| Universal approximations of permutation invariant/equivariant functions by deep neural networks A Sannai, Y Takai, M Cordonnier arXiv preprint arXiv:1903.01939, 2019 | 74 | 2019 |
| Characterization of varieties of Fano type via singularities of Cox rings Y Gongyo, S Okawa, A Sannai, S Takagi arXiv preprint arXiv:1201.1133, 2012 | 64 | 2012 |
| Improved generalization bounds of group invariant/equivariant deep networks via quotient feature spaces A Sannai, M Imaizumi, M Kawano Uncertainty in artificial intelligence, 771-780, 2021 | 40 | 2021 |
| Group equivariant conditional neural processes M Kawano, W Kumagai, A Sannai, Y Iwasawa, Y Matsuo arXiv preprint arXiv:2102.08759, 2021 | 22 | 2021 |
| On dual F-signature A Sannai International Mathematics Research Notices 2015 (1), 197-211, 2015 | 21 | 2015 |
| Galois extensions, plus closure, and maps on local cohomology A Sannai, AK Singh Advances in Mathematics 229 (3), 1847-1861, 2012 | 19 | 2012 |
| Bézier simplex fitting: Describing Pareto fronts of simplicial problems with small samples in multi-objective optimization K Kobayashi, N Hamada, A Sannai, A Tanaka, K Bannai, M Sugiyama Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 2304-2313, 2019 | 15 | 2019 |
| Universal approximation theorem for equivariant maps by group cnns W Kumagai, A Sannai arXiv preprint arXiv:2012.13882, 2020 | 14 | 2020 |
| A characterization of ordinary abelian varieties by the Frobenius push-forward of the structure sheaf A Sannai, H Tanaka Mathematische Annalen 366, 1067-1087, 2016 | 12 | 2016 |
| Reconstruction of training samples from loss functions A Sannai arXiv preprint arXiv:1805.07337, 2018 | 7 | 2018 |
| Asymptotic risk of Bézier simplex fitting A Tanaka, A Sannai, K Kobayashi, N Hamada Proceedings of the AAAI Conference on Artificial Intelligence 34 (03), 2416-2424, 2020 | 6 | 2020 |
| LPML: llm-prompting markup language for mathematical reasoning R Yamauchi, S Sonoda, A Sannai, W Kumagai arXiv preprint arXiv:2309.13078, 2023 | 4 | 2023 |
| On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity Y Takai, A Sannai, M Cordonnier International Conference on Artificial Intelligence and Statistics, 3799-3807, 2021 | 4 | 2021 |
| A characterization of ordinary abelian varieties by the Frobenius push-forward of the structure sheaf II S Ejiri, A Sannai International Mathematics Research Notices 2019 (19), 5975-5988, 2019 | 4 | 2019 |
| Improved generalization bound of permutation invariant deep neural networks A Sannai, M Imaizumi | 4 | 2019 |
| Jet schemes of homogeneous hypersurfaces S Ishii, A Sannai, K Watanabe arXiv preprint arXiv:1109.5321, 2011 | 4 | 2011 |
| Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach M Takayama, T Okuda, T Pham, T Ikenoue, S Fukuma, S Shimizu, ... arXiv preprint arXiv:2402.01454, 2024 | 3 | 2024 |
| F-signature of graded Gorenstein rings A Sannai, K Watanabe Journal of Pure and Applied Algebra 215 (9), 2190-2195, 2011 | 3 | 2011 |
| Universal approximation with neural networks on function spaces W Kumagai, A Sannai, M Kawano Journal of Experimental & Theoretical Artificial Intelligence, 1-12, 2022 | 2 | 2022 |
| Infinitely generated symbolic Rees algebras over finite fields A Sannai, H Tanaka Algebra Number Theory 13 (8), 1879-1891, 2019 | 2 | 2019 |
Wataru KumagaiOmron Sinic XVerified email at sinicx.com
