[35] Behnam Neyshabur and Nathan Srebro. On symmetric and asymmetric lshs for inner product search. In International Conference on Machine Learning, pages 1926-1934. PMLR, 2015.
[36] Anna Pagh, Rasmus Pagh, and S Srinivasa Rao. An optimal bloom filter replacement. In Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms, pages 823-829, 2005.
[37] Hao Peng, Nikolaos Pappas, Dani Yogatama, Roy Schwartz, Noah A Smith, and Lingpeng Kong. Random feature attention. arXiv preprint arXiv:2103.02143, 2021.
[38] Ninh Pham and Rasmus Pagh. Fast and scalable polynomial kernels via explicit feature maps. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 239-247, 2013.
[39] Maxim Raginsky and Svetlana Lazebnik. Locality-sensitive binary codes from shift-invariant kernels. Advances in neural information processing systems, 22, 2009.
[40] Ali Rahimi and Benjamin Recht. Random features for large-scale kernel machines. Advances in neural information processing systems, 20, 2007.
[41] Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 11 2019. URL https://arxiv.org/abs/1908.10084.
[42] Hongyu Ren, Weihua Hu, and Jure Leskovec. Query2box: Reasoning over knowledge graphs in vector space using box embeddings. arXiv preprint arXiv:2002.05969, 2020.
[43] Indradyumna Roy, Abir De, and Soumen Chakrabarti. Adversarial permutation guided node representations for link prediction. In AAAI Conference, 2021. URL https://arxiv.org/abs/2012.08974.
[44] Walter Rudin. Fourier analysis on groups. Courier Dover Publications, 2017.
[45] Fumin Shen, Wei Liu, Shaoting Zhang, Yang Yang, and Heng Tao Shen. Learning binary codes for maximum inner product search. In Proceedings of the IEEE International Conference on Computer Vision, pages 4148-4156, 2015.
[46] Anshumali Shrivastava and Ping Li. Asymmetric LSH (ALSH) for sublinear time maximum inner product search (MIPS). NeurIPS, abs/1405.5869, 2014. URL https://arxiv.org/pdf/1405.5869.pdf.
[47] Anshumali Shrivastava and Ping Li. In defense of minhash over simhash. In Artificial Intelligence and Statistics, pages 886-894. PMLR, 2014.
[48] Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios, and Michalis Vazirgiannis. Rep the set: Neural networks for learning set representations. In Silvia Chiappa and Roberto Calandra, editors, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, volume 108 of Proceedings of Machine Learning Research, pages 1410-1420. PMLR, 26-28 Aug 2020. URL https://proceedings.mlr.press/v108/skianis20a.html.
[49] Zhao Song, David Woodruff, Zheng Yu, and Lichen Zhang. Fast sketching of polynomial kernels of polynomial degree. In International Conference on Machine Learning, pages 9812-9823. PMLR, 2021.
[50] Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems, 33:7537–7547, 2020.
[51] Manolis Terrovitis, Panagiotis Bouros, Panos Vassiliadis, Timos Sellis, and Nikos Mamoulis. Efficient answering of set containment queries for skewed item distributions. In Proceedings of the 14th International Conference on Extending Database Technology, pages 225–236, 2011.