[1] S. K. Ainsworth, J. Hayase, and S. Srinivasa. Git re-basin: Merging models modulo permutation symmetries. arXiv preprint arXiv:2209.04836, 2022.
[2] M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, and N. De Freitas. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems, 29, 2016.
[3] M. Bauer, E. Dupont, A. Brock, D. Rosenbaum, J. Schwarz, and H. Kim. Spatial functa: Scaling functa to imagenet classification and generation. arXiv preprint arXiv:2302.03130, 2023.
[4] J. Brea, B. Simsek, B. Illing, and W. Gerstner. Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape. arXiv preprint arXiv:1907.02911, 2019.
[5] T. Cohen and M. Welling. Group equivariant convolutional networks. In International conference on machine learning, pages 2990-2999. PMLR, 2016.
[6] L. De Luigi, A. Cardace, R. Spezialetti, P. Z. Ramirez, S. Salti, and L. Di Stefano. Deep learning on implicit neural representations of shapes. arXiv preprint arXiv:2302.05438, 2023.
[7] L. Deutsch, E. Nijkamp, and Y. Yang. A generative model for sampling high-performance and diverse weights for neural networks. arXiv preprint arXiv:1905.02898, 2019.
[8] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
[9] E. Dupont, Y. W. Teh, and A. Doucet. Generative models as distributions of functions. arXiv preprint arXiv:2102.04776, 2021.
[10] E. Dupont, H. Kim, S. Eslami, D. Rezende, and D. Rosenbaum. From data to functa: Your data point is a function and you should treat it like one. arXiv preprint arXiv:2201.12204, 2022.
[11] G. Eilertsen, D. Jonsson, T. Ropinski, J. Unger, and A. Ynnerman. Classifying the classifier: dissecting the weight space of neural networks. arXiv preprint arXiv:2002.05688, 2020.
[12] R. Entezari, H. Sedghi, O. Saukh, and B. Neyshabur. The role of permutation invariance in linear mode connectivity of neural networks. arXiv preprint arXiv:2110.06296, 2021.
[13] M. Finzi, M. Welling, and A. G. Wilson. A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups. In International Conference on Machine Learning, pages 3318-3328. PMLR, 2021.
[14] T. Garipov, P. Izmailov, D. Podoprikhin, D. P. Vetrov, and A. G. Wilson. Loss surfaces, mode connectivity, and fast ensembling of DNNs. Advances in neural information processing systems, 31, 2018.
[15] D. Ha, A. Dai, and Q. V. Le. Hypernetworks. arXiv preprint arXiv:1609.09106, 2016.
[16] J. Hartford, D. Graham, K. Leyton-Brown, and S. Ravanbakhsh. Deep models of interactions across sets. In International Conference on Machine Learning, pages 1909-1918. PMLR, 2018.
[17] R. Hecht-Nielsen. On the algebraic structure of feedforward network weight spaces. In Advanced Neural Computers, pages 129-135. Elsevier, 1990.
[18] Y. Jiang, D. Krishnan, H. Mobahi, and S. Bengio. Predicting the generalization gap in deep networks with margin distributions. In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=HJlQfnCqKX.
[19] Y. Jiang, P. Natekar, M. Sharma, S. K. Aithal, D. Kashyap, N. Subramanyam, C. Lassance, D. M. Roy, G. K. Dziugaite, S. Gunasekar, et al. Methods and analysis of the first competition in predicting generalization of deep learning. In NeurIPS 2020 Competition and Demonstration Track, pages 170–190. PMLR, 2021.