[30] William L. Hamilton, Zhitao Ying, and Jure Leskovec. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems, 2017.
[31] Benjamin Hilprecht and Carsten Binnig. Zero-shot cost models for out-of-the-box learned cost prediction. 15(11):2361-2374, jul 2022. ISSN 2150-8097. doi: 10.14778/3551793.3551799. URL https://doi.org/10.14778/3551793.3551799.
[32] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:22118-22133, 2020.
[33] Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. Ogb-lsc: A large-scale challenge for machine learning on graphs. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021.
[34] Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. Adaptive sampling towards fast graph representation learning. Advances in neural information processing systems, 31, 2018.
[35] Vijay Janapa Reddi, David Kanter, Peter Mattson, Jared Duke, Thai Nguyen, Ramesh Chukka, Ken Shiring, Koan-Sin Tan, Mark Charlebois, William Chou, Mostafa El-Khamy, Jungwook Hong, Tom St John, Cindy Trinh, Michael Buch, Mark Mazumder, Relja Markovic, Thomas Atta, Fatih Cakir, Masoud Charkhabi, Xiaodong Chen, Cheng-Ming Chiang, Dave Dexter, Terry Heo, Guenther Schmuelling, Maryam Shabani, and Dylan Zika. Mlperf mobile inference benchmark: An industry-standard open-source machine learning benchmark for on-device ai. In D. Marculescu, Y. Chi, and C. Wu, editors, Proceedings of Machine Learning and Systems, volume 4, pages 352-369, 2022. URL https://proceedings.mlsys.org/paper_files/paper/2022/file/7eabe3a1649ffa2b3ff8c02ebfd5659f-Paper.pdf.
[36] Zhihao Jia, Oded Padon, James Thomas, Todd Warszawski, Matei Zaharia, and Alex Aiken. TASO: Optimizing Deep Learning Computation with Automatic Generation of Graph Substitutions. In Proceedings of the 27th ACM Symposium on Operating Systems Principles, SOSP '19, page 47-62, New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450368735. doi: 10.1145/3341301.3359630.
[37] Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken. Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc. In I. Dhillon, D. Papailiopoulos, and V. Sze, editors, Proceedings of MLSys Conference, volume 2, pages 187-198, 2020.
[38] Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, Rick Boyle, Pierre-luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell, Mike Daley, Matt Dau, Jeffrey Dean, Ben Gelb, Tara Vazir Ghaemmaghami, Rajendra Gottipati, William Gulland, Robert Hagmann, C. Richard Ho, Doug Hogberg, John Hu, Robert Hundt, Dan Hurt, Julian Ibarz, Aaron Jaffey, Alek Jaworski, Alexander Kaplan, Harshit Khaitan, Daniel Killebrew, Andy Koch, Naveen Kumar, Steve Lacy, James Laudon, James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross, Matt Ross, Amir Salek, Emad Samadiani, Chris Severn, Gregory Sizikov, Matthew Snelham, Jed Souter, Dan Steinberg, Andy Swing, Mercedes Tan, Gregory Thorson, Bo Tian, Horia Toma, Erick Tuttle, Vijay Vasudevan, Richard Walter, Walter Wang, Eric Wilcox, and Doe Hyun Yoon. In-Datacenter Performance Analysis of a Tensor Processing Unit. In Proceedings of the 44th Annual International Symposium on Computer Architecture, ISCA '17, 2017.
[39] Norman P. Jouppi, Doe Hyun Yoon, George Kurian, Sheng Li, Nishant Patil, James Laudon, Cliff Young, and David Patterson. A domain-specific supercomputer for training deep neural networks. Commun. ACM, 63(7):67-78, June 2020. ISSN 0001-0782. doi: 10.1145/3360307. URL https://doi.org/10.1145/3360307.
[40] Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. Learning and evaluating contextual embedding of source code. In Proceedings of the 37th International Conference on Machine Learning, ICML'20. JMLR.org, 2020.