[42] Roman Orus. A practical introduction to tensor networks: Matrix product states and projected entangled pair states. Annals of physics, 349:117-158, 2014.
[43] Samet Oymak and Mahdi Soltanolkotabi. End-to-end learning of a convolutional neural network via deep tensor decomposition. arXiv preprint arXiv:1805.06523, 2018.
[44] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. In NIPS-W, 2017.
[45] Giovanni Puccetti. Measuring linear correlation between random vectors. Available at SSRN 3116066, 2022.
[46] Jack Rae and Ali Razavi. Do transformers need deep long-range memory? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 2020. Association for Computational Linguistics. URL https://www.aclweb.org/anthology/2020.acl-main.672.
[47] Noam Razin and Nadav Cohen. Implicit regularization in deep learning may not be explainable by norms. In Advances in Neural Information Processing Systems (NeurIPS), 2020.
[48] Noam Razin, Asaf Maman, and Nadav Cohen. Implicit regularization in tensor factorization. International Conference on Machine Learning (ICML), 2021.
[49] Noam Razin, Asaf Maman, and Nadav Cohen. Implicit regularization in hierarchical tensor factorization and deep convolutional neural networks. International Conference on Machine Learning (ICML), 2022.
[50] Noam Razin, Tom Verbin, and Nadav Cohen. On the ability of graph neural networks to model interactions between vertices. arXiv preprint arXiv:2211.16494, 2022.
[51] Lorenzo Rosasco, Mikhail Belkin, and Ernesto De Vito. On learning with integral operators. J. Mach. Learn. Res., 11:905–934, mar 2010. ISSN 1532-4435.
[52] Siddhartha Sen. Average entropy of a quantum subsystem. Physical review letters, 77(1):1, 1996.
[53] Shai Shalev-Shwartz, Ohad Shamir, and Shaked Shammah. Failures of gradient-based deep learning. In International Conference on Machine Learning, pages 3067-3075. PMLR, 2017.
[54] Or Sharir and Amnon Shashua. On the expressive power of overlapping architectures of deep learning. International Conference on Learning Representations (ICLR), 2018.
[55] Or Sharir, Ronen Tamari, Nadav Cohen, and Amnon Shashua. Tensorial mixture models. arXiv preprint arXiv:1610.04167, 2016.
[56] Ravid Shwartz-Ziv and Amitai Armon. Tabular data: Deep learning is not all you need. Information Fusion, 81:84-90, 2022.
[57] Daniel A Spielman and Shang-Hua Teng. Spectral sparsification of graphs. SIAM Journal on Computing, 40(4):981-1025, 2011.
[58] E Miles Stoudenmire. Learning relevant features of data with multi-scale tensor networks. Quantum Science and Technology, 3(3):034003, 2018.
[59] Edwin Stoudenmire and David J Schwab. Supervised learning with tensor networks. In Advances in Neural Information Processing Systems (NeurIPS), 2016.
[60] Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo. Openml: networked science in machine learning. SIGKDD Explorations, 15(2):49-60, 2013. doi: 10.1145/2641190.2641198. URL http://doi.acm.org/10.1145/2641190.264119.
[61] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[62] Qingcan Wang et al. Exponential convergence of the deep neural network approximation for analytic functions. arXiv preprint arXiv:1807.00297, 2018.
[63] Xiang Wang, Chenwei Wu, Jason D Lee, Tengyu Ma, and Rong Ge. Beyond lazy training for overparameterized tensor decomposition. Advances in Neural Information Processing Systems, 33:21934-21944, 2020.