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2020 – today
- 2024
[j2]Wataru Kumagai, Akiyoshi Sannai, Makoto Kawano:
Universal approximation with neural networks on function spaces. J. Exp. Theor. Artif. Intell. 36(7): 1089-1100 (2024)- [j1]Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai:
Invariant and Equivariant Reynolds Networks. J. Mach. Learn. Res. 25: 42:1-42:36 (2024) - [i15]Toshinori Kitamura, Tadashi Kozuno, Masahiro Kato, Yuki Ichihara, Soichiro Nishimori, Akiyoshi Sannai, Sho Sonoda, Wataru Kumagai, Yutaka Matsuo:
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees. CoRR abs/2401.17780 (2024) - [i14]Masayuki Takayama, Tadahisa Okuda
, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai:
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach. CoRR abs/2402.01454 (2024) - [i13]Koji Hashimoto, Yuji Hirono, Akiyoshi Sannai:
Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE. CoRR abs/2402.02362 (2024) - [i12]Akiyoshi Sannai, Yuuki Takai, Matthieu Cordonnier:
Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation under Symmetry. CoRR abs/2409.16922 (2024) - 2023
- [i11]Ryutaro Yamauchi, Sho Sonoda, Akiyoshi Sannai, Wataru Kumagai:
LPML: LLM-Prompting Markup Language for Mathematical Reasoning. CoRR abs/2309.13078 (2023) - 2022
- [i10]Akiyoshi Sannai, Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Naoki Hamada:
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization. CoRR abs/2205.11099 (2022) - 2021
- [c5]Yuuki Takai, Akiyoshi Sannai, Matthieu Cordonnier:
On the number of linear functions composing deep neural network: Towards a refined definition of neural networks complexity. AISTATS 2021: 3799-3807 - [c4]Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, Yutaka Matsuo:
Group Equivariant Conditional Neural Processes. ICLR 2021 - [c3]Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano:
Improved generalization bounds of group invariant / equivariant deep networks via quotient feature spaces. UAI 2021: 771-780 - [i9]Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai, Yusuke Iwasawa, Yutaka Matsuo:
Group Equivariant Conditional Neural Processes. CoRR abs/2102.08759 (2021) - [i8]Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada:
Approximate Bayesian Computation of Bézier Simplices. CoRR abs/2104.04679 (2021) - [i7]Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai:
Equivariant and Invariant Reynolds Networks. CoRR abs/2110.08092 (2021) - 2020
- [c2]Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada:
Asymptotic Risk of Bézier Simplex Fitting. AAAI 2020: 2416-2424 - [i6]Yuuki Takai, Akiyoshi Sannai, Matthieu Cordonnier:
On the Number of Linear Functions Composing Deep Neural Network: Towards a Refined Definition of Neural Networks Complexity. CoRR abs/2010.12125 (2020) - [i5]Wataru Kumagai, Akiyoshi Sannai:
Universal Approximation Theorem for Equivariant Maps by Group CNNs. CoRR abs/2012.13882 (2020)
2010 – 2019
- 2019
- [c1]Ken Kobayashi, Naoki Hamada, Akiyoshi Sannai, Akinori Tanaka, Kenichi Bannai, Masashi Sugiyama:
Bézier Simplex Fitting: Describing Pareto Fronts of Simplicial Problems with Small Samples in Multi-Objective Optimization. AAAI 2019: 2304-2313 - [i4]Akiyoshi Sannai, Yuuki Takai, Matthieu Cordonnier:
Universal approximations of permutation invariant/equivariant functions by deep neural networks. CoRR abs/1903.01939 (2019) - [i3]Akinori Tanaka, Akiyoshi Sannai, Ken Kobayashi, Naoki Hamada:
Asymptotic Risk of Bezier Simplex Fitting. CoRR abs/1906.06924 (2019) - [i2]Akiyoshi Sannai, Masaaki Imaizumi:
Improved Generalization Bound of Permutation Invariant Deep Neural Networks. CoRR abs/1910.06552 (2019) - 2018
- [i1]Akiyoshi Sannai:
Reconstruction of training samples from loss functions. CoRR abs/1805.07337 (2018)
Coauthor Index
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last updated on 2024-10-18 19:32 CEST by the dblp team
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