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Sho Sonoda

Observed name: Sonoda, Sho
Observed institutions: Waseda University, CyberAgent (Japan), RIKEN, RIKEN Center for Advanced Intelligence Project, Ehime University, The University of Tokyo
ORCID: 0000-0001-7242-4740
Observed ORCID: 0000-0001-7242-4740
OpenAlex ID: A5036537988
Neural network with unbounded activation functions is universal approximator
2015 · Sho Sonoda, Noboru Murata · Applied and Computational Harmonic Analysis · 333
LPML: LLM-Prompting Markup Language for Mathematical Reasoning
2023 · Yamauchi, Ryutaro, Sonoda, Sho, et al. · arXiv (Cornell University) · 23
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A Statistical Model for Predicting the Liquid Steel Temperature in Ladle and Tundish by Bootstrap Filter
2012 · Sho Sonoda, Noboru Murata, et al. · ISIJ International · 20
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LeanConjecturer: Automatic Generation of Mathematical Conjectures for Theorem Proving
2025 · Onda, Naoto, Kasaura, Kazumi, et al. · arXiv (Cornell University) · 13
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Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions
2020 · Yamasaki, Hayata, Subramanian, Sathyawageeswar, et al. · arXiv (Cornell University) · 13
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Sampling Hidden Parameters from Oracle Distribution
2014 · Sho Sonoda, Noboru Murata · Lecture notes in computer science · 11
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
2020 · Li, Ming, Sonoda, Sho, et al. · arXiv (Cornell University) · 10
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Lean Formalization of Generalization Error Bound by Rademacher Complexity and Dudley's Entropy Integral
2025 · Sonoda, Sho, Kasaura, Kazumi, et al. · arXiv (Cornell University) · 9
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Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning
2021 · Yamasaki, Hayata, Sonoda, Sho · arXiv (Cornell University) · 9
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The global optimum of shallow neural network is attained by ridgelet transform
2018 · Sonoda, Sho, Ishikawa, Isao, et al. · arXiv (Cornell University) · 8
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Nonparametric Weight Initialization of Neural Networks via Integral Representation
2013 · Sonoda, Sho, Murata, Noboru · arXiv (Cornell University) · 8
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A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees
2024 · Kitamura, Toshinori, Kozuno, Tadashi, et al. · arXiv (Cornell University) · 7
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Fully-Connected Network on Noncompact Symmetric Space and Ridgelet Transform based on Helgason-Fourier Analysis
2022 · Sonoda, Sho, Ishikawa, Isao, et al. · arXiv (Cornell University) · 7
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Lean-Quantum: Toward AI-Assisted Formalization of Quantum Information
2026 · Kasaura, Kazumi, Tsukamoto, Kei, et al. · arXiv (Cornell University) · 6
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Discovering New Theorems via LLMs with In-Context Proof Learning in Lean
2025 · Kasaura, Kazumi, Onda, Naoto, et al. · arXiv (Cornell University) · 5
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A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks
2024 · Sho Sonoda, Isao Ishikawa, et al. · Journal of Statistical Planning and Inference · 5
Ghosts in Neural Networks: Existence, Structure and Role of Infinite-Dimensional Null Space
2021 · Sonoda, Sho, Ishikawa, Isao, et al. · arXiv (Cornell University) · 5
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Differentiable Multiple Shooting Layers
2021 · Massaroli, Stefano, Poli, Michael, et al. · arXiv (Cornell University) · 4
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Ridge Regression with Over-Parametrized Two-Layer Networks Converge to Ridgelet Spectrum
2020 · Sonoda, Sho, Ishikawa, Isao, et al. · arXiv (Cornell University) · 4
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Fast Approximation and Estimation Bounds of Kernel Quadrature for Infinitely Wide Models
2019 · Sonoda, Sho · arXiv (Cornell University) · 4
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EEG dipole source localization with information criteria for multiple particle filters
2018 · Sho Sonoda, Keita Nakamura, et al. · Neural Networks · 4
Transportation analysis of denoising autoencoders: a novel method for analyzing deep neural networks
2017 · Sonoda, Sho, Murata, Noboru · arXiv (Cornell University) · 4
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Sensitivity Analysis for Controlling Molten Steel Temperature in Tundish
2012 · Noboru Murata, Sho Sonoda, et al. · IFAC Proceedings Volumes · 4
Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs
2026 · Taniguchi, Koichi, Sonoda, Sho · arXiv (Cornell University) · 3
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Deep Ridgelet Transform: Voice with Koopman Operator Proves Universality of Formal Deep Networks
2023 · Sonoda, Sho, Hashimoto, Yuka, et al. · arXiv (Cornell University) · 3
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Works count: 55
Citation count: 540
H-index: 9
I10-index: 7
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