Sho Sonoda

Overview:

Approximation Theory for Deep Neural Networks (MLSS2024 long version): slide v. Mar 2024 (english) 

AIP Open Seminar recordings, Apr2021 (YouTube video, english)

積分表現でニューラルネットを理解する（OCAMI「人工知能と数学」）YouTube動画, slide v2024年3月（日本語）

Specifics:

General Ridgelet Transform Induced from Group Invariant Functions, and Deep Ridgelet Transform: slide v. Dec2023 (english).

Ridgelet Transform for Group Convolutional Neural Networks: slide v. Nov2022 (english) v. 2022年9月（日本語）, poster

Ridgelet Transform for Neural Networks on Noncompact Symmetric Space and Helgason-Fourier Analysis: slide v. Aug2022 (english), poster

Ghosts (or Null Space) in Neural Networks: slide v. June2021 (english)

Approximation Lower Bounds for Neural Networks: slide v. Sep2020 (english) poster v. Jul2023 (english)

Transport analysis for denoising autoencoders: slide v. Aug2019 (english)

Quantum algorithm for optimal random Fourier features: slide v. Nov2021 (english)

積分表現理論と輸送理論（ICML2019読み会資料） : slide v2019年8月 (日本語)

ニューラルネットの零空間（勉強会資料）：slide v2021年10月（日本語）

Research Interest

• Theory of Neural networks

  • Integral representation of neural networks, Ridgelet analysis

  • Transportation analysis of deep neural networks, Wasserstein geometry

  • Synthesizing method for neural networks, Probabilistic numerics

• Machine learning, Data science

  • Non-parametric statistics, kernel methods

  • Particle filtering, EEG signal processing

• Quantum Machine Learning (QML)

• Automated Theorem Proving (ATP)

• Autonomous driving

Contact

sho.sonoda [at] riken.jp

sho.sonoda [at] aoni.waseda.jp (expired at the end of April 2018)

s.sonoda0110 [at] toki.waseda.jp (permanently valid alumni address)