Nonparametric Weight Initialization of Neural Networks via Integral Representation. - Sho Sonoda; Noboru Murata (CoRR, 2013)

Sampling Hidden Parameters from Oracle Distribution. - Sho Sonoda; Noboru Murata (ICANN, 2014)

Neural Network with Unbounded Activations is Universal Approximator. - Sho Sonoda; Noboru Murata (CoRR, 2015)

Decoding Stacked Denoising Autoencoders. - Sho Sonoda; Noboru Murata (CoRR, 2016)

Transportation analysis of denoising autoencoders: a novel method for analyzing deep neural networks. - Sho Sonoda; Noboru Murata (CoRR, 2017)

Localizing Current Dipoles from EEG Data Using a Birth-Death Process. - Keita Nakamura; Sho Sonoda; Hideitsu Hino; Masahiro Kawasaki; Shotaro Akaho; Noboru Murata (BIBM, 2018)

Integral representation of the global minimizer. - Sho Sonoda; Isao Ishikawa; Masahiro Ikeda; Kei Hagihara; Yoshihiro Sawano; Takuo Matsubara; Noboru Murata (CoRR, 2018)

EEG dipole source localization with information criteria for multiple particle filters. - Sho Sonoda; Keita Nakamura; Yuki Kaneda; Hideitsu Hino; Shotaro Akaho; Noboru Murata; Eri Miyauchi; Masahiro Kawasaki (Neural Networks, 2018)

Numerical Integration Method for Training Neural Network. - Sho Sonoda (CoRR, 2019)

Transport Analysis of Infinitely Deep Neural Network. - Sho Sonoda; Noboru Murata (J. Mach. Learn. Res., 2019)

Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions. - Hayata Yamasaki; Sathyawageeswar Subramanian; Sho Sonoda; Masato Koashi (NeurIPS, 2020)

Fast Quantum Algorithm for Learning with Optimized Random Features. - Hayata Yamasaki; Sathyawageeswar Subramanian; Sho Sonoda; Masato Koashi (CoRR, 2020)

Gradient Descent Converges to Ridgelet Spectrum. - Sho Sonoda; Isao Ishikawa; Masahiro Ikeda (CoRR, 2020)

On the Approximation Lower Bound for Neural Nets with Random Weights. - Sho Sonoda; Ming Li 065; Feilong Cao; Changqin Huang; Yu Guang Wang 001 (CoRR, 2020)

Ridge Regression with Over-parametrized Two-Layer Networks Converge to Ridgelet Spectrum. - Sho Sonoda; Isao Ishikawa; Masahiro Ikeda (AISTATS, 2021)

Differentiable Multiple Shooting Layers. - Stefano Massaroli; Michael Poli; Sho Sonoda; Taiji Suzuki; Jinkyoo Park; Atsushi Yamashita; Hajime Asama (NeurIPS, 2021)

Ghosts in Neural Networks: Existence, Structure and Role of Infinite-Dimensional Null Space. - Sho Sonoda; Isao Ishikawa; Masahiro Ikeda (CoRR, 2021)

Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning. - Hayata Yamasaki; Sho Sonoda (CoRR, 2021)

Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis. - Sho Sonoda; Isao Ishikawa; Masahiro Ikeda (ICML, 2022)

Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups. - Sho Sonoda; Isao Ishikawa; Masahiro Ikeda (NeurIPS, 2022)

How Powerful are Shallow Neural Networks with Bandlimited Random Weights? - Ming Li 065; Sho Sonoda; Feilong Cao; Yu Guang Wang 001; Jiye Liang (ICML, 2023)

Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation. - Hayata Yamasaki; Sathyawageeswar Subramanian; Satoshi Hayakawa; Sho Sonoda (ICML, 2023)

Joint Group Invariant Functions on Data-Parameter Domain Induce Universal Neural Networks. - Sho Sonoda; Hideyuki Ishi; Isao Ishikawa; Masahiro Ikeda (NeurReps, 2023)

Koopman-Based Bound for Generalization: New Aspect of Neural Networks Regarding Nonlinear Noise Filtering. - Yuka Hashimoto; Sho Sonoda; Isao Ishikawa; Atsushi Nitanda; Taiji Suzuki (CoRR, 2023)

LPML: LLM-Prompting Markup Language for Mathematical Reasoning. - Ryutaro Yamauchi; Sho Sonoda; Akiyoshi Sannai; Wataru Kumagai (CoRR, 2023)

Deep Ridgelet Transform: Voice with Koopman Operator Proves Universality of Formal Deep Networks. - Sho Sonoda; Yuka Hashimoto; Isao Ishikawa; Masahiro Ikeda (CoRR, 2023)

Deep learning in random neural fields: Numerical experiments via neural tangent kernel. - Kaito Watanabe; Kotaro Sakamoto; Ryo Karakida; Sho Sonoda; Shun-ichi Amari (Neural Networks, 2023)

Koopman-based generalization bound: New aspect for full-rank weights. - Yuka Hashimoto; Sho Sonoda; Isao Ishikawa; Atsushi Nitanda; Taiji Suzuki (ICLR, 2024)

A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees. - Toshinori Kitamura; Tadashi Kozuno; Masahiro Kato; Yuki Ichihara; Soichiro Nishimori; Akiyoshi Sannai; Sho Sonoda; Wataru Kumagai; Yutaka Matsuo (CoRR, 2024)

A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks. - Sho Sonoda; Isao Ishikawa; Masahiro Ikeda (CoRR, 2024)

Constructive Universal Approximation Theorems for Deep Joint-Equivariant Networks by Schur's Lemma. - Sho Sonoda; Yuka Hashimoto; Isao Ishikawa; Masahiro Ikeda (CoRR, 2024)

Lean Formalization of Generalization Error Bound by Rademacher Complexity. - Sho Sonoda; Kazumi Kasaura; Yuma Mizuno; Kei Tsukamoto; Naoto Onda (CoRR, 2025)

Generalization Through Growth: Hidden Dynamics Controls Depth Dependence. - Sho Sonoda; Yuka Hashimoto; Isao Ishikawa; Masahiro Ikeda (CoRR, 2025)

LeanConjecturer: Automatic Generation of Mathematical Conjectures for Theorem Proving. - Naoto Onda; Kazumi Kasaura; Yuta Oriike; Masaya Taniguchi; Akiyoshi Sannai; Sho Sonoda (CoRR, 2025)