Benjamin Chamberlain, James Rowbottom, Davide Eynard, Francesco Di Giovanni, Xiaowen Dong, and Michael Bronstein. Beltrami flow and neural diffusion on graphs. Advances in Neural Information Processing Systems, 34:1594-1609, 2021.
Ines Chami, Rex Ying, Christopher Ré, and Jure Leskovec. Hyperbolic graph convolutional neural networks, 2019.
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik. Emmist: Extending mnist to handwritten letters. In 2017 international joint conference on neural networks (IJCNN), pages 2921-2926. IEEE, 2017.
Giacomo Como, Raffaele Damiano, and Fabio Fagnani. Discrete bayesian optimization algorithms and applications. 2020.
Jiaxu Cui and Bo Yang. Graph bayesian optimization: Algorithms, evaluations and applications. ArXiv, abs/1805.01157, 2018.
Jiaxu Cui, Bo Yang, Bingyi Sun, Xia Ben Hu, and Jiming Liu. Scalable and parallel deep bayesian optimization on attributed graphs. IEEE Transactions on Neural Networks and Learning Systems, 33:103-116, 2020.
Li Deng. The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6):141-142, 2012.
Yilun Du, Katherine Collins, Joshua B. Tenenbaum, and Vincent Sitzmann. Learning signal-agnostic manifolds of neural fields. In Neural Information Processing Systems, 2021.
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter. Neural architecture search: A survey. J. Mach. Learn. Res., 20:55:1-55:21, 2018.
Charles Fefferman, Sanjoy K. Mitter, and Hariharan Narayanan. Testing the manifold hypothesis. arXiv: Statistics Theory, 2013.
Marco Fumero, Luca Di Cosmo, Simone Melzi, and Emanuele Rodolà. Learning disentangled representations via product manifold projection. ArXiv, abs/2103.01638, 2021.
C Lee Giles, Kurt D Bollacker, and Steve Lawrence. Citeseer: An automatic citation indexing system. In Proceedings of the third ACM conference on Digital libraries, pages 89-98, 1998.
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014.
Dhananjay Gopal, Aniruddha V. Deshmukh, Abhay S. Ranadive, and Shubham Yadav. An introduction to metric spaces. 2020.
Albert Gu, Frederic Sala, Beliz Gunel, and Christopher Ré. Learning mixed-curvature representations in product spaces. In International Conference on Learning Representations, 2018.
Søren Hauberg, Oren Freifeld, and Michael Black. A geometric take on metric learning. Advances in Neural Information Processing Systems, 25, 2012.
Wolfgang Henke and Wolfgang Nettekoven. The hyperbolic  $n$ -space as a graph in Euclidean  $(6n - 6)$ -space. Manuscripta Math., 59(1):13-20, 1987.
Felix Hensel, Michael Moor, and Bastian Alexander Rieck. A survey of topological machine learning methods. Frontiers in Artificial Intelligence, 4, 2021.
Chin-Wei Huang, Milad Aghajohari, A. Bose, P. Panangaden, and Aaron C. Courville. Riemannian diffusion models. *ArXiv*, abs/2208.07949, 2022.
Paul Jungeblut, Linda Kleist, and Tillmann Miltzow. The complexity of the Hausdorff distance. In International Symposium on Computational Geometry, 2021.
Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, and Michael Bronstein. Differentiable graph module (DGM) for graph convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2022.