[46] Edward Grant, Marcello Benedetti, Shuxiang Cao, Andrew Hallam, Joshua Lockhart, Vid Stojevic, Andrew G Green, and Simone Severini. Hierarchical quantum classifiers. npj Quantum Information, 4(1): 1-8, 2018.
[47] Wolfgang Hackbusch. On the efficient evaluation of coalescence integrals in population balance models. Computing, 78(2):145-159, 2006.
[48] Michael Hamann, Gerd Lindner, Henning Meyerhenke, Christian L Staudt, and Dorothea Wagner. Structure-preserving sparsification methods for social networks. Social Network Analysis and Mining, 6 (1):1-22, 2016.
[49] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems (NeurIPS), 2017.
[50] William L Hamilton. Graph representation learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 14(3):1-159, 2020.
[51] Robert J Harrison, George I Fann, Takeshi Yanai, and Gregory Beylkin. Multiresolution quantum chemistry in multiwavelet bases. In International Conference on Computational Science, pages 103-110. Springer, 2003.
[52] 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. In Advances in Neural Information Processing Systems (NeurIPS), 2020.
[53] Chenqing Hua, Guillaume Rabusseau, and Jian Tang. High-order pooling for graph neural networks with tensor decomposition. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
[54] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning (ICML), 2015.
[55] Nicolas Keriven and Gabriel Peyre. Universal invariant and equivariant graph neural networks. In Advances in Neural Information Processing Systems (NeurIPS), 2019.
[56] Valentin Khrulkov, Alexander Novikov, and Ivan Oseledets. Expressive power of recurrent neural networks. International Conference on Learning Representations (ICLR), 2018.
[57] Valentin Khrulkov, Oleksii Hrinchuk, and Ivan Oseledets. Generalized tensor models for recurrent neural networks. International Conference on Learning Representations (ICLR), 2019.
[58] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[59] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations (ICLR), 2017.
[60] Jure Leskovec and Christos Faloutsos. Sampling from large graphs. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 631-636, 2006.
[61] Yoav Levine, Or Sharir, and Amnon Shashua. Benefits of depth for long-term memory of recurrent networks. International Conference on Learning Representations (ICLR) Workshop, 2018.
[62] Yoav Levine, David Yakira, Nadav Cohen, and Amnon Shashua. Deep learning and quantum entanglement: Fundamental connections with implications to network design. International Conference on Learning Representations (ICLR), 2018.
[63] Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua. Quantum entanglement in deep learning architectures. Physical review letters, 122(6):065301, 2019.
[64] Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, and Amnon Shashua. Limits to depth efficiencies of self-attention. In Advances in Neural Information Processing Systems (NeurIPS), 2020.
[65] Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen, and Amnon Shashua. The inductive bias of in-context learning: Rethinking pretraining example design. International Conference on Learning Representations (ICLR), 2022.
[66] Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem. Deepergcn: All you need to train deeper gcns. arXiv preprint arXiv:2006.07739, 2020.