-1 = {https://openaccess.thecvf.com/content%5C_CVPR%5C_2020/html/Yang%5C_CARS%5C_Continuous%5C_Evolution%5C_for%5C_Efficient%5C_Neural%5C_Architecture%5C_Search%5C_CVPR%5C_2020%5C_paper.html},
	bdsk-url-2 = {https://doi.org/10.1109/CVPR42600.2020.00190}}

@inproceedings{ChuEccv2020,
	_booktitle = {Computer Vision - {ECCV} 2020 Workshops - Glasgow, UK, August 23-28, 2020, Proceedings, Part {IV}},
	_editor = {Bartoli, Adrien and Fusiello, Andrea},
	_series = {Lecture Notes in Computer Science},
	_url = {https://doi.org/10.1007/978-3-030-66823-5\_6},
	author = {Chu, Xiangxiang and Zhang, Bo and Xu, Ruijun},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/eccv/ChuZX20.bib},
	booktitle = {Computer Vision -- {ECCV} 2020 Workshops},
	date-modified = {2022-06-12 20:12:23 +0200},
	doi = {10.1007/978-3-030-66823-5_6},
	pages = {99--113},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	timestamp = {Sat, 09 Jan 2021 14:14:40 +0100},
	title = {Multi-objective Reinforced Evolution in Mobile Neural Architecture Search},
	volume = {12538},
	year = 2020,
	bdsk-url-1 = {https://doi.org/10.1007/978-3-030-66823-5%5C_6}}

@inproceedings{TanCvpr2019,
	_booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2019, Long Beach, CA, USA, June 16-20, 2019},
	author = {Tan, Mingxing and Chen, Bo and Pang, Ruoming and Vasudevan, Vijay and Sandler, Mark and Howard, Andrew and Le, Quoc V.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/cvpr/TanCPVSHL19.bib},
	booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2019, June 16-20, 2019},
	doi = {10.1109/CVPR.2019.00293},
	pages = {2820--2828},
	publisher = {Computer Vision Foundation / {IEEE}},
	timestamp = {Mon, 30 Aug 2021 17:01:14 +0200},
	title = {MnasNet: Platform-Aware Neural Architecture Search for Mobile},
	url = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Tan\_MnasNet\_Platform-Aware\_Neural\_Architecture\_Search\_for\_Mobile\_CVPR\_2019\_paper.html},
	year = {2019},
	bdsk-url-1 = {http://openaccess.thecvf.com/content%5C_CVPR%5C_2019/html/Tan%5C_MnasNet%5C_Platform-Aware%5C_Neural%5C_Architecture%5C_Search%5C_for%5C_Mobile%5C_CVPR%5C_2019%5C_paper.html},
	bdsk-url-2 = {https://doi.org/10.1109/CVPR.2019.00293}}

@inproceedings{ZophIclr2017,
	author = {Zoph, Barret and Le, Quoc V.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/iclr/ZophL17.bib},
	booktitle = {5th International Conference on Learning Representations, {ICLR} 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings},
	publisher = {OpenReview.net},
	timestamp = {Thu, 04 Apr 2019 13:20:08 +0200},
	title = {Neural Architecture Search with Reinforcement Learning},
	url = {https://openreview.net/forum?id=r1Ue8Hcxg},
	year = {2017},
	bdsk-url-1 = {https://openreview.net/forum?id=r1Ue8Hcxg}}

@inproceedings{TanIcml2019,
	_booktitle = {Proceedings of the 36th International Conference on Machine Learning, {ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
	_editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
	author = {Tan, Mingxing and Le, Quoc V.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/icml/TanL19.bib},
	booktitle = {36th International Conference on Machine Learning, {ICML} 2019},
	pages = {6105--6114},
	publisher = {{PMLR}},
	series = {Proceedings of Machine Learning Research},
	timestamp = {Tue, 11 Jun 2019 15:37:38 +0200},
	title = {EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
	url = {http://proceedings.mlr.press/v97/tan19a.html},
	volume = {97},
	year = {2019},
	bdsk-url-1 = {http://proceedings.mlr.press/v97/tan19a.html}}

@incollection{AgiolloExtraamas2021,
	_address = {Basel, Switzerland},
	_booktitle = {Explainable and Transparent AI and Multi-Agent Systems. Third International Workshop, EXTRAAMAS 2021, Virtual Event, May 3--7, 2021, Revised Selected Papers},
	_editor = {Calvaresi, Davide and Najjar, Amro and Winikoff, Michael and Fr{\"a}mling, Kary},
	_isbn = {978-3-030-82016-9},
	_series = {Lecture Notes in Computer Science},
	_url = {http://link.springer.com/10.1007/978-3-030-82017-6_5},
	author = {Agiollo, Andrea and Ciatto, Giovanni and Omicini, Andrea},
	booktitle = {Explainable and Transparent AI and Multi-Agent Systems. 3rd International Workshop, EXTRAAMAS 2021},
	date-modified = {2022-06-12 20:12:13 +0200},
	doi = {10.1007/978-3-030-82017-6_5},
	isbn-online = {978-3-030-82017-6},
	issn = {0302-9743},
	keywords = {Neural Architecture Search; Evolutionary Algorithm; Opacity; Interpretability},
	pages = {63--82},
	publisher = {Springer Nature},
	series = {Lecture Notes in Computer Science},
	subseries = {Lecture Notes in Artificial Intelligence},
	title = {{\it Shallow2Deep}: Restraining Neural Networks Opacity through Neural Architecture Search},
	volume = 12688,
	year = 2021,
	bdsk-url-1 = {http://link.springer.com/10.1007/978-3-030-82017-6_5},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-030-82017-6_5}}

@article{WuIeee2021,
	_url = {https://doi.org/10.1109/TNNLS.2020.2978386},
	author = {Wu, Zonghan and Pan, Shirui and Chen, Fengwen and Long, Guodong and Zhang, Chengqi and Yu, Philip S.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/journals/tnn/WuPCLZY21.bib},
	date-modified = {2022-06-12 20:17:34 +0200},
	doi = {10.1109/TNNLS.2020.2978386},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	number = 1,
	pages = {4--24},
	timestamp = {Tue, 01 Jun 2021 09:59:58 +0200},
	title = {A Comprehensive Survey on Graph Neural Networks},
	volume = 32,
	year = 2021,
	bdsk-url-1 = {https://doi.org/10.1109/TNNLS.2020.2978386}}

@article{ZhouAi2020,
	_url = {https://doi.org/10.1016/j.aiopen.2021.01.001},
	author = {Zhou, Jie and Cui, Ganqu and Hu, Shengding and Zhang, Zhengyan and Yang, Cheng and Liu, Zhiyuan and Wang, Lifeng and Li, Changcheng and Sun, Maosong},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/journals/aiopen/ZhouCHZYLWLS20.bib},
	date-modified = {2022-06-12 20:22:33 +0200},
	doi = {10.1016/j.aiopen.2021.01.001},
	journal = {{AI} Open},
	pages = {57--81},
	timestamp = {Fri, 13 Aug 2021 14:56:35 +0200},
	title = {Graph neural networks: {A} review of methods and applications},
	volume = {1},
	year = {2020},
	bdsk-url-1 = {https://doi.org/10.1016/j.aiopen.2021.01.001}}

@article{FungNature2021,
	_url = {https://doi.org/10.1038/s41524-021-00554-0},
	author = {Fung, Victor and Zhang, Jiaxin and Juarez, Eric and Sumpter, Bobby G.},
	da = {2021/06/03},
	date-added = {2021-10-11 18:10:38 +0200},
	date-modified = {2022-06-12 20:04:16 +0200},
	doi = {10.1038/s41524-021-00554-0},
	id = {Fung2021},
	isbn = {2057-3960},
	journal = {npj Computational Materials},
	number = {1},
	pages = {84},
	title = {Benchmarking graph neural networks for materials chemistry},
	ty = {JOUR},
	volume = {7},
	year = {2021},
	abstract = {Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis. However, a consistent benchmark of these models remains lacking, hindering the development and consistent evaluation of new models in the materials field. Here, we present a workflow and testing platform, MatDeepLearn, for quickly and reproducibly assessing and comparing GNNs and other machine learning models. We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry. From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized. We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs, due to learned rather than defined representations. Meanwhile several weaknesses of GNNs are also observed including high data requirements, and suggestions for further improvement for applications in materials chemistry are discussed.},
	bdsk-url-1 = {https://doi.org/10.1038/s41524-021-00554-0}}

@inproceedings{FanWww2019,
	_booktitle = {The World Wide Web Conference, {WWW} 2019, San Francisco, CA, USA, May 13-17, 2019},
	_editor = {Liu, Ling and White, Ryen W. and Mantrach, Amin and Silvestri, Fabrizio and McAuley, Julian J. and Baeza{-}Yates, Ricardo and Zia, Leila},
	_url = {https://doi.org/10.1145/3308558.3313488},
	author = {Fan, Wenqi and Ma, Yao and Li, Qing and He, Yuan and Zhao, Yihong Eric and Tang, Jiliang and Yin, Dawei},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/www/Fan0LHZTY19.bib},
	booktitle = {The World Wide Web Conference, {WWW} 2019},
	date-modified = {2022-06-12 20:03:29 +0200},
	doi = {10.1145/3308558.3313488},
	pages = {417--426},
	publisher = {{ACM}},
	timestamp = {Mon, 16 Dec 2019 18:15:56 +0100},
	title = {Graph Neural Networks for Social Recommendation},
	year = {2019},
	bdsk-url-1 = {https://doi.org/10.1145/3308558.3313488}}

@article{WangAcm2019,
	_url = {https://doi.org/10.1145/3326362},
	author = {Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/journals/tog/WangSLSBS19.bib},
	date-modified = {2022-06-12 20:15:12 +0200},
	doi = {10.1145/3326362},
	journal = {{ACM} Transactions on Graphics},
	number = 5,
	pages = {146:1--146:12},
	timestamp = {Thu, 16 Apr 2020 07:55:10 +0200},
	title = {Dynamic Graph {CNN} for Learning on Point Clouds},
	volume = 38,
	year = 2019,
	bdsk-url-1 = {https://doi.org/10.1145/3326362}}

@inproceedings{HamiltonNips2017,
	_booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, {USA}},
	_editor = {Guyon, Isabelle and von Luxburg, Ulrike and Bengio, Samy and Wallach, Hanna M. and Fergus, Rob and Vishwanathan, S. V. N. and Garnett, Roman},
	author = {Hamilton, William L. and Ying, Zhitao and Leskovec, Jure},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/nips/HamiltonYL17.bib},
	booktitle = {Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017},
	pages = {1024--1034},
	timestamp = {Thu, 21 Jan 2021 15:15:21 +0100},
	title = {Inductive Representation Learning on Large Graphs},
	url = {https://proceedings.neurips.cc/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html},
	year = {2017},
	bdsk-url-1 = {https://proceedings.neurips.cc/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html}}

@inproceedings{YuIjcai2018,
	_booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, {IJCAI} 2018, July 13-19, 2018, Stockholm, Sweden},
	_editor = {Lang, J{\'{e}}r{\^{o}}me},
	_url = {https://doi.org/10.24963/ijcai.2018/505},
	author = {Yu, Bing and Yin, Haoteng and Zhu, Zhanxing},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/ijcai/YuYZ18.bib},
	booktitle = {27th International Joint Conference on Artificial Intelligence, {IJCAI} 2018},
	date-modified = {2022-06-12 20:19:20 +0200},
	doi = {10.24963/ijcai.2018/505},
	pages = {3634--3640},
	publisher = {ijcai.org},
	timestamp = {Tue, 20 Aug 2019 16:19:08 +0200},
	title = {Spatio-Temporal Graph Convolutional Networks: {A} Deep Learning Framework for Traffic Forecasting},
	year = {2018},
	bdsk-url-1 = {https://doi.org/10.24963/ijcai.2018/505}}

@inproceedings{GoodfellowNips2014,
	_booktitle = {Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada},
	_editor = {Ghahramani, Zoubin and Welling, Max and Cortes, Corinna and Lawrence, Neil D. and Weinberger, Kilian Q.},
	author = {Goodfellow, Ian J. and Pouget{-}Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde{-}Farley, David and Ozair, Sherjil and Courville, Aaron C. and Bengio, Yoshua},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/nips/GoodfellowPMXWOCB14.bib},
	booktitle = {Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014},
	pages = {2672--2680},
	timestamp = {Thu, 21 Jan 2021 15:15:23 +0100},
	title = {Generative Adversarial Nets},
	url = {https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html},
	year = {2014},
	bdsk-url-1 = {https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html}}

@inproceedings{YouIcml2018,
	_booktitle = {Proceedings of the 35th International Conference on Machine Learning, {ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July 10-15, 2018},
	_editor = {Dy, Jennifer G. and Krause, Andreas},
	author = {You, Jiaxuan and Ying, Rex and Ren, Xiang and Hamilton, William L. and Leskovec, Jure},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/icml/YouYRHL18.bib},
	booktitle = {35th International Conference on Machine Learning, {ICML} 2018},
	pages = {5694--5703},
	publisher = {{PMLR}},
	series = {Proceedings of Machine Learning Research},
	timestamp = {Mon, 01 Mar 2021 13:46:59 +0100},
	title = {GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models},
	url = {http://proceedings.mlr.press/v80/you18a.html},
	volume = {80},
	year = {2018},
	bdsk-url-1 = {http://proceedings.mlr.press/v80/you18a.html}}

@article{LiCorr2018,
	author = {Li, Yujia and Vinyals, Oriol and Dyer, Chris and Pascanu, Razvan and Battaglia, Peter W.},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/journals/corr/abs-1803-03324.bib},
	eprint = {1803.03324},
	eprinttype = {arXiv},
	journal = {CoRR},
	timestamp = {Wed, 24 Jul 2019 11:32:11 +0200},
	title = {Learning Deep Generative Models of Graphs},
	url = {http://arxiv.org/abs/1803.03324},
	volume = {abs/1803.03324},
	year = {2018},
	bdsk-url-1 = {http://arxiv.org/abs/1803.03324}}

@article{DecaoCorr2018,
	author = {De Cao, Nicola and Kipf, Thomas},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/journals/corr/abs-1805-11973.bib},
	eprint = {1805.11973},
	eprinttype = {arXiv},
	journal = {CoRR},
	timestamp = {Mon, 13 Aug 2018 16:47:58 +0200},
	title = {MolGAN: An Implicit Generative Model for Small Molecular Graphs},
	url = {http://arxiv.org/abs/1805.11973},
	volume = {abs/1805.11973},
	year = {2018},
	bdsk-url-1 = {http://arxiv.org/abs/1805.11973}}

@article{DongTpami2021,
	_note = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}},
	author = {Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},
	date-modified = {2022-06-12 20:00:37 +0200},
	doi = {10.1109/TPAMI.2021.3054824},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
	title = {{NATS-Bench}: Benchmarking {NAS} Algorithms for Architecture Topology and Size},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1109/TPAMI.2021.3054824}}

@inproceedings{YingPmlr2019,
	_address = {Long Beach, California, USA},
	_booktitle = {Proceedings of the 36th International Conference on Machine Learning},
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@inproceedings{LiuIclr2019,
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@inproceedings{LuoNips2018,
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@inproceedings{PhamIcml2018,
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	editor = {Dy, Jennifer G. and Krause, Andreas},
	pages = {4092--4101},
	publisher = {{PMLR}},
	series = {Proceedings of Machine Learning Research},
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